The same geometric quirk that lets visitors murmur messages around the circular dome of the whispering gallery at St. Paul’s Cathedral in London or across St. Louis Union Station’s whispering arch also enables the construction of high-resolution optical sensors. Whispering-gallery-mode (WGM) resonators have been used for decades to detect chemical signatures, DNA strands and even single molecules.In the same way that the architecture of a whispering gallery bends and focuses sound waves, WGM microresonators confine and concentrate light in a tiny circular path. This enables WGM resonators to detect and quantify physical and biochemical characteristics, making them ideal for high-resolution sensing applications in fields such as biomedical diagnostics and environmental monitoring. However, the broad use of WGM resonators has been limited by their narrow dynamic range as well as their limited resolution and accuracy.&nbsp;In a recent study,&nbsp;<a href="https://engineering.wustl.edu/faculty/Lan-Yang.html" target="_blank">Lan Yang</a>, the Edwin H. &amp; Florence G. Skinner Professor, and Jie Liao, a postdoctoral research associate, both in the Preston M. Green Department of Electrical &amp; Systems Engineering in the McKelvey School of Engineering at Washington University in St. Louis, demonstrate a transformative approach to overcome these limitations:&nbsp;<a href="https://ieeexplore.ieee.org/abstract/document/10407034" target="_blank">optical WGM barcodes for multimode sensing</a>. Liao and Yang’s innovative technique allows simultaneous monitoring of multiple resonant modes within a single WGM resonator, considering distinctive responses from each mode, vastly expanding the range of measurements achievable.WGM sensing uses a specific wavelelength of light that can circulate around the perimeter of the microresonator millions of times. When the sensor encounters a molecule, the resonant frequency of the circulating light shifts. Researchers can then measure that shift to detect and identify the presence of specific molecules.“Multimode sensing allows us to pick up multiple resonance changes in wavelength, rather than just one,” Liao explained. “With multiple modes, we can expand optical WGM sensing to a greater range of wavelengths, achieve greater resolution and accuracy, and ultimately sense more particles.”Liao and Yang found the theoretical limit of WGM detection and used it to estimate the sensing capabilities of a multimode system. They compared conventional single-mode with multimode sensing and determined that while single-mode sensing is limited to very narrow range – about 20 picometers (pm), constrained by the laser hardware – the range for multimode sensing is potentially limitless using the same setup.“More resonance means more information,” Liao said. “We derived a theoretically infinite range, though we’re practically limited by the sensing apparatus. In this study, the experimental limit we found was about 350 times larger with the new method than the conventional method for WGM sensing.”Commercial applications of multimode WGM sensing could include biomedical, chemical and environmental uses, Yang said. In biomedical applications, for instance, researchers could detect subtle changes in molecular interactions with unprecedented sensitivity to improve disease diagnosis and drug discovery. In environmental monitoring, with the capability to detect minute changes in environmental parameters such as temperature and pressure, multimode sensing could enable early warning systems for natural disasters or facilitate monitoring pollution levels in air and water.This new technology also enables continuous monitoring of chemical reactions, as demonstrated in the recent experiments conducted by Yang’s group. This capability holds promise for real-time analysis and control of chemical processes, offering potential applications in fields such as pharmaceuticals, materials science, and the food industry.&nbsp;“WGM resonators’ ultrahigh sensitivity lets us detect single particles and ions, but the potential of this powerful technology has not been fully utilized because we can’t use this ultrasensitive sensor directly to measure a complete unknown,” Liao added. “Multimode sensing enables that look into the unknown. By expanding our dynamic range to look at millions of particles, we can take on more ambitious projects and solve real-world problems.”<hr>Liao J and Yang L. Multimode sensing by optical whispering-gallery-mode barcodes: A new route to expand dynamic range for high-resolution measurement.&nbsp;IEEE Transactions on Instrumentation and Measurement, online January 18, 2024. DOI: 10.1109/TIM.2024.3352712This work was supported by the National Institutes of Health (R21EB030845).

An innovative optical sensing technology, developed in Yang’s lab at the McKelvey School of Engineering, utilizes multimode resonance to boost sensing capabilities. By analyzing patterns in the resonance spectrum, the innovative barcode technique provides detailed information about the sensor's surroundings, offering enhanced dynamic range and accuracy in various sensing applications. (Credit: Yang Lab)

Lan Yang and Jie Liao’s optical barcodes for multimode sensing have potential applications in biomedical diagnostics, environmental monitoring, chemical sensing and more

Barcodes expand range of high-resolution sensor

As recently as&nbsp;2010, class-leading activity trackers generally recorded three parameters - how much you moved, and approximately how much sleep you got and how many calories you burned. Movement was measured by an embedded accelerometer, alongside a 25 MHz processor, 8 kB RAM and 128 kB Flash memory. Add in a rechargeable battery and that was about all the tech you needed to power a wearable in the early 2000s.&nbsp;&nbsp;In the intervening years&nbsp;as the technology has advanced,&nbsp;fitness trackers have not only become vastly more sophisticated in terms of what&nbsp;data they are able to&nbsp;record, the&nbsp;line between fitness, health and medical monitoring has become&nbsp;increasingly blurred.&nbsp;Today’s&nbsp;high-end&nbsp;wearables can support an impressive array of sensors&nbsp;to record, for example, the wearer’s V02&nbsp;max, blood oxygen saturation (Sp02), temperature, heart rate,&nbsp;and&nbsp;heart rate variability&nbsp;(HRV), as well&nbsp;as&nbsp;the&nbsp;sleep and activity data&nbsp;their&nbsp;early predecessors pioneered.&nbsp;&nbsp; <h2>Wearables&nbsp;crossover from fitness to health</h2>The heart monitoring capability&nbsp;of today’s wrist-worn devices has&nbsp;transformed the wearable from a&nbsp;fitness tracker to a powerful tool for managing an individual’s&nbsp;cardiovascular health.&nbsp;Heart rate and&nbsp;HRV, alongside skin temperature measurements,&nbsp;have also been shown to&nbsp;be valuable in predicting and detecting&nbsp;hypoglycemia[1], enabling&nbsp;next generation wearables to also provide insight on an individual’s&nbsp;blood glucose history and associated&nbsp;diabetic&nbsp;risk.&nbsp;Further,&nbsp;data from these same sensors can provide stress and heart rate data, that&nbsp;combined with&nbsp;sleep quality information, can&nbsp;provide&nbsp;valuable insight into&nbsp;an individual’s mental&nbsp;health,&nbsp;and&nbsp;assist&nbsp;in better managing stress and&nbsp;anxiety[2].&nbsp;<h2>Next gen SoCs optimized for sensor fusion and ML</h2>Demand for ever more sophisticated wearables is placing increasing demand on the wireless SoCs that power the devices. Supporting wireless connectivity and supervising a host of sensors that can generate millions of data points is one thing. But making sense of all that data in context and quickly enough to enable rapid and effective clinical intervention demands an SoC with serious processing heft, and the ability to perform machine learning (ML) and sensor fusion at the edge.&nbsp;ML algorithms bring the ability to deal with a large volume of data and extract clinically relevant information from large datasets. This makes ML useful, for example, in healthcare wearables designed for human activity recognition (HAR). HAR enables a wearable to determine both specific ambulation activities (whether the wearer walking or jogging, for example) and functional activities (have they brushed their teeth, washed their hands, prepared food?). Assessing such activities and making inferences can help discern an individual’s health and wellness[3].&nbsp;To get that complete picture requires combining different data streams from multiple sensors, and that requires an SoC capable of sensor fusion so it can filter information and determine which data points from all the different sensors correspond to the same activity or health concern, and which do not.&nbsp;<h2>Nordic nRF54 Series&nbsp;future ready&nbsp;for advanced wearables&nbsp;</h2>Nordic Semiconductor has always taken the “if you build it the developer will find innovative ways to use it” approach to SoC development. In other words, the applications that require the processing power or memory allocation its cutting-edge technology provides may not have been dreamed up yet - but put the chips in the hands of clever engineers and they soon will be.&nbsp;This has never been more true than with the launch of the company’s revolutionary&nbsp;<a href="https://www.nordicsemi.com/Products/nRF54H20" target="_blank">nRF54H20</a>&nbsp;and&nbsp;<a href="https://www.nordicsemi.com/Products/nRF54L15" target="_blank">nRF54L15</a>&nbsp;multiprotocol SoCs. While their immediate predecessor the&nbsp;<a href="https://www.nordicsemi.com/Products/nRF5340" target="_blank">nRF5340</a>&nbsp;was the world’s first wireless SoC to feature two Arm Cortex-M33 processors, the nRF54H20 now boasts multiple Arm Cortex-M33 processors and multiple RISC-V coprocessors. The processors are clocked at up to 320 MHz and are supported by 2 MB non-volatile memory and 1 MB of RAM. This processing power and amount of memory make the SoC perfect for running ML models and sensor fusion at the edge.&nbsp;<h2>State-of-the-art&nbsp;security to protect sensitive data&nbsp;</h2>The nRF54H20 is also one of the most secure low power, multiprotocol SoCs on the market, essential for healthcare applications that demand security of potentially sensitive personal data. Its state-of-the-art security is designed for Platform Security Architecture (PSA) Certified Level 3 IoT security standard. The SoC also supports security services such as Secure Boot, Secure Firmware Update, and Secure Storage, and has cryptographic accelerators that are hardened against side-channel attacks and tamper sensors that detect an attack in progress and take appropriate action.&nbsp;&nbsp;By replacing multiple components with a highly integrated SoC, and prolonging battery life with an ultra-low power radio and minimal sleep currents, tomorrow’s healthcare wearables powered by the nRF54H20 will continue to get smaller and become more efficient and functional, and in so doing will be able to capture various health metrics that are not possible to capture today. The rest will be down to the imagination of the developers.&nbsp;&nbsp;<hr><h3 id="isPasted">References</h3>1. The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey. National Institutes of Health, 2022. &nbsp;2. A Survey on Wearable Sensors for Mental Health Monitoring. National Institutes of Health, 2023.3. Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms. B Vidya, Sasikumar P, 2022&nbsp;

Today’s high-end wearables can support an impressive array of sensors to record, for example, the wearer’s V02 max, blood oxygen saturation (Sp02), temperature, heart rate, and heart rate variability (HRV), as well as the sleep and activity data their early predecessors pioneered.

Sky's the limit for wearables thanks to next gen wireless SoCs

Low-cost, wearable sensors could increase access to care for patients with Parkinson’s disease. New machine-learning approaches and a baseline of data from healthy older adults improve the accuracy of the results from such sensors, University of Illinois Urbana-Champaign researchers and clinical collaborators found in a new study.&nbsp;“This study demonstrates that the expansion of datasets with healthy older adult motion data and integration with deep learning approaches can help improve the accuracy of detecting differences in motor impairment in persons with Parkinson's disease for use in future telemedicine sessions,” said study leader <a href="https://experts.illinois.edu/en/persons/manuel-enrique-hernandez" target="_blank">Manuel Enrique Hernandez</a>, a professor of <a href="https://medicine.illinois.edu/academics/departments/biomedical-and-translational-sciences" target="_blank">biomedical and translational sciences</a> within the <a href="https://medicine.illinois.edu/" target="_blank">Carle Illinois College of Medicine</a> at the U. of I.To have their symptoms monitored, patients with Parkinson’s disease must periodically undergo assessment — typically a time-consuming, in-person visit to a specialist with limited availability, Hernandez said.&nbsp;Evaluation by telemedicine would improve access to care for patients but is made challenging by the lack of quantifiable measurements. For example, the physician can watch the patient move, but cannot assess rigidity or muscle tone. While some progress has been made in using wearable sensors to assess motor skills, the cost limits their use.&nbsp;To address these challenges, the Illinois team focused on ways to improve assessment using low-cost wearable sensors.&nbsp;“Ideally, we’d have something that’s completely passive, gathering data as a person goes through their everyday environment and using that information to provide guidance on their overall motor function and progression of neurological symptoms. But then you face the big challenge of how to parse all that information and make it useful to a clinician,” Hernandez said. “That’s what led to our strategy of improving the machine learning, honing in on activities useful for assessment and looking at healthy individuals as a baseline.”&nbsp;The researchers adapted the gold standard of clinical assessment, the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale. The MDS-UPDRS outlines specific tasks that a patient would perform and qualitative observations a physician would make during an exam and organizes them into categories for scoring.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713912642131-225490.jpg" style="width: 100%px;" class="fr-fic fr-dib">Subjects completed tasks normally done during in-person clinical assessment with sensors on their arms, hands and feet to gather data. Image courtesy of Manuel Enrique HernandezFor their study, the researchers had patients perform tasks and muscle movements while wearing sensors to provide the data in the categories scored by MDS-UPDRS. They used data from both patients with Parkinson’s disease and from healthy older adults to train their machine learning model.&nbsp;“Improving machine learning models requires a lot of data. We hypothesized that healthy individuals can serve as the basis for what to expect with potential age-related changes. Then we could build a comparative model to get a sense of when there might be a marked change in terms of motor function or motor impairment,” said Hernandez, who also is affiliated with the departments of <a href="https://ahs.illinois.edu/kch-home" target="_blank">kinesiology and community health</a> and <a href="https://bioengineering.illinois.edu/" target="_blank">bioengineering</a>, as well as the <a href="https://beckman.illinois.edu/" target="_blank">Beckman Institute for Advanced Science and Technology</a> at Illinois.&nbsp;The researchers also used deep learning techniques known as pretraining to make the model more robust, better able to identify important data points and filter out irrelevant ones. The pretraining and the addition of healthy older adult data improved the accuracy of identifying motor impairments in hand movement tasks by more than 12% over the current standard model. Additionally, including data from healthy older adults improved the accuracy of assessing every task for the upper and lower body, except for toe tapping.&nbsp;The <a href="https://www.mdpi.com/1424-8220/23/21/9004" target="_blank">results</a> were reported in the journal Sensors.Next, the researchers hope to collaborate further with neurologists and clinicians to expand their model. They aim to identify a small number of additional tasks that could quantitatively measure more information about the classical symptoms of Parkinson’s disease, such as tremor, while maintaining low cost and ease of use for the sensors.&nbsp;“There’s a very large need for us to better understand and better quantify changes that are ongoing with Parkinson’s disease,” Hernandez said. “The ability to use wearable sensors while performing activities that are part of the course of clinical evaluations in a telehealth setting might open up the doors for more objective, more quantifiable information that can help direct treatment for individuals with Parkinson’s disease and hopefully improve quality of life moving forward.”<a href="https://ise.illinois.edu/directory/profile/r-sowers" target="_blank">Richard Sowers</a>, Illinois professor of <a href="https://ise.illinois.edu/" target="_blank">industrial and enterprise systems engineering</a> and <a href="https://math.illinois.edu/" target="_blank">mathematics</a>, and James R. Brasic, professor and neurologist at Johns Hopkins School of Medicine, New York University and NYC Health + Hospitals, were coauthors of the paper. &nbsp;&nbsp;<hr>Editor’s note: &nbsp;&nbsp;To reach Manuel Enrique Hernandez, email <a href="mailto:mhernand@illinois.edu" target="_blank">mhernand@illinois.edu</a>.&nbsp;The paper, “A deep learning approach for automatic and objective grading of the motor impairment severity in Parkinson’s disease for use in tele-assessments,” is available <a href="https://www.mdpi.com/1424-8220/23/21/9004" target="_blank">online</a>. DOI: 10.3390/s23219004This research had no external funding.&nbsp;

Illinois professors Richard Sowers, left, and Manuel Hernandez used machine learning techniques to improve data analysis from wearable sensors to detect symptoms and monitor the progression of Parkinson’s disease. Photo by Fred Zwicky

Low-cost, wearable sensors could increase access to care for patients with Parkinson’s disease. New machine-learning approaches and a baseline of data from healthy older adults improve the accuracy of the results from such sensors

Wearable sensors for Parkinson's can improve with machine learning, data from healthy adults

Optical sensors serve as the backbone of numerous scientific and technological endeavors, from detecting gravitational waves to imaging biological tissues for medical diagnostics. These sensors use light to detect changes in properties of the environment they’re monitoring, including chemical biomarkers and physical properties like temperature. A persistent challenge in optical sensing has been enhancing sensitivity to detect faint signals amid noise.New research from Lan Yang, the Edwin H. &amp; Florence G. Skinner Professor in the Preston M. Green Department of Electrical &amp; Systems Engineering in the McKelvey School of Engineering at Washington University in St. Louis, unlocks the power of exceptional points (EPs) for advanced optical sensing. In a study published April 5 in&nbsp;Science Advances, Yang and first author Wenbo Mao, a doctoral student in Yang’s lab, showed that these unique EPs – specific conditions in systems where extraordinary optical phenomena can occur – can be deployed on conventional sensors to achieve a striking sensitivity to environmental perturbations.Yang and Mao developed an EP-enhanced sensing platform that overcomes the limitations of previous approaches. Unlike traditional methods that require modifications to the sensor itself, their innovative system features an EP control unit that can plug into physically separated external sensors. This configuration allows EPs to be tuned solely through adjustments to the control unit, allowing for ultrahigh sensitivity without the need for complex modifications to the sensor.“We’ve implemented a novel platform that can impart EP enhancement to conventional optical sensors,” Yang said. “This system represents a revolutionary extension of EP-enhanced sensing, significantly expanding its applicability and universality. Any phase-sensitive sensor can acquire improved sensitivity and reduced detection limit by connecting to this configuration. Simply by tuning the control unit, this EP configuration can adapt to various sensing scenarios, such as environmental detection, health monitoring and biomedical imaging.”By decoupling the sensing and control functions, Yang and Mao have effectively skirted the stringent physical requirements for operating sensors at EPs that have so far hindered their widespread adoption. This clears the way for EP enhancement to be applied to a wide range of conventional sensors – including ring resonators, thermal and magnetic sensors, and sensors that pick up vibrations or detect perturbations in biomarkers – vastly improving the detection limit of sensors scientists are already using. With the control unit set to an EP, the sensor can operate differently – not at an EP – and still reap the benefits of EP enhancement.As a proof-of-concept, Yang’s team tested a system’s detection limit, or ability to detect weak perturbations over system noise. They demonstrated a six-fold reduction in the detection limit of a sensor using their EP-enhanced configuration compared to the conventional sensor.&nbsp;“With this work, we’ve shown that we can significantly enhance our ability to detect perturbations that have weak signals,” Mao said. “We’re now focused on bringing that theory to broad applications. I’m specifically focused on medical applications, especially working to enhance magnetic sensing, which could be used to improve MRI technology. Currently, MRIs require a whole room with careful temperature control. Our EP platform could be used to enhance magnetic sensing to enable portable, bedside MRI.”&nbsp;<hr>Mao W, Fu Z, Li Y, Li F, and Yang L. Exceptional-point-enhanced phase sensing.&nbsp;Science Advances, April 5, 2024. DOI:&nbsp;<a href="https://doi.org/10.1126/sciadv.adl5037" target="_blank">https://doi.org/10.1126/sciadv.adl5037</a>This project is supported in part by the Chan Zuckerberg Initiative (CZI). The authors acknowledge the Institute of Materials Science &amp; Engineering (IMSE) at Washington University in St. Louis for the use of instruments, financial support and staff assistance.

Optical sensors have been widely used in various applications, from structural health monitoring to medical diagnosis and imaging. An innovative platform, featuring a control unit operated at an exceptional point (EP), has been developed to enhance the performance of conventional optical sensors. (Credit: Yang Lab)

Lan Yang and her team have developed new plug-and-play hardware to dramatically enhance the sensitivity of optical sensors

Innovative sensing platform unlocks ultrahigh sensitivity in conventional sensors

In this episode, we discuss a breakthrough from ETH Zurich to mimic natural foot signals to the brain using state of the art neuroprosthetics. 

Podcast: Neuroprosthetics: The Next Step Towards Limb Reconstruction

The digital revolution has undeniably transformed how we access information and complete tasks, with laptops, tablets, and smartphones becoming ubiquitous companions in both our personal and professional lives. While these devices offer undeniable benefits, their constant use can also pose new challenges to our well-being, raising concerns about potential impacts on visual health and musculoskeletal function.One emerging area of concern is the intersection of presbyopia, a natural age-related decline in near vision, and the use of progressive lenses. These lenses, often referred to as 'no-line bifocals', &nbsp;provide wearers with a seamless transition between near and far vision, addressing the challenges of presbyopia. However, research suggests that the way we use progressive lenses, with their specific viewing zones, may subtly alter our head and neck postures, potentially leading to discomfort or even musculoskeletal disorders.To shed light on this issue, FREMAP, a leading Spanish mutual benefit association, embarked on a <a href="https://practicapreventiva.fremap.es/2023/02/09/uso-de-dispositivos-digitales-con-gafas-progresivas/" target="_blank" rel="noopener noreferrer">research</a> study to objectively assess the impact of progressive lenses on workplace ergonomics. Their primary objective was to quantify the visual and postural changes associated with using these lenses in a typical workstation environment. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwODM2NzM4NDQxLWVtaWFwLTEuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Worker wearing progressive lenses with the Xsens motion capture sensors to analyze postural changesTo achieve this objective, the research team, led by National Ergonomics Consultant at FREMAP Prevention, D. Antonio Emir Díaz Martínez, employed <a href="https://www.movella.com/products/motion-capture" target="_blank" rel="noopener noreferrer">Xsens Motion Capture technolog</a>y, specifically an upper body configuration with 11 strategically placed inertial sensors. This system was integrated with FREMAP's own ergonomics recording, analysis and representation software, utilizing <a href="https://www.movella.com/products/motion-capture/mvn-analyze" target="_blank" rel="noopener noreferrer">Xsens Analyze Pro</a> for data processing. This setup allowed for the accurate capture and analysis of human movement and posture data.Antonio Emir Díaz Martínez, commented: "The use of this system allows us to quantify, concisely, precisely, and without interference with normal working conditions, the postural requirements of the cervical spine in workplaces with screens for data visualization. This is thanks to having a software solution that removes the possible interferences caused by local magnetic fields."By leveraging Xsens Motion Capture, the FREMAP researchers were able to quantify the precise head and neck movements associated with using progressive lenses while interacting with a computer screen.The research involved monitoring the grazing and cervical spine movements of a worker wearing progressive glasses while they interacted with a computer workstation. To ensure standardized conditions, the upper part of the screen was positioned at eye level, maintaining a distance of 60 cm from the worker. Additionally, the worker's posture was controlled by having them maintain their back against the chair's backrest and their elbows bent at an angle of approximately 80 degrees.Research Findings and RecommendationsThe analysis revealed that participants maintained consistent neck extension throughout the study, exceeding recommended ergonomic limits. This posture is likely linked to the need to look through the lower portion of progressive lenses for optimal near vision. While neck turning remained within acceptable limits, occasional lateral tilts suggest further ergonomic adjustments may be necessary.Based on the findings, the research makes the following recommendations for progressive lens users.<ul><li>Screen Positioning:&nbsp;Ensure the screen is positioned correctly for optimal viewing. Adjust the viewing distance based on task and screen size (400-750 mm for common office monitors). Consider increasing font size for individuals with progressive lenses to increase viewing distance and minimize reliance on the lower lens segment.</li><li>Screen Height: For progressive lens users, reduce the screen height significantly compared to the standard recommendation of aligning the top of the screen with eye level. This helps avoid neck extension for viewing through the lower lens zone.</li><li>Document Holders: Utilize adjustable lecterns or document holders to facilitate switching between digital and printed materials, minimizing strain on eye accommodation.</li><li>Recovery Breaks:&nbsp;Encourage frequent breaks to rest the eyes and change posture, especially for those wearing progressive lenses.</li><li>Occupational Spectacles: For individuals with presbyopia and prolonged digital device use, consider occupational spectacles specifically designed for computer work.</li><li>Training and Awareness:&nbsp;Educate first-time progressive lens users about adaptation exercises and the importance of adjusting their workstation layout. Encourage them to adopt healthy postural habits and avoid extended periods of static posture at their workstation.</li></ul>By implementing these recommendations, individuals who rely on progressive lenses can create a more ergonomically friendly workspace and potentially mitigate the risks associated with prolonged computer use.This research project by FREMAP provides valuable insights into the potential impact of progressive lenses on workplace ergonomics. By utilizing advanced motion capture technology like Xsens, the study offers a quantifiable and objective perspective on this complex issue. The findings can help inform future research, guide the development of ergonomic interventions, and ultimately contribute to promoting healthy workplace practices for individuals reliant on progressive lenses.Xsens motion capture for ergonomics assessments<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwODM3MDc2NDU2LWVtaWFwLTIuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Accurate motion data: Xsens Awinda Motion Capture Technology featuring 18 inertial sensorsEnsuring proper workplace ergonomics is crucial for employee well-being, regulatory compliance, and increased productivity. This focus stems from the significant prevalence of musculoskeletal disorders (MSDs), often linked to poor ergonomics, affecting nearly 1.71 billion people globally.However, traditional assessment methods can be time-consuming and rely on subjective observations, potentially hindering accurate risk identification. Xsens Motion capture technology offers a compelling solution. Xsens captures objective and validated data on worker movements, eliminating subjectivity and increasing data collection efficiency. This empowers companies to swiftly identify potential hazards and make informed decisions regarding workplace design and modifications. Ultimately, this fosters safer and more productive workspaces, leading to a thriving and healthy working environment for all.<a href="https://www.movella.com/applications/health-sports/workplace-ergonomics" target="_blank" rel="noopener noreferrer">Learn more</a> about Xsens solutions for workplace ergonomics.&nbsp;

FREMAP uses Xsens motion capture to analyze the ergonomic impact of progressive lenses in the workplace and makes recommendations for improvement

Ergonomic impact of progressive lenses in the workplace

Small wearable or implantable electronics could help monitor our health, diagnose diseases, and provide opportunities for improved, autonomous treatments. But to do this without aggravating or damaging the cells around them, these electronics will need to not only bend and stretch with our tissues as they move, but also be soft enough that they will not scratch and damage tissues.Researchers at Stanford have been working on skin-like, stretchable electronic devices for over a decade. In a paper published March 13 in Nature, they present a new design and fabrication process for skin-like integrated circuits that are five times smaller and operate at one thousand times higher speeds than earlier versions. The researchers demonstrated that their soft integrated circuits are now able to drive a micro-LED screen and detect a braille array that is more sensitive than human fingertips.“We’ve made a significant leap forward. For the first time, stretchable integrated circuits are now small enough and fast enough for many applications,” said <a href="https://mandrillapp.com/track/click/30855492/profiles.stanford.edu?p=eyJzIjoiOVAtVVZYVEltMTAyQXVpZ2ppSmFNd3RkQjNvIiwidiI6MSwicCI6IntcInVcIjozMDg1NTQ5MixcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL3Byb2ZpbGVzLnN0YW5mb3JkLmVkdVxcXC96aGVuYW4tYmFvXCIsXCJpZFwiOlwiMGM2YWMxYWU5MTg4NGZjYWFjMzI1ZDc2M2MyZDhhODdcIixcInVybF9pZHNcIjpbXCJkMGY0ZGMxYjBmMDE3OTkzNjhmOGE4NzJlMmViNzQ3ZTFjZmNmMjBkXCJdfSJ9" data-extlink="" target="_blank">Zhenan Bao</a>, a K. K. Lee Professor in Chemical Engineering at Stanford and senior author on the paper. “We hope that this can make wearable sensors and implantable neural and gut probes more sensitive, operate more sensors, and potentially consume less power.”<h2>Flexible, stretchable, and functional</h2>The core of the circuits are stretchable transistors made from semiconducting carbon nanotubes and soft elastic electronic materials developed in Bao’s lab. Unlike silicon, which is hard and brittle, the carbon nanotubes sandwiched between elastic materials have a fishnet-like structure that allows them to continue to function while they stretch and deform. The transistors and circuits are patterned onto a stretchable substrate, along with stretchable semiconductor, conductor, and dielectric material.“This is many years of material and engineering development,” Bao said. “We not only needed to develop new materials, but we also needed to develop the circuit design and the fabrication process to make the circuits. There are many layers stacked together and if one layer doesn’t work, we have to restart everything from scratch.”In one demonstration of their new stretchable electronics design, the researchers were able to fit more than 2,500 sensors and transistors into a square centimeter, creating an active-matrix tactile array over ten times more sensitive than human fingertips. The researchers showed that the sensor array can detect the locations and orientation of tiny shapes or recognize whole words in Braille.“With Braille, you usually sense one letter at a time,” said <a href="https://mandrillapp.com/track/click/30855492/baogroup.stanford.edu?p=eyJzIjoiQWFWMU9jM2VqYXZMR2g5TFVfMnRPcTE4cGxnIiwidiI6MSwicCI6IntcInVcIjozMDg1NTQ5MixcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL2Jhb2dyb3VwLnN0YW5mb3JkLmVkdVxcXC9wZW9wbGVcXFwvZG9uZ2xhaS16aG9uZ1wiLFwiaWRcIjpcIjBjNmFjMWFlOTE4ODRmY2FhYzMyNWQ3NjNjMmQ4YTg3XCIsXCJ1cmxfaWRzXCI6W1wiNTgyODY0ZTdiMWU3NWU4ZGRiOWM5OWIyOTI5YWU1YTFiODU3MDZiYVwiXX0ifQ" data-extlink="" target="_blank">Donglai Zhong</a>, a postdoctoral researcher in Bao’s lab and co-first author on the paper. “With such a high resolution, you could sense a whole word, or potentially a whole sentence, with just one touch.”The researchers also used their stretchable circuits to drive a micro-LED display with a refresh rate of 60 Hz, which is the typical refresh rate of a computer or TV screen. Previous versions of the stretchable circuits weren’t fast enough at small sizes to generate enough current to accomplish this.“We’re really excited about these performance improvements because they enable us to do a lot of new things,” said <a href="https://mandrillapp.com/track/click/30855492/baogroup.stanford.edu?p=eyJzIjoia1V0NTdYUFNUVGxncTZEZFg0RWh1MGRnRDg0IiwidiI6MSwicCI6IntcInVcIjozMDg1NTQ5MixcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL2Jhb2dyb3VwLnN0YW5mb3JkLmVkdVxcXC9wZW9wbGVcXFwvY2FuLXd1XCIsXCJpZFwiOlwiMGM2YWMxYWU5MTg4NGZjYWFjMzI1ZDc2M2MyZDhhODdcIixcInVybF9pZHNcIjpbXCI4MTliZDk3YzY5NzMxMjg0ZjcwMDgwNTY4MDdhZjFjOGNlYmE0ZTExXCJdfSJ9" data-extlink="" target="_blank">Can Wu</a>, a postdoctoral researcher in Bao’s lab and co-first author on the paper. “Preliminary results demonstrate that our transistor can be used to drive commercial displays commonly used in computer monitors, for example. And for biomedical applications, a high-density, soft, and conformable sensing array could allow us to sense human body signals, such as from our brains and muscles, at a large scale and fine resolution. This could lead to next-generation brain-machine interfaces that are both high-performance and biocompatible.”<h2>Soft circuits for the future</h2>The researchers intentionally developed materials and processes that could work with existing fabrication tools, so that the circuits could make the jump to commercial manufacturing more easily. Their process relies on fabrication techniques similar to what is currently used for making display screens, although the materials involved are entirely different. Manufacturers wouldn’t be able to make these circuits without some additional fine-tuning, but the tools are already in place, Bao said.Of course, there are more hurdles before these stretchable and soft integrated circuits can be ready for commercialization. Body and tissue movement can still cause some variation in the electrical characteristics of the circuits – Bao and her colleagues are working on new designs that could reduce these effects – and the devices will need some sort of soft moisture protection before they can be put to use.“There are still challenges for the future of this technology, but these recent developments open up some very exciting biomedical applications for wearable and implantable electronics,” Bao said. “And it also has applications in soft robotics, giving robots a sensing functionality that approaches that of humans and makes them safer for people to work with.”Bao is a member of&nbsp;<a href="https://biox.stanford.edu/" data-extlink="" target="_blank">Stanford Bio-X</a>, the&nbsp;<a href="https://med.stanford.edu/cvi.html" data-extlink="" target="_blank">Stanford Cardiovascular Institute</a>, the&nbsp;<a href="https://humanperformance.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Human Performance Alliance</a>, the&nbsp;<a href="https://neuroscience.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Neurosciences Institute</a>, and the&nbsp;<a href="https://med.stanford.edu/mchri.html" data-extlink="" target="_blank">Maternal &amp; Child Health Research Institute</a>; an affiliate of the&nbsp;<a href="https://energy.stanford.edu/" data-extlink="" target="_blank">Precourt Institute for Energy</a>&nbsp;and the&nbsp;<a href="https://woods.stanford.edu/" data-extlink="" target="_blank">Stanford Woods Institute for the Environment</a>; and a faculty fellow of&nbsp;<a href="https://chemh.stanford.edu/" data-extlink="" target="_blank">Sarafan ChEM-H</a>. Bao is an investigator of CZ-Biohub-San Francisco and an innovation investigator of Arc Institute.Other Stanford co-authors of this research include research scientist Jeffrey B.-H. Tok; postdoctoral researchers Yuanwen Jiang (Co-first-author), Min-gu Kim, Chien-Chung Shih, Jian-Cheng La, Chuanzhen Zhao, Shiyuan Wei, Xiaozhou Ji, Yi-Xuan Wang, Chengyi Xu, Naoji Matsuhisa, Yusheng Lei, Deya Liu, Song Zhang, and Yuto Ochiai; and doctoral students Yujia Yuan, Yuya Nishio, Weichen Wang, Theodore Z. Gao, Yu Zheng, Zhiao Yu, Huaxin Gong, and Shuhan Liu.

Intrinsically stretchable transistors and integrated circuits under large deformation after being released from the supporting substrate. | Image courtesy of Donglai Zhong, Jiancheng Lai, and Yuya Nishio of the Bao Group

Stanford researchers have developed soft integrated circuits that are powerful enough to drive a micro-LED screen and small enough to read thousands of sensors in a single square centimeter.

Smaller, more powerful stretchable electronics for wearables and implantables

Testa-Seat has been recognized for its use of FGF 3D printing to create custom seating solutions for children with disabilities. This product is the winner of the 2024 &nbsp;3D Printing FGF Engineering Challenge, an engineering challenge aimed at identifying and nurturing innovative applications of fused granule fabrication technology. Testa-Seat will receive $25,000 worth of support from Mitsubishi Chemical Group to improve the 3D printing material, design, and manufacturing process for their business.<h2 dir="ltr">Background and Objective</h2>Adaptive seating for children with disabilities presents a complex challenge, requiring solutions that can adapt to diverse needs while being economically viable. Traditional methods often fall short, being either too expensive or not sufficiently customizable. Testa-Seat, developed by Alexander Geht and his team, introduces a new approach to this problem, leveraging 3D printing to offer affordable tailored support systems.Traditional seating is often not suitable for children with disabilities who require more support or differently sized seating options. Appropriate seating enables these children to increase their access to education, the labour market, and other activities of daily life.<h2 dir="ltr">Testa-Seat’s Approach</h2>The core innovation lies in Testa-Seat’s utilisation of 3D printing technology, specifically Fused Granule Fabrication (FGF), to manufacture seating solutions that are both customizable and cost-effective.This process begins with the precise measurements of the user, which are then input into a design system to create a digital model of the seat. The 3D printer uses ABS pellets, a type of plastic known for its strength and flexibility, to build the seat layer by layer. This method allows for the production of seats that are perfectly contoured to the individual's body, ensuring maximum support and comfort without the need for traditional and often imprecise casting or moulding techniques.&nbsp; The use of FGF not only streamlines the manufacturing process but also significantly reduces waste, as materials are only used where necessary, contributing to the environmental sustainability of the project.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwNTA4NjAwNjg0LUlNRy01MTQ2LmpwZWciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Image credit: Testa-Seat<h3 dir="ltr">Technical and Manufacturing Readiness</h3>Testa-Seat has reached a significant level of technical readiness, having been tested and optimised through the delivery of over 120 seating systems to rehabilitation centres, hospitals, and private customers. The manufacturing process, currently capable of producing around 300 seats per year, is set for expansion in the near future.<h3 dir="ltr">Team and Expertise</h3>The project is driven by a diverse team of experts, including founder and CEO Alexander Geht, an expert in additive manufacturing, and a range of advisors and board members with backgrounds in entrepreneurship, law, physical therapy, industrial design, and assistive technology.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwNTA4NjU5ODA0LUlNRy01ODIwLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Image credit: Testa-Seat<h3 dir="ltr">Conclusion</h3>Testa-Seat's victory in the engineering challenge is a step forward in making everyday life more accessible for children with disabilities. As Testa-Seat continues to develop and refine its products, it sets a precedent for how innovation and technology can work hand in hand to address complex challenges in healthcare and accessibility.<h3 dir="ltr">Special Mention</h3>The competition saw numerous compelling entries, and besides the winner, two submissions particularly distinguished themselves.From Poland, the startup HELIX, initiated by Filip Turzynski, is pioneering a compact, cost-effective, durable, and versatile FGF extrusion system adaptable to a wide range of both desktop and industrial 3D printers. This innovation transforms pellet-based 3D printing by allowing for the creation of smaller components with a broader array of printing materials.In the United States, Bala Cherukuri and his team at Center Street Technologies have introduced a breakthrough in large format additive manufacturing using water-soluble polymer pellets. Their innovation is centred on the production of cores and mandrels for composite parts, especially useful in the large-scale production of airframes.Interested in learning more about engineering challenges? Sign up on the Wevolver newsletter to get the latest updates.&nbsp;<iframe src="https://c8056756.sibforms.com/serve/MUIFALo-2r_R7W9fdBHNYADE_tozH2lGA3267iamBDH7KM7kSRNIXJhxk3rIigppuA71AKPAUKQ2FpoQH5LGwdznoCnlUnfYaX90-GOcvLV3_hT-Q-m2jB4wucnawOohsedgPtjhnzcTcN66N8L37JMppoPJ9N1DrdaHVEeunYrLASbtH4NxojhTFZ4oDL-XBbS5hoeVHKi3z9wl" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block; margin-left: auto; margin-right: auto; max-width: 100%; width: 540px; height: 325px;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> <h2 dir="ltr">About Mitsubishi Chemical Group Growth Garage</h2>Growth Garage is the Open Innovation Hub of the Mitsubishi Chemical Group. Its mission is to support and develop new ideas and projects using advanced manufacturing technologies and Specialty Materials, to help tackle some of today's biggest engineering challenges. Growth Garage is offering opportunities for engineers, innovators, and entrepreneurs from startups, scaleups and innovative companies to join the community and submit their ideas and projects through the open innovation programs. Successful applications will get exposure and access to Mitsubishi Chemical Group’s network of industry experts, Mitsubishi Chemical Group’s Corporate Venture Capital team and business partners to advance their ideas and projects.

3D printed Body-contouring Therapeutic Positioning Equipment provides support for people with mild to moderate control of the body and reduces damage caused by improper seating during child development.


Revolutionizing Comfort: Testa-Seat Triumphs in the FGF Engineering Challenge with Innovative 3D-Printed Adaptive Seating

In this episode, we discuss a novel sticker capable of monitoring the health of organs in real time allowing for more successful organ transplants and catching signs of diseases earlier than ever!

Podcast: Sticker to Monitor Your Internal Organs

Fat tissue holds the key to 3D printing layered living skin and potentially hair follicles, according to researchers who recently harnessed fat cells and supporting structures from clinically procured human tissue to precisely correct injuries in rats. The advancement could have implications for reconstructive facial surgery and even hair growth treatments for humans.&nbsp;The team’s findings published today (March 1) in&nbsp;<a href="https://doi.org/10.1016/j.bioactmat.2023.10.034" target="_blank">Bioactive Materials</a>. The U.S. Patent and Trademark Office granted the team a patent in February for the bioprinting technology it developed and used in this study.&nbsp;“Reconstructive surgery to correct trauma to the face or head from injury or disease is usually imperfect, resulting in scarring or permanent hair loss,” said Ibrahim T. Ozbolat, professor of engineering science and mechanics, of biomedical engineering and of neurosurgery at Penn State, who led the international collaboration that conducted the work. “With this work, we demonstrate bioprinted, full thickness skin with the potential to grow hair in rats. That’s a step closer to being able to achieve more natural-looking and aesthetically pleasing head and face reconstruction in humans.”While scientists have previously 3D bioprinted thin layers of skin, Ozbolat and his team are the first to intraoperatively print a full, living system of multiple skin layers, including the bottom-most layer or hypodermis. Intraoperatively refers to the ability to print the tissue during surgery, meaning the approach may be used to more immediately and seamlessly repair damaged skin, the researchers said. The top layer — the epidermis that serves as visible skin — forms with support from the middle layer on its own, so it doesn’t require printing. The hypodermis, made of connective tissue and fat, provides structure and support over the skull.“The hypodermis is directly involved in the process by which stem cells become fat,” Ozbolat said. “This process is critical to several vital processes, including wound-healing. It also has a role in hair follicle cycling, specifically in facilitating hair growth.”The researchers started with human adipose, or fat, tissue obtained from patients undergoing surgery at Penn State Health Milton S. Hershey Medical Center. Collaborator Dino J. Ravnic, associate professor of surgery in the Division of Plastic Surgery at Penn State College of Medicine, led his lab in obtaining the fat for extraction of the extracellular matrix — the network of molecules and proteins that provides structure and stability to the tissue — to make one component of the bioink.Ravnic’s team also obtained stem cells, which have the potential to mature into several different cell types if provided the correct environment, from the adipose tissue to make another bioink component. Each component was loaded into one of three compartments in the bioprinter. The third compartment was filled with a clotting solution that helps the other components properly bind onto the injured site.“The three compartments allow us to co-print the matrix-fibrinogen mixture along with the stem cells with precise control,” Ozbolat said. “We printed directly into the injury site with the target of forming the hypodermis, which helps with wound healing, hair follicle generation, temperature regulation and more.”They achieved both the hypodermis and dermis layers, with the epidermis forming within two weeks by itself.“We conducted three sets of studies in rats to better understand the role of the adipose matrix, and we found the co-delivery of the matrix and stem cells was crucial to hypodermal formation,” Ozbolat said. “It doesn’t work effectively with just the cells or just the matrix — it has to be at the same time.”They also found that the hypodermis contained downgrowths, the initial stage of early hair follicle formation. According to the researchers, while fat cells do not directly contribute to the cellular structure of hair follicles, they are involved in their regulation and maintenance.“In our experiments, the fat cells may have altered the extracellular matrix to be more supportive for downgrowth formation,” Ozbolat said. “We are working to advance this, to mature the hair follicles with controlled density, directionality and growth.”According to Ozbolat, the ability to precisely grow hair in injured or diseased sites of trauma can limit how natural reconstructive surgery may appear. He said that this work offers a “hopeful path forward,” especially in combination with other projects from his lab involving printing bone and investigating how to match pigmentation across a range of skin tones.“We believe this could be applied in dermatology, hair transplants, and plastic and reconstructive surgeries — it could result in a far more aesthetic outcome,” Ozbolat said.“With the fully automated bioprinting ability and compatible materials at the clinical grade, this technology may have a significant impact on the clinical translation of precisely reconstructed skin.”Ravnic and Ozbolat also are affiliated with the Huck Institutes of the Life Sciences and the Penn State Cancer Institute. Ozbolat has additional affiliations with the Penn State Materials Research Institute and the Department of Medical Oncology at Cukurova University in Turkey, where he is currently on sabbatical leave. Other contributors include Yogendra Pratap Singh, postdoctoral scholar, and Mecit Altan Alioglu, graduate student, both in the Penn State Department of Engineering Science and Mechanics; Youngnam Kang and Miji Yeo, both researchers, and Irem Deniz Derman, graduate student, in the Huck Institutes of the Life Sciences; Yang Wu, Harbin Institute of Technology in China; and Jasson Makkar and Ryan R. Driskell, College of Veterinary Medicine at Washington State University. Kang, Yeo, Singh, Alioglu and Derman also are affiliated with Penn State Department of Engineering Science and Mechanics.

Miji Yeo, a postdoctoral researcher at Penn State, checks the bioink cartridges on a 3D printer developed to intraoperatively print layers of skin. Credit: Michelle Bixby/Penn State. All Rights Reserved.

Bioengineered advancement may have implications for more natural-looking reconstructive surgery outcomes, according to international research team

3D-printed skin closes wounds and contains hair follicle precursors

<h2 id="isPasted">What is silver/silver chloride ink?</h2>‍Silver/silver chloride (Ag/AgCl) is a conductive ink typically used to print electrodes for biomedical applications. It’s a non-toxic, environmentally friendly ink with stable electrochemical potential used to create disposable on-skin medical electrodes for biosensors, like "ECG, EEG and TENS electrodes; defibrillator pads; transdermal drug delivery; biomedical sensors and reference electrodes," according to <a href="https://www.creativematerials.com/products/ag-agcl-inks/" target="_blank">Creative Materials</a>.&nbsp;‍<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524616616-63d022dacfb6e86a1a3cf1c4_ffhgM2WgWmVRDuIv4GtMPtOgJnNsHrpU0DlhQb2hw9DhzCZ4C7IujXkmI6-Wb1oQkcFbDkFukxQI-6qOCSdGzc-RG3ihBfYeMomcrtZXFV6dX66qBWO6I56fjNV7IngcIVg4ZVpc3em72_ZUQ7XEEeOsDnoXYJ3UkXIu4GSpT1uTJ1njEsNQKtcxJaKNC.png" style="width: 100%px;" class="fr-fic fr-dib">Source: Henkel<h3 id="isPasted">Features:</h3><ul><li>Silver-to-silver chloride ratios can be adjusted and blended to achieve different resistance values and sensitivity levels.&nbsp;</li><li>The ink has a smooth surface finish with excellent adhesion and crease resistance when printed onto polyester and plastic substrates.&nbsp;</li><li>Silver/silver chloride is compatible with screen printing and direct-ink-writing printing technology.&nbsp;</li></ul><h2>Smart health patches</h2>‍Used in both healthcare and athletics applications, smart health patches deliver new capabilities in monitoring. These devices can continuously measure heart rate, brain activity, movement, and other body functions. <a href="https://www.quad-ind.com/smart-health-patches-to-run-smart/" target="_blank">Quad Industries’ Smart Health Patch</a>, designed to optimize training for ultra-runners, is made using Henkel’s silver/silver chloride ink.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524652661-63d022daa5cbee3ab018e3e7_8VgrqoubKIfwaL1zITpxMMjBjwZ0pwQdx2p_UCyq48yZIDh9jqqb281s3xO3BjQ7xTJO1oM0xUcUoKoQ6506xXmB2AnVlUENl_QzrSiBiDSWsE25qMLNrOtCmqt1c8hxKm-xZMRmZoxMabvNRQA6Udn9Q6RByf0cqbC_Dy22HfbQWYy4S7Tu8SvzcJjw9.png" style="width: 100%px;" class="fr-fic fr-dib">Source: Quad Industries<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524667591-63d022dac9c95f5daebb1936_7x7-GMmj24mLom3wqW-nHmMi9PeRUDPwS6Pv5tYsxw_QONplpV8Uyxc6qzd6FJCo3t4G_uNybUwFGiJohdDlYNlrqbXxN0Dv3g1kujpKAT8hPn5BLnvSYClXC0DZIMi8kvIL44HJyqEZwq3CgivP2w_SNr8UElf52dxJI3B_k1N1qiwnP6yNkXgmyRzOe.png" style="width: 100%px;" class="fr-fic fr-dib"><figure data-rt-type="image" data-rt-align="center" id="isPasted"><figcaption>Source: Quad Industries‍</figcaption></figure>‍Silver/silver chloride ink is also used to print glucose sensors for continuous glucose monitoring (CGM) patches like Freestyle Libre.CGM patches are being widely adopted for diabetes management. Rather than pricking a finger several times a day to monitor glucose levels, the CGM continuously measures glucose in the blood and interfaces with a reader device or smartphone app. This allows patients to monitor glucose levels throughout the day and refine the timing for their insulin injections.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524688181-63d022da3ae9a36f914066d5_hHSjbJgZWS36H1qokZ_5LN2coCJiIs7H9b7Uw6TUm0o7Qbss4LCqngtZB53W-wdG_sMiMR7KnUMKSRcuX3OhSHY_ngYl7SoPJEe1ZVD_LTrvMQfALXzmxpZAVSwQMxAkHLrVb_SZ0MxJqfYS9fmmN_cH8CSOwUOq1YLmYLEZqfyrDEwgtp1lhSBGSFLoV.png" style="width: 100%px;" class="fr-fic fr-dib">Source: Healthline<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524702228-63d022da3916c36e5646942d_QdXGhsRpN4wtDyT6VosSkgxf0n4hxX7QICaxkDGbyBOM1Ar0t7ehDpr8ZQoIHzbhKxWRzY8_0G1bBz20I3Yi61Pf0pI0vCZ-6Bd6a7TGwEZp6waSXE0_Ascb-h8PVZQsQcwqcIt5-SUFVGauo6chHPNjqTfZa3hUNWCJfkaXF0WyMdRWn0TQdMiYLySC.jpeg" style="width: 100%px;" class="fr-fic fr-dib">Source: Advanced Technologies and Polymer Materials for Surgical Sutures<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524715190-63d022dad7285c542dc110f7_YSW1DtYARgBxTI1OcJYkuL_bHjR4eAI01NARpWowdleL1PdML6EBfQ_ksZ3d9jS4o9G6m6beeDeroNPsSs1OxRiOhx7wZA7-ws935lrFJZpgSVoHFMf49r1TYDTgE-eP-sOOrhR3C15nlwi19GzLEiqP-VQNR-vhQo7fOW3MlwK5SiBN7Uh-ltI3qhHSP.png" style="width: 100%px;" class="fr-fic fr-dib">Source: HenkelIn recent years, CGMs also have become a tool for athletic performance. Endurance athletes use CGMs to monitor for dips in glucose during training, and then methodically dial in their fuel strategy and nutrition plan accordingly. The <a href="https://www.supersapiens.com/" target="_blank">Supersapiens</a> glucose monitoring system is being leveraged by athletes like Crossfit Games champion Samantha Briggs, olympic triathlon gold medalist Kristian Blummenfelt, and endurance obstacle course world champion Ryan Atkins.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1709524734463-63d022dabc5f8813d838b2d8_x7tSiD3dwvioNowEvZUasv-THGBtHupPy1ikaI29n8xyFH_LLcNtG0P3YC-ZncS2A82C9caDVwLeK1aHPlWYoN5ex_AI4YwzcVdOPMuK5d9AG7Os9XvLz1v68Xk56Uu_Q_DKjbceQ84tf8ZoTsFuLDn4GvyU6fWrkS92eL7nrIRSUA7ehMEgPfYecPW2F.png" style="width: 768px;" class="fr-fic fr-dib">Source: SupersapiensSilver/silver chloride ink can also be paired with TPU used to print stretchable interconnections for constructing <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163896/" target="_blank">medical sensor textiles</a> and <a href="https://journals.sagepub.com/doi/abs/10.1177/00405175211005039" target="_blank">smart textile garments</a>. Commercialization has been limited due to lack of cost-effective, standard fabrication processes that can be applied to a large variety of fabrics, but companies like <a href="https://softmatter.io/" target="_blank">Softmatter</a> and <a href="https://www.memtronik.com/printed-electronics/smart-clothing/" target="_blank">Memtronik</a> now offer customized smart-garment design and fabrication.<hr><h2>I want this ink. Where do I start?</h2><h4>‍Henkel Adhesive Technologies</h4>If you work in the engineering world, you’re probably familiar with Loctite, a line of products by Henkel Adhesive Technologies. Loctite EDAG 7019 E&amp;C is a silver/silver chloride conductive ink, formulated to provide excellent adhesion to the wide variety of substrates used in the manufacture of electrically conductive medical devices.&nbsp;Read the datasheet here: <a href="https://tds.henkel.com/tds5/Studio/ShowPDF/?pid=EDAG%207019%20EC&format=MTR&subformat=HYS&language=EN&plant=WERCS&authorization=2" target="_blank">LOCTITE EDAG 7019 E&amp;C Technical Data Sheet </a>Henkel also sells a&nbsp;<a href="https://print-your-electronics-with-loctite.com/LOCTITE-SENSOR-DEMONSTRATOR" target="_blank">Loctite sensor demonstrator kit</a> with twelve dry adhesive electrodes for medical research and technical material validation. Henkel Adhesive Technologies have developed a wide portfolio of innovative solutions in adhesives, sealants, inks, and functional coatings.&nbsp;<h4>Dupont</h4>‍Another option is Dupont 5874 silver/silver chloride, which is designed for screen printing on polyester, and is suitable for a wide variety of biomedical applications. Dupont is an industry leader in materials development, combining scientific and applications development expertise to accelerate innovation. Read the datasheet here: <a href="https://www.dupont.com/content/dam/dupont/amer/us/en/mobility/public/documents/en/EI-10086-DuPont-5874-Ag_AgCI-Composition-Data-Sheet.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">Dupont 5874 silver/silver chloride composition for screen printing</a><h4>Kayaku Advanced Materials‍</h4>Kayaku Advanced Materials also offers a silver/silver chloride conductive ink, designed for screen printing medical EEG and EKG sensors. Kayaku develops and manufactures innovative specialty materials to push the latest innovation trends. Read the datasheet here: <a href="https://kayakuam.com/wp-content/uploads/2020/03/agcl-675-silver-chloride-ink.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">AGCL-675 SILVER/SILVER CHLORIDE INK&nbsp;</a>

‍Silver/silver chloride (Ag/AgCl) is a conductive ink typically used to print electrodes for biomedical applications.

Materials Feature: Silver or Silver Chloride Ink

After the incredible impact of the previous Build2gether 1.0 challenge, we’re thrilled to invite innovators, dreamers, and engineers from around the globe to participate in this year's challenge for groundbreaking solutions that enhance accessibility for people with disabilities.Objective: Focus on creating assistive technologies that address the unique challenges faced by individuals with visual and mobility impairments, enhancing their daily lives both indoors and outdoors.Prizes:&nbsp;Over $100,000 in Prizes.&nbsp;From the Build2Gether 2.0 Ultimate Award to multiple category prizes and lucky draws.<a href="https://wevlv.co/btg2-hack-lp" target="_blank" rel="noopener noreferrer" class="button-main-action">Learn more</a><h2 id="isPasted">Two Paths to Innovation</h2>This year's challenge is organized into two main focus areas, each divided into two specific tracks to guide your innovations. There are several Contest Masters (i.e., experts in the disability space) helping you in each track.VISUAL IMPAIRMENTS<ul><li>Track 1:Adaptation for&nbsp;OUTDOOR&nbsp;Activities for people with visual impairments with Contest Masters: Marc Chiang, Steady Goh, Stuart Francis, Tony Morgan, Kah Wee Neo, Joan Hung Hui Xin, and Josh Tseng.</li><li>Track 2:Adaptation for INDOOR Activities for people with visual Impairments&nbsp;with Contest Masters: Amanda Chong, Amanda Swafford, Benson Loo, Hong Sen, Patricia Poo, Alan Etermi, and Dallon Au Yew Zhang.</li></ul>MOBILITY IMPAIRMENTS<ul><li>Track 1:&nbsp;Accessible HOME &amp; TOOLS&nbsp;for people with mobility impairments&nbsp;with Contest Masters: Paul Fjare, Drew McPherson, Paul Amadeus Lane, and Vivek Gohil.</li><li>Track 2:&nbsp;Accessible SPORTS &amp; HOBBIES for People with Mobility Impairments&nbsp;with Contest Masters: Julie Keenan, Ashley Lindsey, Jake Scarborough, and Hoàng Gia Khánh.</li></ul><h3>Win Big and Make an Impact:</h3><ul><li>200 Hardware SUPERBOXES up for grabs, each packed with cutting-edge tech.</li><li>Over $100,000 in Prizes: From the Build2Gether 2.0 Ultimate Award to multiple category prizes and lucky draws.</li></ul>The SUPERBOX contains the following hardware:<ul><li><a href="https://blues.com/" data-ha='{"eventName":"Clicked link","customProps":{"value":"Blues","href":"https://blues.com/","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Blues</a>' kit worth $130 (<a href="https://shop.blues.com/collections/swan/products/swan" data-ha='{"eventName":"Clicked link","customProps":{"value":"Swan v3","href":"https://shop.blues.com/collections/swan/products/swan","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Swan v3</a>,&nbsp;<a href="https://shop.blues.com/collections/notecarrier/products/carr-al" data-ha='{"eventName":"Clicked link","customProps":{"value":"Notecarrier AL or AA","href":"https://shop.blues.com/collections/notecarrier/products/carr-al","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Notecarrier AL or AA</a>,&nbsp;<a href="https://shop.blues.com/collections/notecard/products/notecard" data-ha='{"eventName":"Clicked link","customProps":{"value":"Notecard Cellular NBGL","href":"https://shop.blues.com/collections/notecard/products/notecard","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Notecard Cellular NBGL</a>,&nbsp;<a href="https://shop.blues.com/collections/notecard/products/wifi-notecard" data-ha='{"eventName":"Clicked link","customProps":{"value":"Notecard WiFi v1","href":"https://shop.blues.com/collections/notecard/products/wifi-notecard","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Notecard WiFi v1</a>).</li><li><a href="https://www.nordicsemi.com/" data-ha='{"eventName":"Clicked link","customProps":{"value":"Nordic Semiconductor","href":"https://www.nordicsemi.com/","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Nordic Semiconductor</a>'s&nbsp;<a href="https://www.nordicsemi.com/Products/Development-hardware/nrf52840-dk" data-ha='{"eventName":"Clicked link","customProps":{"value":"nRF52840 DK","href":"https://www.nordicsemi.com/Products/Development-hardware/nrf52840-dk","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">nRF52840 DK</a>&nbsp;worth $40.</li><li><a href="https://www.seeedstudio.com/?gad_source=1&gclid=CjwKCAiA29auBhBxEiwAnKcSqoK1Oe2qAYQ7zDNvRcVUjsKDK6-d3mpEPcpYo00BeFEuC66g1oDICRoCqM4QAvD_BwE" data-ha='{"eventName":"Clicked link","customProps":{"value":"Seeed Studio","href":"https://www.seeedstudio.com/?gad_source=1\u0026gclid=CjwKCAiA29auBhBxEiwAnKcSqoK1Oe2qAYQ7zDNvRcVUjsKDK6-d3mpEPcpYo00BeFEuC66g1oDICRoCqM4QAvD_BwE","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Seeed Studio</a>'s kit worth $20 (<a href="https://www.seeedstudio.com/XIAO-ESP32S3-Sense-p-5639.html" data-ha='{"eventName":"Clicked link","customProps":{"value":"XIAO ESP32S3 Sense","href":"https://www.seeedstudio.com/XIAO-ESP32S3-Sense-p-5639.html","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">XIAO ESP32S3 Sense</a>&nbsp;+&nbsp;<a href="https://www.seeedstudio.com/Grove-Shield-for-Seeeduino-XIAO-p-4621.html" data-ha='{"eventName":"Clicked link","customProps":{"value":"Grove Shield for Xiao","href":"https://www.seeedstudio.com/Grove-Shield-for-Seeeduino-XIAO-p-4621.html","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">Grove Shield for Xiao</a>).</li><li><a href="https://www.dfrobot.com/" data-ha='{"eventName":"Clicked link","customProps":{"value":"DFRobot","href":"https://www.dfrobot.com/","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">DFRobot</a>&nbsp;$80 giftcard for&nbsp;<a href="https://www.dfrobot.com/product-2691.html" data-ha='{"eventName":"Clicked link","customProps":{"value":"UNIHIKER","href":"https://www.dfrobot.com/product-2691.html","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">UNIHIKER</a>.</li><li><a href="https://www.pcbway.com/" data-ha='{"eventName":"Clicked link","customProps":{"value":"PCBWay","href":"https://www.pcbway.com/","type":"story","location":"story"},"clickOpts":{"delayRedirect":true}}' rel="nofollow" target="_blank">PCBWay</a>&nbsp;$40 giftcard.</li></ul><h3 id="isPasted">Key Dates</h3><table><tbody><tr><td>EVENT</td><td>DATE</td></tr><tr><td>Contest Begins</td><td>February 26, 2024 at 3:00 PM PST</td></tr><tr><td>Submissions close</td><td>August 15, 2024 at 11:00 PM PDT</td></tr><tr><td>Winners announced by</td><td><div data-hypernova-key="ContestBriefPage" data-hypernova-id="e201336d-ef39-435b-a2dc-a0af0298ffbc" id="isPasted"><div data-reactroot=""><div data-hypernova-key="ContestBriefPage" data-hypernova-id="98dfc449-4aee-4f2c-8039-a0ec0e635839" id="isPasted"><div data-reactroot="">Sep 1, 2024</div></div></div></div></td></tr></tbody></table><h2>Get Started:</h2>Engage: Submit up to two solutions, one for each focus area. *For your submission to be eligible to receive prizes, you must fill out the survey found at hackster.io by&nbsp;May 1, 2024 at 10:00 AM PT.Collaborate: Benefit from tailored feedback from our Contest Masters.Innovate: Your solutions could win you amazing prizes and the chance to change lives.Everything you need to know, including information on the prizes, Contest Masters etc. can be found at the <a target="_blank" data-stringify-link="http://hackster.io" data-sk="tooltip_parent" href="https://wevlv.co/btg2-hack-lp" rel="noopener noreferrer" id="isPasted">hackster.io</a> page.<a href="https://wevlv.co/btg2-hack-lp" target="_blank" rel="noopener noreferrer" class="button-main-action">Learn more</a> <h2>Sponsors of the Build2gether 2.0 Challenge</h2><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5Mjg5ODk5MDY2LXNwb25zb3JzdHJ1Y3R1cmVfVVFvcjFuQWlBTSAoMSkucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">

Explore and address needs in assistive technology, creating meaningful solutions for individuals with disabilities. Win great prizes!

Build2gether 2.0 Inclusive Innovation Challenge

We’ve all heard of Large Language Models, or LLMs – massive scale deep learning models trained on huge amounts of text that form the basis for chatbots like OpenAI’s ChatGPT. Next-generation Multimodal Models (MMs) can learn from inputs beyond text, including video, images, and sound.Creating MM models at a smaller scale poses significant challenges, including the problem of being robust to non-random missing information. This is information that a model doesn’t have, often due to some biased availability in resources. It is thus critical to ensure the model does not learn the patterns of biased missingness in making its predictions.<h2>MultiModN turns this around</h2>In response to this problem, researchers from the&nbsp;<a href="https://www.epfl.ch/labs/ml4ed/" target="_blank">Machine Learning for Education</a> (ML4ED) and&nbsp;<a href="https://www.epfl.ch/labs/mlo/" target="_blank">Machine Learning and Optimization</a> (MLO) Laboratories in<a href="https://www.epfl.ch/schools/ic/" target="_blank">&nbsp;EPFL’s School of Computer and Communication Sciences</a> have developed and tested the exact opposite to a Large Language Model.Spearheaded by Professor Mary-Anne Hartley, head of the Laboratory for<a href="https://www.epfl.ch/labs/mlo/igh-intelligent-global-health/" target="_blank">&nbsp;intelligent Global Health Technologies</a> hosted jointly in the MLO and the Yale School of Medicine and Professor Tanja Käser, head of ML4ED,&nbsp;MultiModN&nbsp;is a unique&nbsp;<a href="https://arxiv.org/abs/2309.14118" target="_blank" rel="noopener">Modular Multimodal Model</a>, presented recently at the&nbsp;<a href="https://neurips.cc/Conferences/2023" target="_blank" rel="noopener">NeurIPS2023</a> conference.Like existing Multimodal Models&nbsp;MultiModN can learn from text, images, video, and sound. Unlike existing MMs, it is made up of any number of smaller, self-contained, and input-specific modules that can be selected depending on the information available, and then strung together in a sequence of any number, combination, or type of input. Itcan then output any number, or combination, of predictions.“We evaluated&nbsp;MultiModN across ten real-world tasks including medical diagnosis support, academic performance prediction, and weather forecasting. Through these experiments, we believe that&nbsp;MultiModN is the first inherently interpretable, MNAR-resistant approach to multimodal modelling,” explained Vinitra Swamy, a PhD student with ML4ED and MLO and joint first author on the project.<h3>A first use case: medical decision-making</h3>The first use case for&nbsp;MultiModN will be as a clinical decision support system for medical personnel in low-resource settings. In healthcare, clinical data is often missing, perhaps due to resource constraints (a patient can’t afford the test) or resource abundance (the test is redundant due to a superior one that was performed).&nbsp;MultiModN is able to learn from this real-world data without adopting its biases, as well as adapting predictions to any combination or number of inputs.“Missingness is a hallmark of data in low-resource settings and when models learn these patterns of missingness, they may encode bias into their predictions. The need for flexibility in the face of unpredictably available resources is what inspired&nbsp;MultiModN,” explained Hartley, who is also a medical doctor.<h3>From the lab to real life</h3>Publication, however, is just the first step towards implementation. Hartley has been working with colleagues at Lausanne University Hospital<a href="https://www.chuv.ch/fr/chuv-home/" target="_blank" rel="noopener">&nbsp;(CHUV</a>) and Inselspital, University Hospital Bern&nbsp;<a href="https://www.insel.ch/fr/" target="_blank" rel="noopener">uBern</a> to conduct clinical studies focused on pneumonia and tuberculosis diagnosis in low resource settings and they are recruiting thousands of patients in South Africa, Tanzania, Namibia and Benin.The research teams undertook a large training initiative, teaching more than 100 doctors to systematically collect multimodal data including images and ultrasound video, so that&nbsp;MultiModN can be trained to be sensitive to real data coming from low resource regions.“We are collecting exactly the kind of complex multimodal data that&nbsp;MultiModN is designed to handle,” said Dr Noémie Boillat-Blanco, an infectious diseases doctor at CHUV. “We are excited to see a model that appreciates the complexity of missing resources in our settings and of systematic missingness of routine clinical assessments,” added Dr Kristina Keitel at Inselspital, University Hospital Bern.<h3>Machine learning for the public good</h3>The development and training of&nbsp;MultiModN is a continuation of EPFL efforts to adapt machine learning tools to reality and for the public good. It comes not long after the launch of<a href="https://actu.epfl.ch/news/epfl-s-new-large-language-model-for-medical-knowle/" target="_blank">&nbsp;Meditron</a>, the world’s best performing open source LLM also designed to help guide clinical decision-making.Both tools align with the mission of the new&nbsp;<a href="https://actu.epfl.ch/news/opening-of-the-new-epfl-ai-center-a-hub-for-ai-i-2/" target="_blank">EPFL AI Center</a> that focuses on how responsible and effective AI can advance technological innovation for the benefit of all sectors of society.Dr. Mary-Anne Hartley is a keynote speaker at the upcoming Applied Machine Learning Days (AMLD) at the SwissTech Convention Center March 23-26, 2024. Vinitra Swamy will be presenting MultiModN at AMLD’s Applied eXplainable AI track. Discover other keynotes<a href="https://2024.appliedmldays.org/media-9-speakers" target="_blank" rel="noopener">here</a>&nbsp;and register<a href="https://2024.appliedmldays.org/media-12-registration" target="_blank" rel="noopener">here</a>.

Machine Learning Neural Network © iStock

Researchers at EPFL have developed a new, uniquely modular machine learning model for flexible decision-making. It is able to input any mode of text, video, image, sound, and time-series and then output any number, or combination, of predictions. 

Anything-in-anything-out: a new modular AI model

In this episode, we discuss the shortcomings of previous attempts at making flexible wearable sensors and how researchers at CalTech have addressed them to create high performance stress sensor stickers.

Stress Sensing Stickers

After a stroke, survivors often experience uncontrollable spasms that can twist their arms and hands into perpetual fists. The only treatments are expensive, frequently painful injections of botulinum toxin or oral medications so strong they may put patients to sleep. Both offer only temporary relief.Engineers at Stanford University and the Georgia Institute of Technology now say they have developed a glove-like wearable medical device that achieves as-good-or-better results as the injections or drugs by applying simple, high-frequency mechanical vibrations to the hands and fingers.“Vibration therapies are not unknown, so we knew we would see some effect, but clinically it was just amazing to see that these beneficial effects persist,” explained <a href="https://profiles.stanford.edu/allison-okamura" data-extlink="" target="_blank">Allison Okamura</a>, Richard W. Weiland Professor are the Stanford School of Engineering and senior author of a series of studies introducing the glove.“There are significant improvements in symptoms using the simulation method. And we think it can potentially address several effects of stroke — numbness, spasticity, and, for some, limited range of motion,” said <a href="https://neuroscience.stanford.edu/people/caitlyn-seim" data-extlink="" target="_blank">Caitlyn Seim</a>, a post-doctoral researcher in Okamura’s lab and initiator of the project. “We're most excited about treating spasticity because it affects so many people's quality of life and has such limited treatment options.”<h2>Form factor</h2>To the researchers’ knowledge this is the first effort to explore vibrotactile stimulation (VTS) technique at a wearable scale. State-of-the-art methods employ whole-body vibration using machines that are large, expensive, and require visits to the clinic for treatment.Patients in trials wore the VTS device three hours a day for eight weeks. Results were compared to the effectiveness of injections and oral medications. When measured, more than half of participants who receive regular botulinum injections had equal or better results with daily use of the VTS glove.“I think we can safely say that VTS is at least as good as injections and oral medicines,” Okamura said. “And it was exciting to see there is some maintenance of the improvements over time, though more study of that is needed.”The prototype device was first developed by Seim and colleagues at Georgia Tech as a tool to help people learn motor tasks such as typing or playing musical instruments via haptic guidance. The device was later studied as an interactive music exercise tool for people with partial spinal cord injuries, which led the team to the realization that haptic stimulation may be therapeutic on its own. Their unique wearable form factor makes a new paradigm in intensive stimulation therapy possible, since users can wear the gloves throughout the day.“We started to wonder if stimulation might also be a therapeutic tool for brain and spinal injury. It was very exciting,” Seim said.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1707898661498-vibrating-glove-helps-1.jpg" style="width: 766px;" class="fr-fic fr-dib">The VTS Glove on a patient’s hand. The team engineered and fabricated over 20 devices for this study in-house in Stanford labs. | Image courtesy of Andrew Brodhead<h2 id="isPasted">Notable results</h2>The latest version of the VTS device wraps around the wrist, palm, and fingers and includes motors that provide continual high-frequency stimulation to individual fingers as well as muscles in the hand and wrist. The vibrations are subtle and unobtrusive — similar to the intensity of a cell phone vibrating. Patients are free to go about their normal activities while wearing the device.Neurologically, the mechanisms underlying the symptom improvements are still being studied, but the team theorizes that the device is taking advantage of the brain’s remarkable plasticity — its ability to find new pathways in response to damage or dysfunction. They believe that the VTS vibrations can modulate out-of-control muscles and send neural signals from the muscles back to the brain where the signals re-train undamaged areas of the brain. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; .“The brain is essentially relearning the ability to perceive touch and proprioception,” Seim said of the brain’s ability to feel and understand where and how the arm, hands, and fingers are positioned in three-dimensional space.In candid video testimonials, patients speak in glowing terms of their preference for VTS over injections and medication. Those same patients also say that the VTS glove is “pleasant” or at least “comfortable” to wear. VTS is non-invasive, inexpensive, and avoids the side effects of injections and muscle relaxants. Most promising of all, Seim notes that many patients in the trials reported they voluntarily reduced their use of oral medications and some even planned to forego quarterly botulinum injections in favor of the VTS glove &nbsp; &nbsp; .“This is the magic glove, it’s better than [botulinum],” said one patient after just two weeks using VTS. “My fingers and wrist are more relaxed. Before it was very difficult to straighten my fingers and now it’s a lot easier.”“I stopped my morning dose of [muscle relaxer] and now I want to stop taking my night dose because the glove is working and I don’t need it,” noted another patient after a similar two-week period.The hope may be shared as both researchers say they believe VTS might also prove effective in other brain injury-related motor conditions and diseases — areas the team is already pursuing as they explore stroke effectiveness.“There are currently 100 million people worldwide living with stroke,” Seim said. “As many as one third experience these paralyzing arm spasms. We targeted the largest-possible population first, but certainly other conditions are on our radar.”As of now, the VTS device is available only to patients in the clinical trial, and Okamura and Seim are gearing up for the next phases that will include additional clinical studies to understand the device’s long-term effectiveness and optimal design.To that end, the team recently received a considerable grant from the National Science Foundation’s Convergence Accelerator program to further develop the vibrotactile glove as a commercial product.“We think we have enough clinical study under our belt to know that VTS is a very safe, effective treatment,” Okamura said. “We'd love to start getting it out there as a publicly available therapy.”Okamura is also a member of&nbsp;<a href="http://biox.stanford.edu/" data-extlink="" target="_blank">Stanford Bio-X</a>, the&nbsp;<a href="https://humanperformance.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Human Performance Alliance</a>, and the&nbsp;<a href="https://neuroinstitute.stanford.edu/" data-extlink="" target="_blank">Wu Tsai Neurosciences Institute</a>, and a faculty affiliate of the&nbsp;<a href="https://hai.stanford.edu/" data-extlink="" target="_blank">Institute for Human-Centered Artificial Intelligence (HAI)</a>.

Postdoctoral researcher Caitlyn Seim takes measurements with a participant in the clinical trial. | Image courtesy of Andrew Brodhead

For those with stroke, involuntary contractions of the hands and arms often follow. A simple, wearable vibrating glove may offer a more effective treatment.

Vibrating glove helps stroke patients recover from muscle spasms

RE2 Robotics

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healthcare

Autonomous Mobile Robots employed for material handling; Credits: Teradyne

This article offers an in-depth view of AMR robots, their underlying technology, and applications crucial in today's rapidly evolving technological landscape.

Understanding AMR Robots: A Comprehensive Guide

This is the 2nd article in a 3 part series featuring the applications of tinyML. The series introduces the concept of tinyML and explains some selected applications of the technology in various fields like sustainability, healthcare and education.By adopting digital advancements, healthcare providers are updating their services to improve them and make them accessible to more people. 5G networks, Augmented Reality (AR) &amp; Virtual Reality (VR), Wearables, Digital Twins, and many more technologies are enabling this digital transformation.One such technology with great potential in ensuring healthy lives and well-being for all is tinyML. The concept is a crossover between Artificial Intelligence (AI) and embedded systems that involves developing solutions that bring Machine Learning (ML) capabilities to edge devices. Benefits include privacy, affordability, low-power utilisation, the ability to work in remote locations, and a lot more.In our <a href="https://www.wevolver.com/article/tinyml" target="_blank">previous article</a> about the applications of tinyML, we explained how it unlocks new possibilities for sustainable development technologies. This article continues the series by showcasing two more applications of tinyML for medical research and improved healthcare.<h2 dir="ltr">MaRTiny: a tinyML solution for extreme heat sensing for urban climate science</h2>As a direct effect of global warming, summers are getting warmer. [1] This disruption of the natural balance poses a risk to all living entities on the planet. Higher temperatures are known to cause a degradation in the air quality and induce severe health-related issues like heat strokes. Looking at the data of weather-related fatalities in America, heat is the single most significant cause of weather-related deaths over the last 30 years, surpassing floods and storms. [2]But it’s not just the ambient temperature that causes discomfort. It’s the Mean Radiant Temperature (MRT) which accounts for the influence of all the surfaces in the surrounding that ultimately determines the occupant’s comfort. Often, the effect of roads, buildings and other entities has an adverse effect on the MRT, creating a complex problem intersecting health and urban infrastructure.To aid the collection of information related to the MRT, a tinyML solution called MaRTiny was developed by researchers from Arizona State University (ASU). It is a $200 low-cost mobile platform for bio-metrological sensing.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3MzYxNzgyLTE2NDI1MTczNjE3ODIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="297" id="isPasted" class="fr-fic fr-dii" alt="MaRTiny-tech-stack">The tech stack of the MaRTiny platformThe list of sensors embedded on the device include:<ul><li dir="ltr">Ambient temperature sensor</li><li dir="ltr">Globe temperature sensor for sensing the radiant heat</li><li dir="ltr">Humidity sensor</li><li dir="ltr">Ultra Violet (UV) sensor</li><li dir="ltr">Anemometer for measuring wind speed and direction.</li></ul>The platform uses an <a href="https://www.wevolver.com/profile/arduino.pro" target="_blank">Arduino</a> Uno to collect the data and a NodeMCU to provide it with the electronic infrastructure required for wireless data communication. At the core of the system is the Nvidia Jetson Nano development board that provides MaRTiny with the power to run tinyML algorithms.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NDcxMTAyLU5WSURJQV9KZXRzb25fTmFub19EZXZlbG9wZXJfS2l0Xyg0MDY1MDQyNTUwMykuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib" alt="nvidia-jetson-nano-development-board">The Nvidia Jetson Nano development board provides MaRTiny with image processing and ML capabilitiesJetson Nano, with its Camera Serial Interface (CSI), can connect with a camera for shadow detection, object detection and people-in-shade detection. Jetson’s ARM Cortex A57 MPcore, 128 CUDA core Maxwell GPU coupled with 4 GB RAM is just enough to offer advanced ML capabilities on edge.The task of MRT estimation was formulated as a supervised learning problem by the inventors that required a lot of true data for training. This true data was collected with the help of a previously developed, larger and more expensive MRT detecting system going by the name of MaRTy. [3]&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NTMzMzM5LTE1MDB4NTAwLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="MaRTiny-MaRTy">MaRTiny aims to offer similar functionality as that of MaRTy (in the image) at just 1% of the price. Image source: @ASUMaRTy, TwitterIt was observed that a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieved the highest accuracy with the MaRTiny. The method being computationally light, offered an easy way of deployment on Jetson. To improve the estimations, both SVM and Artificial Neural Network (ANN) models were utilised and trained with data collected via sensors. [4]For privacy, the images taken by MaRTiny are not stored in the cloud or even in the device’s memory. It automatically discards personally identifiable information (PII) before reporting anything back to the servers, which is central to the idea of tinyML and a must-have feature for public deployment.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NTc1MjI0LWltYWdlNy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="MaRTiny-setup">The experimental setup of MaRTinyThe tinyML solution, MaRTiny, can provide crucial insights related to weather conditions and the utilisation of shades and space by the public. Multiple MaRTiny devices can be installed across the city to gather valuable data for architecture and town planning.<h2 dir="ltr">Pneumonia Detection using Embedded Machine Learning</h2>Pneumonia is a respiratory infection caused by viruses, bacterias and fungi. According to the World Health Organisation (WHO) data, it is estimated that as much as 22% of all deaths in children aged from 1 to 5 is due to pneumonia. Bacteria induced pneumonia can be treated with antibiotics and other low-cost and low-tech medical facilities if detected at the right time. [5]Chest X-ray, blood oxygen levels and Complete Blood Count (CBC) are used to detect pneumonia which is time-consuming. Doctors manually review the chest X-ray and decide if the person is infected, which is sometimes inaccurate and requires an automated solution with better accuracy. This is the idea behind the tinyML solution that we are about to discuss in this section.Arijit Das, a 15-year-old tinyML enthusiast, has developed a tinyML model using the <a href="https://www.wevolver.com/profile/edge.impulse" target="_blank">Edge Impulse</a> platform to detect pneumonia from chest X-rays. The model can be deployed on single board development platforms like Raspberry Pi 4, Sony Supersense and a few other devices. With just the main board, a camera shield and the Edge Impulse tinyML model, the tiny setup can detect pneumonia in under a minute which is phenomenal considering the fact that it takes anywhere between 1 to 4 days for detection by manual inspection.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NzA0Nzk2LVJhc3BiZXJyeV9QaV80X01vZGVsX0JfLV9TaWRlLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="Raspberry-Pi-4">Credit card-sized single-board computers like Raspberry Pi come with enough compute power to run optimised tinyML algorithmsThe tinyML model doesn’t require internet connectivity to work and offers an accuracy of over 80%, which is far better compared to manual inspection. This can prove to be quite helpful in largely affected regions like Sub-Saharan Africa and South Asia, where a huge number of examinations need to be conducted related to the same disease.&nbsp;At the time of writing this article, the project is fully functional and has been clinically tested by over 10 doctors in a local survey conducted by the inventor. The project is available on <a href="https://github.com/Pneumonia-Detection-using-EdgeML" target="_blank">GitHub</a> for everyone to try, test and contribute. [6]<h2 dir="ltr">Conclusion</h2>It’s the third year into the pandemic, and the emphasis on healthcare research is more than ever. Introducing technologies that can work with little power and no network connectivity at an affordable price in healthcare can save several lives. Leveraging interdisciplinary technologies like tinyML is the way forward to a safer and healthier future.About tinyML FoundationWith its workshops, webinars, expert talks, research conferences, competitions, and a plethora of other events, tinyML Foundation enables students, researchers, and industry personnel to come together and share their experiences about the technology. It is the incredibly collaborative nature of the community that allows it to advance rapidly.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQyNTE3NzY5MDkxLWV5SmlkV05yWlhRaU9pSjNaWFp2YkhabGNpMXdjbTlxWldOMExXbHRZV2RsY3lJc0ltdGxlU0k2SW1aeWIyRnNZUzh4TmpNMk9UZ3lNVEUwT1RRNExWUnBibmxOVEdadmNrZHZiMlFnS0RRd01EQWdlQ0EzTlRBZ2NIZ3BMbXB3WnlJc0ltVmthWFJ6SWpwN0luSmxjMmw2WlNJNmV5SjNhV1IwYUNJNk9UVXdMQ0ptYVhRaU9pSmpiM1psY2lKOWZYMD0ud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 300px;" class="fr-fic fr-dib" alt="tinyML-banner"> The <a href="https://www.wevolver.com/article/tinyml-for-good-where-machine-learning-meets-edge-computing" target="_blank">introductory article</a> familiarised the readers with the concept of tinyML.The&nbsp;<a href="https://www.wevolver.com/article/tinyml" target="_blank">first article</a> explained some possibilities of tinyML applications in sustainable technologies.The <a href="https://www.wevolver.com/article/applications-of-tinyml-in-healthcare-for-a-safer-future/" rel="noopener noreferrer" target="_blank">second article</a> is about tinyML applications aiding healthcare and medical research.The third and final article shall cover the tinyML applications in&nbsp;pedagogy&nbsp;and education.<hr><h2>References</h2>[1] Extreme Weather Toolkits, Climate Central, [Online], Available from: &nbsp;<a href="https://medialibrary.climatecentral.org/extreme-weather-toolkits/extreme-heat" target="_blank">https://medialibrary.climatecentral.org/extreme-weather-toolkits/extreme-heat</a>[2] Weather Related Fatality and Injury Statistics, National Weather Service, [Online], Available from:&nbsp;<a href="https://www.weather.gov/hazstat/" target="_blank">https://www.weather.gov/hazstat/</a>[3] Ariane Middel, E. Scott Krayenhoff, ‘Micrometeorological determinants of pedestrian thermal exposure during record-breaking heat in Tempe, Arizona: Introducing the MaRTy observational platform’, Science of The Total Environment, Volume 687, 2019, Pages 137-151, ISSN 0048-9697, Available from:&nbsp;<a href="https://doi.org/10.1016/j.scitotenv.2019.06.085" target="_blank">https://doi.org/10.1016/j.scitotenv.2019.06.085</a>[4] Karthik K. Kulkarni, 2021, ‘Machine Learning and Vision Using Edge Devices for Multimodal Chatbots andBio-meteorological Sensing’, MS Thesis, Arizona State University, United States of America, [Online], Available from:&nbsp;<a href="https://keep.lib.asu.edu/_flysystem/fedora/c7/Kulkarni_asu_0010N_21269.pdf" target="_blank">https://keep.lib.asu.edu/_flysystem/fedora/c7/Kulkarni_asu_0010N_21269.pdf</a>[5] Pneumonia Facts Sheet, World Health Organisation, [Online], Available from:&nbsp;<a href="https://www.who.int/news-room/fact-sheets/detail/pneumonia" target="_blank">https://www.who.int/news-room/fact-sheets/detail/pneumonia</a>[6] Arijit Das, Pneumonia-Detection-using-EdgeML, GitHub, [Online], Available from:&nbsp;<a href="https://github.com/Pneumonia-Detection-using-EdgeML" target="_blank">https://github.com/Pneumonia-Detection-using-EdgeML</a>

In this article, we take a look at two tinyML projects related to healthcare. The first project helps gather Mean Radiant Temperature data outdoors to protect people from extreme heat, and the other one is a solution for affordable, accurate, & rapid detection of pneumonia.

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