healthcare

Implantation of a stent-like flow diverter can offer one option for less invasive treatment of brain aneurysms &ndash; bulges in blood vessels &ndash; but the procedure requires frequent monitoring while the vessels heal. Now, a multi-university research team has demonstrated proof-of-concept for a highly flexible and stretchable sensor that could be integrated with the flow diverter to monitor hemodynamics in a blood vessel without costly diagnostic procedures.The sensor, which uses capacitance changes to measure blood flow, could reduce the need for testing to monitor the flow through the diverter. Researchers, led by Georgia Tech, have shown that the sensor accurately measures fluid flow in animal blood vessels in vitro, and are working on the next challenge: wireless operation that could allow in vivo testing.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571290181747-sensor_9577.jpg" style="width: 766px;" class="fr-fic fr-dib">Woon-Hong Yeo, an assistant professor in Georgia Tech&rsquo;s George W. Woodruff School of Mechanical Engineering and Wallace H. Coulter Department of Biomedical Engineering, holds a flow sensor on a stent backbone. (Credit: John Toon, Georgia Tech)&nbsp;The research was reported July 18 in the journal ACS Nano and was supported by multiple grants from Georgia Tech&rsquo;s Institute for Electronics and Nanotechnology, the University of Pittsburgh and the Korea Institute of Materials Science.&nbsp;&ldquo;The nanostructured sensor system could provide advantages for patients, including a less invasive aneurysm treatment and an active monitoring capability,&rdquo; said&nbsp;<a href="http://www.me.gatech.edu/faculty/yeo" target="_blank">Woon-Hong Yeo</a>, an assistant professor in Georgia Tech&rsquo;s&nbsp;<a href="http://www.me.gatech.edu/" target="_blank">George W. Woodruff School of Mechanical Engineering</a> and&nbsp;<a href="http://www.bme.gatech.edu/" target="_blank">Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University</a>. &ldquo;The integrated system could provide active monitoring of hemodynamics after surgery, allowing the doctor to follow up with quantitative measurement of how well the flow diverter is working in the treatment.&rdquo;Cerebral aneurysms occur in up to five percent of the population, with each aneurysm carrying a one percent risk per year of rupturing, noted Youngjae Chun, an associate professor in the Swanson School of Engineering at the University of Pittsburgh. Aneurysm rupture will cause death in up to half of affected patients.&nbsp;Endovascular therapy using platinum coils to fill the aneurysm sac has become the standard of care for most aneurysms, but recently a new endovascular approach &ndash; a flow diverter &ndash; has been developed to treat cerebral aneurysms. Flow diversion involves placing a porous stent across the neck of an aneurysm to redirect flow away from the sac, generating local blood clots within the sac.&ldquo;We have developed a highly stretchable, hyper-elastic flow diverter using a highly-porous thin film nitinol,&rdquo; Chun explained. &ldquo;None of the existing flow diverters, however, provide quantitative, real-time monitoring of hemodynamics within the sac of cerebral aneurysm. Through the collaboration with Dr. Yeo&#39;s group at Georgia Tech, we have developed a smart flow-diverter system that can actively monitor the flow alterations during and after surgery.&rdquo; &nbsp;Repairing the damaged artery takes months or even years, during which the flow diverter must be monitored using MRI and angiogram technology, which is costly and involves injection of a magnetic dye into the blood stream. Yeo and his colleagues hope their sensor could provide simpler monitoring in a doctor&rsquo;s office using a wireless inductive coil to send electromagnetic energy through the sensor. By measuring how the energy&rsquo;s resonant frequency changes as it passes through the sensor, the system could measure blood flow changes into the sac.&ldquo;We are trying to develop a batteryless, wireless device that is extremely stretchable and flexible that can be miniaturized enough to be routed through the tiny and complex blood vessels of the brain and then deployed without damage,&rdquo; said Yeo. &ldquo;It&rsquo;s a very challenging to insert such electronic system into the brain&rsquo;s narrow and contoured blood vessels.&rdquo;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571290214140-sensor_0903.jpg" style="width: 753px;" class="fr-fic fr-dib">With gloved fingers for scale, a proof-of-concept flow sensor is shown here on a stent backbone. (Credit: Woon-Hong Yeo, Georgia Tech) The sensor uses a micro-membrane made of two metal layers surrounding a dielectric material, and wraps around the flow diverter. The device is just a few hundred nanometers thick, and is produced using nanofabrication and material transfer printing techniques, encapsulated in a soft elastomeric material.&ldquo;The membrane is deflected by the flow through the diverter, and depending on the strength of the flow, the velocity difference, the amount of deflection changes,&rdquo; Yeo explained. &ldquo;We measure the amount of deflection based on the capacitance change, because the capacitance is inversely proportional to the distance between two metal layers.&rdquo;Because the brain&rsquo;s blood vessels are so small, the flow diverters can be no more than five to ten millimeters long and a few millimeters in diameter. That rules out the use of conventional sensors with rigid and bulky electronic circuits.&ldquo;Putting functional materials and circuits into something that size is pretty much impossible right now,&rdquo; Yeo said. &ldquo;What we are doing is very challenging based on conventional materials and design strategies.&rdquo;The researchers tested three materials for their sensors: gold, magnesium and the nickel-titanium alloy known as nitinol. All can be safely used in the body, but magnesium offers the potential to be dissolved into the bloodstream after it is no longer needed.The proof-of-principle sensor was connected to a guide wire in the in vitro testing, but Yeo and his colleagues are now working on a wireless version that could be implanted in a living animal model. While implantable sensors are being used clinically to monitor abdominal blood vessels, application in the brain creates significant challenges.&ldquo;The sensor has to be completely compressed for placement, so it must be capable of stretching 300 or 400 percent,&rdquo; said Yeo. &ldquo;The sensor structure has to be able to endure that kind of handling while being conformable and bending to fit inside the blood vessel.&rdquo;

A proof-of-concept flow sensor is shown here on a stent backbone. (Credit: John Toon, Georgia Tech)

Implantation of a stent-like flow diverter can offer one option for less invasive treatment of brain aneurysms – bulges in blood vessels – but the procedure requires frequent monitoring while the vessels heal.

Integrated Sensor Could Monitor Brain Aneurysm Treatment

3D printed tracheal splints are shown around a model of a patient’s trachea. The splints are designed to be absorbed into the body, allowing for expansion of the trachea and bronchus. (Credit: Rob Felt, Georgia Tech)

Children’s Healthcare of Atlanta has performed Georgia’s first-ever procedure to place 3D-printed tracheal splints in a pediatric patient.

3D-Printed Tracheal Splints Used in Groundbreaking Pediatric Surgery

Rear view of the ankle-assisting exosuits with its soft robotic elements and the cable that transfers mechanical power from actuators operated from the hip to the ankle join. (Image courtesy of the Rolex Awards/Fred Merz)

A soft wearable robotic suit promotes normal walking in stroke patients, opening new approaches to gait re-training and rehabilitation

Post-stroke patients reach terra firma with exosuit technology

Flexible endoscopes can snake through narrow passages to treat difficult to reach areas of the body. However, once they arrive at their target, these devices rely on rigid surgical tools to manipulate or remove tissue. These tools offer surgeons reduced dexterity and sensing, limiting the current therapeutic capabilities of the endoscope.Now, researchers from the&nbsp;<a href="http://seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences</a> and&nbsp;<a href="http://wyss.harvard.edu/" target="_blank">the Wyss Institute for Biologically Inspired Engineering</a> at Harvard University have developed a hybrid rigid-soft robotic arm for endoscopes with integrated sensing, flexibility, and multiple degrees of freedom. This arm &mdash; built using a manufacturing paradigm based on pop-up fabrication and soft lithography &mdash; lies flat on an endoscope until it arrives at the desired spot, then pops up to assist in surgical proceduresThe research is described in <a href="http://onlinelibrary.wiley.com/doi/10.1002/admt.201700135/full" target="_blank">Advanced Materials Technologies</a>.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1569906785018-ArmwithWritingWeb.jpg" style="width: 766px;" class="fr-fic fr-dib">Multi-articulated soft pop-up robotic arm. Concept of the system (left): An endoscope navigating in the GI tract and detail of the arm mounted at the tip of the endoscope. Soft pop-up arm (right) performing tissue counter-traction during an ex-vivo test on a porcine stomach (Image courtesy of Harvard University)Soft robots are so promising for surgical applications because they can match the stiffness of the body, meaning they won&rsquo;t accidentally puncture or tear tissue. However, at small scales, soft materials cannot generate enough force to perform surgical tasks.&ldquo;At the millimeter scale, a soft device becomes so soft that it can&rsquo;t damage tissue but it also can&rsquo;t manipulate the tissue in any meaningful way,&rdquo; said Tommaso Ranzani, a postdoctoral fellow at SEAS and the Wyss Institute and coauthor of the paper. &nbsp;&ldquo;That limits the application of soft microsystems for performing therapy. The question is, how can we develop soft robots that are still able to generate the necessary forces without compromising safety.&rdquo;Inspired by biology, the team developed a hybrid model that used a rigid skeleton surrounded by soft materials. The manufacturing method drew on previous work in origami-inspired, pop-up fabrication, developed by Robert Wood, the Charles River Professor of Engineering and Applied Sciences.Wood coauthored the paper and is a Core Faculty Member of the Wyss Institute. &nbsp;Previous pop-up manufacturing techniques &mdash; such as those used with the&nbsp;<a href="http://micro.seas.harvard.edu/research.html#flapping" target="_blank">Robobees</a> &mdash; rely on actuation methods that require high voltages or temperatures to operate, something that wouldn&rsquo;t be safe in a surgical tool directly manipulating biological tissues and organs.So, the team integrated soft actuators into the pop-up system.&nbsp;&ldquo;We found that by integrating soft fluidic microactuators into the rigid pop-up structures, we could create soft pop-up mechanisms that increased the performance of the actuators in terms of the force output and the predictability and controllability of the motion,&rdquo; said Sheila Russo, postdoctoral fellow at SEAS and Wyss and lead author of the paper. &ldquo;The idea behind this technology is basically to obtain the best of both worlds by combining soft robotic technologies with origami-inspired rigid structures. Using this fabrication method, we were able to design a device that can lie flat when the endoscope is navigating to the surgical area, and when the surgeon reaches the area they want to operate on, they can deploy a soft system that can safely and effectively interact with tissue.&rdquo;The soft actuators are powered by water. They are connected to the rigid components with an irreversible chemical bond, without the need of any adhesive. The team demonstrated the integration of simple capacitive sensing that can be used to measure forces applied to the tissue and to give the surgeon a sense of where the arm is and how it&rsquo;s moving. The fabrication method allows for bulk manufacturing, which is important for medical devices, and allows for increased levels of complexity for more sensing or actuation. Furthermore, all materials used are biocompatible. &nbsp;The arm is also equipped with a suction cup &mdash; inspired by octopus tentacles &mdash; to safely interact with tissue. The team tested the device ex vivo, simulating a complicated endoscopic procedure on pig tissue. The arm successfully manipulated the tissue safely.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1569906823020-ArmPigWeb.jpg" style="width: 688px;" class="fr-fic fr-dib"><div class="inline-image">Soft pop-up arm performing tissue counter-traction during an ex-vivo test on a porcine stomach (Image courtesy of Harvard University)</div>&ldquo;The ability to seamlessly integrate gentle yet effective actuation into millimeter-scale deployable mechanisms fits naturally with a host of surgical procedures,&rdquo; said Wood. &ldquo;We are focused on some of the more challenging endoscopic techniques where tool dexterity and sensor feedback are at a premium and can potentially make the difference between success and failure.&rdquo;The researchers demonstrated that the device could be scaled down to 1 millimeter, which would allow it to be used in even tighter endoscopic procedures, such as in lungs or the brain.Next, the researchers hope to test the device&nbsp;in vivo.&ldquo;Our technology paves the way to design and develop smaller, smarter, softer robots for biomedical applications,&rdquo; said Russo.

SEM images of the hybrid soft pop-up actuators. The image has been colored in post processing to differentiate between the soft (in yellow) and the rigid structure (in blue) (Image courtesy of the Wyss Institute at Harvard University)

Bioinspired approach combines pop-up fabrication with soft robotics

Smaller, smarter, softer robotic arm for endoscopic surgery

Compression therapy is a standard form of treatment for patients who suffer from venous ulcers and other conditions in which veins struggle to return blood from the lower extremities. Compression stockings and bandages, wrapped tightly around the affected limb, can help to stimulate blood flow. But there is currently no clear way to gauge whether a bandage is applying an optimal pressure for a given condition.Now engineers at MIT have developed pressure-sensing photonic fibers that they have woven into a typical compression bandage. As the bandage is stretched, the fibers change color. Using a color chart, a caregiver can stretch a bandage until it matches the color for a desired pressure, before, say, wrapping it around a patient&rsquo;s leg.The photonic fibers can then serve as a continuous pressure sensor &mdash; if their color changes, caregivers or patients can use the color chart to determine whether and to what degree the bandage needs loosening or tightening.&ldquo;Getting the pressure right is critical in treating many medical conditions including venous ulcers, which affect several hundred thousand patients in the U.S. each year,&rdquo; says Mathias Kolle, assistant professor of mechanical engineering at MIT. &ldquo;These fibers can provide information about the pressure that the bandage exerts. We can design them so that for a specific desired pressure, the fibers reflect an easily distinguished color.&rdquo;Kolle and his colleagues have published their results in the journal Advanced Healthcare Materials. Co-authors from MIT include first author Joseph Sandt, Marie Moudio, and Christian Argenti, along with J. Kenji Clark of the Univeristy of Tokyo, James Hardin of the United States Air Force Research Laboratory, Matthew Carty of Brigham and Women&rsquo;s Hospital-Harvard Medical School, and Jennifer Lewis of Harvard University.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1568994301001-single-fiber-stretch.gif" style="width: 766px;" class="fr-fic fr-dib">Natural inspirationThe color of the photonic fibers arises not from any intrinsic pigmentation, but from their carefully designed structural configuration. Each fiber is about 10 times the diameter of a human hair. The researchers fabricated the fiber from ultrathin layers of transparent rubber materials, which they rolled up to create a jelly-roll-type structure. Each layer within the roll is only a few hundred nanometers thick.In this rolled-up configuration, light reflects off each interface between individual layers. With enough layers of consistent thickness, these reflections interact to strengthen some colors in the visible spectrum, for instance red, while diminishing the brightness of other colors. This makes the fiber appear a certain color, depending on the thickness of the layers within the fiber.&ldquo;Structural color is really neat, because you can get brighter, stronger colors than with inks or dyes just by using particular arrangements of transparent materials,&rdquo; Sandt says. &ldquo;These colors persist as long as the structure is maintained.&rdquo;The fibers&rsquo; design relies upon an optical phenomenon known as &ldquo;interference,&rdquo; in which light, reflected from a periodic stack of thin, transparent layers, can produce vibrant colors that depend on the stack&rsquo;s geometric parameters and material composition. Optical interference is what produces colorful swirls in oily puddles and soap bubbles. It&rsquo;s also what gives peacocks and butterflies their dazzling, shifting shades, as their feathers and wings are made from similarly periodic structures.&ldquo;My interest has always been in taking interesting structural elements that lie at the origin of nature&rsquo;s most dazzling light manipulation strategies, to try recreating and employing them in useful applications,&rdquo; Kolle says.<img src="blob:https://www.wevolver.com/6c22c5d1-56bb-4ed0-a5ce-9b2933c079ca" style="width: 766px;" class="fr-fic fr-dib">A multilayered approachThe team&rsquo;s approach combines known optical design concepts with soft materials, to create dynamic photonic materials.While a postdoc at Harvard in the group of Professor Joanna Aizenberg, Kolle was inspired by the work of Pete Vukusic, professor of biophotonics at the University of Exeter in the U.K., on&nbsp;Margaritaria nobilis,&nbsp;a tropical plant that produces extremely shiny blue berries. The fruits&rsquo; skin is made up of cells with a periodic cellulose structure, through which light can reflect to give the fruit its signature metallic blue color.Together, Kolle and Vukusic sought ways to translate the fruit&rsquo;s photonic architecture into a useful synthetic material. Ultimately, they fashioned multilayered fibers from stretchable materials, and assumed that stretching the fibers would change the individual layers&rsquo; thicknesses, enabling them to tune the fibers&rsquo; color. The results of these first efforts were published in&nbsp;Advanced Materials in 2013.When Kolle joined the MIT faculty in the same year, he and his group, including Sandt, improved on the photonic fiber&rsquo;s design and fabrication. In their current form, the fibers are made from layers of commonly used and widely available transparent rubbers, wrapped around highly stretchable fiber cores. Sandt fabricated each layer using spin-coating, a technique in which a rubber, dissolved into solution, is poured onto a spinning wheel. Excess material is flung off the wheel, leaving a thin, uniform coating, the thickness of which can be determined by the wheel&rsquo;s speed.For fiber fabrication, Sandt formed these two layers on top of a water-soluble film on a silicon wafer. He then submerged the wafer, with all three layers, in water to dissolve the water-soluble layer, leaving the two rubbery layers floating on the water&rsquo;s surface. Finally, he carefully rolled the two transparent layers around a black rubber fiber, to produce the final colorful photonic fiber.&shy;&shy;Reflecting pressureThe team can tune the thickness of the fibers&rsquo; layers to produce any desired color tuning, using standard optical modeling approaches customized for their fiber design.&ldquo;If you want a fiber to go from yellow to green, or blue, we can say, &lsquo;This is how we have to lay out the fiber to give us this kind of [color] trajectory,&rsquo;&rdquo; Kolle says. &ldquo;This is powerful because you might want to have something that reflects red to show a dangerously high strain, or green for &lsquo;ok.&rsquo; We have that capacity.&rdquo;The team fabricated color-changing fibers with a tailored, strain-dependent color variation using the theoretical model, and then stitched them along the length of a conventional compression bandage, which they previously characterized to determine the pressure that the bandage generates when it&rsquo;s stretched by a certain amount.The team used the relationship between bandage stretch and pressure, and the correlation between fiber color and strain, to draw up a color chart, matching a fiber&rsquo;s color (produced by a certain amount of stretching) to the pressure that is generated by the bandage.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1568995401582-MIT-Bandage-Pressure-02.jpg" style="width: 766px;" class="fr-fic fr-dib">As the bandage is stretched, the fibers change color. Courtesy of the researchersTo test the bandage&rsquo;s effectiveness, Sandt and Moudio enlisted over a dozen student volunteers, who worked in pairs to apply three different compression bandages to each other&rsquo;s legs: a plain bandage, a bandage threaded with photonic fibers, and a commercially-available bandage printed with rectangular patterns. This bandage is designed so that when it is applying an optimal pressure, users should see that the rectangles become squares.Overall, the bandage woven with photonic fibers gave the clearest pressure feedback. Students were able to interpret the color of the fibers, and based on the color chart, apply a corresponding optimal pressure more accurately than either of the other bandages.The researchers are now looking for ways to scale up the fiber fabrication process. Currently, they are able to make fibers that are several inches long. Ideally, they would like to produce meters or even kilometers of such fibers at a time.&ldquo;Currently, the fibers are costly, mostly because of the labor that goes into making them,&rdquo; Kolle says. &ldquo;The materials themselves are not worth much. If we could reel out kilometers of these fibers with relatively little work, then they would be dirt cheap.&rdquo;Then, such fibers could be threaded into bandages, along with textiles such as athletic apparel and shoes as color indicators for, say, muscle strain during workouts. Kolle envisions that they may also be used as remotely readable strain gauges for infrastructure and machinery.&ldquo;Of course, they could also be a scientific tool that could be used in a broader context, which we want to explore,&rdquo; Kolle says.

Engineers at MIT have developed pressure-sensing photonic fibers that they have woven into a typical compression bandage  Courtesy of the researchers

Bandage is threaded with photonic fibers that change color to signal pressure level.

Engineers design color-changing compression bandage

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 <a href="https://www.wevolver.com/article/understanding-nvidia-cuda-cores-a-comprehensive-guide">CUDA core</a> 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" data-lf-fd-inspected-kn9eq4roawkarlvp="true">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.