This article provides a detailed understanding of microprocessor vs integrated circuit, their basics, key differences, future trends, challenges, and how they synergize to form the backbone of our digital systems.

Microprocessor vs Integrated Circuit: Unveiling the Core of Modern Technology

<h2 dir="ltr" id="isPasted">Introduction</h2>Embedded systems are integral to our daily lives, found in everything from home appliances to industrial machines. The heart of any such system is its microcontroller (MCU). This tiny yet important component is more than just a piece of hardware; it is the decision-maker that drives the functionality, efficiency, and success of the entire project. Choosing the right MCU is not a mere technical decision; it's a strategic one that significantly impacts the overall system performance, power consumption, and functionality of your embedded design. A well-chosen MCU ensures that your system runs reliably and efficiently, handling tasks with the necessary speed and consuming power with careful consideration. Conversely, a poorly selected MCU can lead to underperformance, high energy consumption, and increased costs, potentially compromising the project's success. Therefore, understanding the main benefits of MCUs and aligning them with your project's needs is an important part of your project in the field of embedded systems engineering.<h2>Understanding Project Requirements&nbsp;</h2>Selecting the right MCU for an embedded project is an important decision relying on the project's specific requirements, as the chosen MCU directly influences the functionality, efficiency, and scalability of the project. This process begins with a thorough analysis of the project's goals and technical needs. Firstly, it’s important to analyze the project's functional requirements, which includes understanding the tasks the MCU needs to perform, such as data processing, signal control, or communication with other devices.&nbsp;The processing power of the MCU determines how quickly and efficiently it can execute tasks, important for applications that require real-time processing or handling complex algorithms. It is essential to assess the processing needs of your project, whether it involves simple control tasks or more demanding computational work. A more powerful MCU might be necessary for intensive tasks, but it could also mean higher costs and energy consumption.Memory is another critical factor. The MCU's memory is split into two main types: flash (for storing your program) and RAM (for temporary data storage during operation). The size of your code and the data it needs to handle will dictate the memory requirements. Insufficient memory can lead to performance issues or limit the scope of your project, so it’s important to estimate memory needs accurately.Furthermore, the nature and number of I/O interfaces determine how the MCU will interact with other components like sensors, actuators, and user interfaces. Count the number and type of I/O pins you need and ensure that the MCU can accommodate them. Additionally, consider the need for specialized functions, such as analog-to-digital converters (ADCs) or pulse-width modulation (PWM) outputs.But also, many embedded projects require MCUs to communicate with other devices. This could be through standard protocols like UART, SPI, or I2C for onboard communication, or more advanced interfaces like Ethernet or Wi-Fi for network connectivity. The choice of MCU should align with these communication needs, ensuring it supports the required protocols without necessitating extensive additional hardware.<h2>Microcontroller Architectures</h2>In order to choose the right MCU it is important to understand the various MCU architectures, like AVR, ARM, and PIC, each having unique properties and influence differently like performance, power efficiency, and development flexibility.Firstly, AVR architecture, widely recognized through the Arduino platform, stands out for its user-friendly approach. Ideal for beginners and educational purposes, AVR chips are known for their simplicity in programming and debugging. They offer a decent balance of power efficiency and processing capability, making them well-suited for moderate-level applications like hobbyist projects or basic automation systems.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5ODE0NDk4OTIwLVBST0dSQU0gUk9NLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">ARM architecture, on the other hand, is known for its high-performance capabilities and remarkable energy efficiency. ARM-based MCUs are versatile, scaling from lower-end Cortex-M series to the more powerful Cortex-R and Cortex-A series, making them applicable for a broad range of applications, from simple devices to complex, compute-intensive applications like smart home systems, advanced robotics, and IoT devices.PIC architecture is favored for its adaptability and low power consumption, comes in a variety of options, accommodating a wide range of needs from basic to complex. PIC chips are particularly noted for their robustness and reliability, making them a preferred choice in industrial applications and consumer electronics.Selecting the appropriate architecture requires a careful evaluation of your project's needs. If your project demands high processing power and energy efficiency, ARM-based MCUs are typically the go-to choose. For projects that prioritize ease of use and cost-effectiveness, especially for those new to embedded systems, AVR might be more appropriate. Meanwhile, for applications that require a balance between performance, power efficiency, and versatility, PIC MCUs are often a reliable choice.<h2 dir="ltr">Power Consumption Considerations</h2>The energy efficiency of a MCU can greatly influence the longevity and reliability of a system, affecting everything from user experience to maintenance costs. In battery-operated devices, such as portable medical devices, wearables, or remote sensors, minimizing power consumption is important. Especially when high power usage leads to frequent battery replacements or recharges, which can be impractical or costly. Additionally, in applications where it is difficult to access the device for maintenance, such as embedded sensors in smart infrastructure, efficient power usage becomes even more important for ensuring long-term operation.Modern MCUs are equipped with various low-power modes and energy-saving features. These include sleep or idle modes, where the MCU slows down or turns off certain functions when they are not needed. For instance, a MCU might power down its CPU while keeping its memory active, allowing it to quickly resume full operation when required. Other features include efficient clock management and power gating, which selectively shuts down unneeded peripherals.Achieving the right balance between processing power and energy efficiency is a challenging aspect of MCU selection. Higher processing power generally leads to higher power consumption, but this is not always a linear relationship. Advanced MCUs are designed to provide optimum performance with minimal energy use. When selecting a MCU, consider the processing demands of your application and compare them against the power specifications of the MCU. Often, it’s possible to find a MCU that offers sufficient processing capabilities while still maintaining a low energy footprint.<h2 dir="ltr">Development Ecosystem and Tools</h2>The choice of a MCU is greatly influenced by the availability and quality of its development ecosystem. This ecosystem encompasses the software tools, libraries, community support, and documentation available for a particular MCU. A strong development ecosystem simplifies every stage of the project lifecycle, from prototyping to deployment. It provides developers with vital resources like compilers, integrated development environments (IDEs), and code libraries. Additionally, a community support network can be an important benefit, offering solutions to common problems, advice on best practices, and shared experiences from fellow developers.A well-supported ecosystem can significantly streamline the development process. Access to comprehensive libraries and frameworks allows developers to avoid misleading steps for common tasks and functionalities. Moreover, good documentation and active community forums can speed up the troubleshooting process, reducing development time.Development tools are important especially in simplifying debugging and testing. Advanced IDEs offer features like code completion, error highlighting, and sophisticated debugging tools that can detect and help resolve issues quickly. These tools are invaluable in ensuring the reliability and performance of the final product. Moreover, some ecosystems provide simulation tools allowing developers to test their code in a virtual environment before deploying it on actual hardware, further easing the development process.<h2>Costs</h2>The price of a microcontroller can significantly impact the overall budget of a project, especially in large-scale productions or cost-sensitive applications. The cost of a microcontroller is not limited to its purchase price alone. It encompasses the long-term expenses associated with development, maintenance, and potential scalability needs. In projects where large quantities of MCUs are required, even a small difference in unit cost can substantially affect the total expenditure. Therefore, it is important to choose wisely and select a MCU that fits within the budget while meeting the project requirements.<h2>Practical Examples&nbsp;</h2>In this section, two practical examples will be shortly observed. In the first one, application of MCUs for home automation systems is of interest. This automation project has requirements to have low power consumption, Wi-Fi connectivity, and the ability to handle multiple sensor inputs. An ESP32 microcontroller was chosen for its built-in Wi-Fi capabilities, efficient power management, and ample GPIO pins for sensor connectivity. The result was a cost-effective, energy-efficient system capable of seamless integration into a smart home environment.Another example is installation of MCUs as an industrial monitoring device, where a robust MCU for monitoring equipment is required. The critical factors were reliability under harsh conditions, extended temperature range, and real-time processing. A microcontroller from the STM32 family was selected for its ruggedness, wide temperature tolerance, and high processing power. This choice ensured reliable and accurate monitoring under industrial conditions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA5Nzk0ODM2OTA1LW1vdXNlciBtY3UuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><h2 dir="ltr" id="isPasted">Conclusion&nbsp;</h2>The selection of the right microcontroller extends far beyond technical specifications, it also involves a thoughtful and customized approach that aligns with the specific needs and constraints of the project. A microcontroller is not just a component; it is the driving force behind the functionality, efficiency, and scalability of an embedded system. The diverse range of architectures, from AVR to ARM to PIC, offers a spectrum of options catering to different project needs. By understanding these various elements and applying this knowledge carefully, engineers and developers can make informed decisions that lead to efficient, reliable, and successful embedded systems.

Selecting the right microcontroller (MCU) is crucial in embedded systems engineering, as it significantly influences the system's performance, power consumption, and overall success.

Choosing the Right Microcontroller for Your Embedded Project

Wearable electronic devices designed to be worn while in use are becoming more popular and powerful as they can monitor various aspects of human health, activity, and environment. However, these devices also face challenges such as limited battery life, data privacy, and network bandwidth. To overcome these challenges, edge computing and AI/ML are emerging as key technologies that can enable wearable devices to process data locally, reduce latency, and enhance user experience.&nbsp;As wearable technologies evolve to meet market needs, new devices are upgrading features such as basic BLE connectivity by adding the ability to run AI algorithms on neural processors on-device rather than in the cloud. This reduces the amount of data that needs to be transmitted over the network, saving energy and bandwidth. With the AI/ML models running in silicon, wearable devices can, therefore, learn from data and perform tasks such as recognition, classification, prediction, and optimization without incurring additional latency, data costs, and power consumption. However, these improvements require greater processing power and capabilities from embedded microcontrollers (MCUs) and application processors. At the same time, consumers expect longer battery life from their wearables. Thus, new chips must balance connectivity and higher processing power with low power consumption while maintaining a small enough package size.&nbsp;This case study explores the challenges for the next generations of wearable MCUs with a specific look at <a href="https://alifsemi.com/" target="_blank" rel="noopener noreferrer">Alif Semiconductor's®</a> Balletto™ family of BLE-enabled MCUs for wearable applications.<h2 dir="ltr">The current landscape</h2>The wearables market, which includes common accessories such as smart bands, smart rings, smartwatches, and medical devices, such as glucose monitoring devices is projected to grow to USD 118.16 billion by 2028 at a 13.8% CAGR. Demand for multimedia devices, smartphones, fitness trackers, and health wearables drives growth. Increasing health awareness fuels interest in fitness tracking devices for activities like cycling and running, contributing to market expansion.[2,3]Wearables have evolved significantly in recent years, presenting enhanced capabilities in various domains, such as AI processing and wirelessly transmitting data. New market demands have driven the expansion of wearable features. For example, the COVID-19 pandemic made blood oxygen sensing a must-have feature for many smartwatches. As a result, Apple and Samsung included blood oxygen monitoring in their smartwatches in 2020.Wearables incorporate edge AI-based solutions to process huge amounts of data in real-time. AI-enabled microcontrollers are a must to carry out these large AI/ML workloads efficiently and quickly. The integration of artificial intelligence enables new features, such as:<ul><li dir="ltr">Tracking and analyzing changes in users’ health data.</li><li dir="ltr">Sending users alerts for noise levels or air quality.</li><li dir="ltr">Personalize your device based on your tracked activity.&nbsp;</li></ul>Equally important as collecting and processing user data is the ability to wirelessly share data between devices. As such, Bluetooth connectivity has risen as a top requirement for wearables including functionalities, such as:&nbsp;<ul><li dir="ltr">Sending an alert to emergency services if a fall is detected.</li><li dir="ltr">Sharing important health updates with an approved health care professional.</li><li dir="ltr">Sharing workout metrics with a friend to stay motivated.</li></ul><h2 dir="ltr">Bluetooth LE changes the status quo in wearables</h2>Among the many low-power wireless technologies on the market, Bluetooth Low Energy (BLE) technology is uniquely suited for wearable devices that require long battery life measured in days or even weeks before needing to be recharged. BLE allows wearables to easily connect and communicate with other devices, such as smartphones, tablets, exercise equipment, or even other wearables.&nbsp;This enhances the device's functionality by enabling it to share exercise data, receive timely notifications, and more. Increasingly, wearable devices are adding audio support for which BLE is uniquely positioned. With the addition of support for the Low Complexity Communication Codec (LC3), BLE is now capable of use cases such as broadcast audio and shared or group listening experiences with LE Audio’s Auracast feature. For example, a wearable device can use the on-device neural processor such as Arm’s Ethos-U55 to run a convolutional neural network (CNN) model for gesture recognition or activity classification based on the motion data captured by inertial measurement units (IMUs). The device can then use BLE to communicate with other devices or a smartphone app to provide feedback or instructions to the user.&nbsp;In the Smart Home, wearables can be integrated into a smart home system, with AI algorithms learning the user's habits and preferences to automate various home appliances. BLE 5.4's enhanced performance and range can facilitate efficient communication with many IoT devices in the home. For example, a wearable could learn that a user likes to listen to calming music when they get home from work and automatically instruct the smart speaker to play the user's favorite playlist when they walk through the door.&nbsp;Additionally, features allow wearables to be located precisely, while location-based services (LBS) such as personalized fitness coaching (created by learning from a user’s previous workout data) can be enabled. It can suggest workout routines tailored to the user's fitness goals and current health status. The wearable device can recognize the user’s activity and posture using an ML model that runs on the Ethos-U55 core and sends the data to a cloud service via Enhanced Attribute Protocol (EAP). This could enable personalized, reliable, and secure fitness tracking for the user.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMTY0MTY0MDQ1LVdob29wLUJvZHlUcmFpbmluZy1Ub3AuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Next-gen wearables will enable highly-personal fitness tracking. Credit: WHOOP.&nbsp;AI/ML algorithms can be trained to detect patterns in biometric data gathered by wearables, such as heart rate, blood pressure, and oxygen saturation levels. BLE's enhanced broadcast communication mechanism (EAD, PAwR) can allow these devices to send real-time health risk alerts to other devices, such as a smartphone, even in congested or high-interference environments. For example, a wearable could alert a user or a healthcare provider if it detects signs of an impending health crisis like a heart attack or stroke.&nbsp;When you combine AI-enabled MCUs with BLE, wearables become more personalized, efficient, secure, and useful in our everyday lives. Alif Semiconductor <a href="https://alifsemi.com/press-release/first-ble-matter-wireless-microcontroller-with-neural-co-processor-for-ai-ml/" target="_blank">has just announced</a> their Balletto family of BLE and Matter microcontrollers&nbsp;with neural processing for AI/ML workloads, which seeks to exceed the standard for low-power BLE operations.<h2 dir="ltr">Hardware accelerated machine learning</h2>To meet the market-need for state-of-the-art wearables, Alif Semiconductor offers AI/ML-enabled microcontrollers with dedicated NPU accelerators that allow heavy AI/ML workloads to run without compromising battery life. <a href="https://alifsemi.com/products/balletto/" target="_blank" id="isPasted">The Alif Balletto family</a> of microcontrollers introduces the world's most power-efficient microcontroller with on-device secure AI/ML processing, top-notch security, and ample peripherals for applications requiring integrated Bluetooth Low Energy and 802.15.4-based connectivity.&nbsp;The Balletto family boasts the powerful Arm® Cortex®-M55 core, equipped with double-precision FPU and Helium Vector Extensions, operating at a remarkable speed of up to 160MHz. Arm Ethos-U55 is a dedicated NPU accelerator that offloads AI/ML operations from the Cortex-M55 processor, allowing the M55 cores to operate in very low-power modes. This is especially important for wearables like hearing aids, which need to have a long battery life. Equally as important for wearables is having a small form-factor design, which Balletto can provide in a sub 16mm ² package. &nbsp;&nbsp;Balletto’s Ethos™-U55 Neural Processing Unit (NPU) can improve the performance and efficiency of speech recognition machine learning models by up to two orders of magnitude, excluding the 5-10x improvement already provided by the Cortex-M55 core over previous generation Cortex-M CPUs. This is ideal for wearables that rely on voice commands, improving processing speed and lowering latency.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyODQyNDU1ODU5LWFsaWYgbWFpbiAyLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Balletto and Ensemble families. Image credit: Alif Semiconductor.<h3 dir="ltr" id="isPasted">Battery-friendly microcontrollers powered by Alif’s aiPM™ technology&nbsp;</h3>The Balletto family also tackles a common problem seen by previously existing AI/ML-powered microcontrollers: short battery life. Connected wearables or mission-critical devices like hearing aids depend on long battery life for users. Their ability to function is hindered when there is a need for heavy local processing and AI/ML. Balletto MCUs are the most power-efficient in their class, thanks to Alif's innovative aiPM technology. This technology manages all aspects of the device, including individual processing cores for real-time control, neural processing, DSP and audio, deep security, and radio protocols capable of BLE 5.4 with LE Audio, Thread, and Matter. Balletto devices also include a wide range of interfaces and large memories, all monolithically integrated inside extremely small packages. This makes them ideal for demanding battery-powered applications such as wearables and hearables.Alif Semiconductor’s aiPM technology manages the power usage of the device through 8 power domains. It activates only the necessary chip sections and deactivates them when not in use, resulting in a highly efficient always-on region when there is a high requirement for local processing and AI/ML. The integrated dual-PA and on-chip antenna gives customers control of the reliability-range equation by providing tuning options of 4 dBm or up to 10 dBm Bluetooth transmit power. &nbsp;Balletto’s Bluetooth radio has a receive current of 1.5mA and a transmit current of 2.0mA, making it power conscious.Additionally, off-die peripherals produce higher energy consumption for wearables, while Balletto’s on-die solution fosters maximum efficiency. Utilization of the internal bus fabric effectively lowers access time, dynamic power, and standby power, ultimately providing more granular and precise power control at a lower cost.&nbsp;<h3 dir="ltr">Balletto family multi-layered security</h3>In addition to handling processing requirements, microcontrollers for wearables must ensure data security. Alif Balletto microcontrollers provide exceptional security features with multiple layers of protection, including:<ul><li dir="ltr">Root of Trust</li><li dir="ltr">Unique device ID for each individual chip</li><li dir="ltr">Dedicated security processor</li><li dir="ltr">Dedicated protected memory</li><li dir="ltr">Configurable firewalls that regulate access of each CPU to sections of memories and individual peripherals and extend the capabilities of the standard Arm Trust Zone security partitioning</li></ul>Alif Semiconductor is the only company in this market segment that injects at manufacturing, a unique public key infrastructure (PKI) key pair to support immutable&nbsp;hardware root of trust (RoT) from secure boot to strong authentication and remote software attestation capabilities.As a result, the personal data acquired by sensors and processed by the Alif Balletto microcontrollers are secured against third-party attacks.Users can securely manage all aspects of the device lifecycle, including key management, certificate management, remote updates, and more. Legacy MCUs may stop at TrustZone, but Alif takes you beyond TrustZone to ensure that multi-core devices are protected by not allowing two CPUs to access the same Secure domain and share secrets.&nbsp;<h3 dir="ltr">Graphics and imaging</h3>The Balletto family includes a number of hardware features to help offload traditionally CPU-intensive workloads from the MCU/MPU cores to improve performance and keep current consumption in check. One such feature is an integrated 2D graphics accelerator that can help speed up drawing operations by 4-10x compared to an unaccelerated system. This, combined with a set of imaging and display interfaces, which include parallel as well as MIPI-CSI2 and MIPI-DSI interfaces for 24-bit RGB displays and 8 to 16-bit cameras, provide everything needed for responsive and buttery-smooth graphics on pixel-dense screens.<h2 dir="ltr">Conclusion</h2>As the wearable technology market continues to expand, the convergence of edge AI and BLE technology has enabled wearables to become smarter, more efficient, and more secure. Alif Semiconductor's Balletto family of MCUs represents a significant leap forward in meeting the ever-growing demands of the wearables market. These innovative solutions empower wearables to not only collect and process data but also to provide real-time, personalized, and secure experiences, all while maintaining long-lasting battery life and optimizing form factor. The future of wearables is here, and it's exciting.<h3 dir="ltr">References</h3><ol><li dir="ltr">GlobeNewswire. <a href="https://www.globenewswire.com/en/news-release/2023/03/15/2627702/0/en/2023-2028-Smart-Wearables-Market-Size-Share-and-Trends-Analysis-Report-By-Y-O-Y.html" target="_blank">2023- 2028 Smart Wearables Market Size, Share and Trends Analysis Report By Y-O-Y</a>.</li><li dir="ltr">businesswire. <a href="https://www.businesswire.com/news/home/20220104005806/en/Global-Wearable-Technology-Market-Trends-Analysis-Report-2021-2028-Adoption-of-Fitness-Trackers-and-Health-based-Wearables-is-Anticipated-to-Propel-Growth---ResearchAndMarkets.com" target="_blank">Global Wearable Technology Market Trends &amp; Analysis Report 2021-2028: Adoption of Fitness Trackers and Health-based Wearables is Anticipated to Propel Growth - ResearchAndMarkets.com.</a></li></ol><h3>Further Research</h3>Ramesh Nair, 2023. “<a href="https://www.safetymint.com/blog/smart-wearables/" target="_blank">Smart Wearables: Advancing Safety in the Workplace</a>”StartUs Insights, 2023. “<a href="https://www.startus-insights.com/innovators-guide/wearables-trends/" target="_blank">Top 10 Wearables Trends in 2023</a>”GlobalData, 2022. “<a href="https://www.medicaldevice-network.com/comment/wearable-technology-iot/" target="_blank">Wearable technology market continues to rise</a>”Lisa Eadicicco, 2022. “<a href="https://www.cnet.com/tech/mobile/smartwatches-have-measured-blood-oxygen-for-years-but-is-it-useful/" target="_blank">Smartwatches Have Measured Blood Oxygen for Years. But Is This Useful?</a>”Fi Forest, 2022. “<a href="https://www.pharmaceutical-technology.com/sponsored/wearables-managing-complexities-data-privacy-consent/" target="_blank">Wearables: managing complexities in data privacy and consent in healthcare tech</a>”Industry News, 2022. “<a href="https://www.helpnetsecurity.com/2022/11/16/alif-semiconductor-telit/" target="_blank">Alif Semiconductor partners with Telit to develop and deploy IoT edge devices</a>”Alif Semiconductor, 2023. “<a href="https://alifsemi.com/ensemble/" target="_blank">Supplying next generation scalable microcontrollers</a>”Arrow, 2022. “<a href="https://www.arrow.com/en/research-and-events/articles/alif-semiconductor" target="_blank">Alif Semiconductor</a>”Eduardo Montanez, 2022. “<a href="https://www.nxp.com/company/blog/nxp-i-mx-rt-mcu-technology-powers-our-smartwatch-future:BL-NXP-IMX-RT-MCU-TECHNOLOGY" target="_blank">NXP i.MX RT MCU Technology Powers Our Smartwatch Future</a>”

Learn how bluetooth connected AI/ML microcontrollers are setting the bar for next generation wearables.

Leading Wearables into a New Era with Cutting-Edge Connected Bluetooth LE based MCUs

Microcontrollers and microprocessors are fundamental components in electronics and computing. Both play crucial roles in operating various devices, from smartphones to embedded systems in automobiles and home appliances. This article studies their differences in architecture, performance, and more.

Microcontroller vs Microprocessor: A Comprehensive Guide to Their Differences and Applications

<h1 dir="ltr" id="isPasted">Introduction</h1>In the intricate tapestry of modern technology, where devices are becoming increasingly interconnected and intelligent, the ability to accurately interpret the world around us is paramount. This is where the concept of 'sensor fusion' shines brightly, helping to make sense of vast quantities of data. Sensor fusion, in its essence, is the art and science of amalgamating data from multiple sensors to provide a more holistic, accurate, and reliable view of the environment. It's akin to piecing together a jigsaw puzzle, where each sensor provides a fragment of the bigger picture, and when fused together, these fragments reveal a comprehensive image.As we navigate through this article, we will delve deep into the technical intricacies and methodologies underpinning sensor fusion. We'll explore its applications, from autonomous vehicles to healthcare, and highlight the challenges that developers and engineers face in this domain. Furthermore, we'll introduce the transformative potential of integrating sensor fusion with cutting-edge tools like the SensiEDGE CommonSense addon for Sony Spresense and the paradigm of Edge AI. By the end, readers will gain a comprehensive understanding of the current landscape of sensor fusion and the promising capability it brings.<iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" 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> <h1 dir="ltr">Understanding Sensor Fusion</h1>Sensor fusion is a multidisciplinary field that integrates data from multiple sensors to derive a more comprehensive and accurate representation of the environment than any individual sensor can provide. Central to this is the principle of Redundancy and Diversity.&nbsp;Redundant sensors, which are multiple units measuring the same parameter, ensure that the system remains resilient against individual sensor failures or inaccuracies. On the other hand, diverse sensors, each capturing different facets of the environment, ensure a comprehensive view. This dual approach not only bolsters the reliability of measurements but also provides a panoramic perspective of the surroundings.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0NzkzMTkxLWltYWdlNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensor-fusion-aircrafts">Fig. 1: The idea of combining data from multiple sensors to improve decision-making, accuracy, and reliability is not new. Historically, sectors like defense and aerospace were among the pioneers in harnessing the power of sensor fusion for tasks such as navigation, target tracking, and situational awareness.<h2 dir="ltr" id="isPasted">Methodologies Driving Fusion</h2>Two methodologies particularly stand out in the realm of sensor fusion are Kalman Filtering and Particle Filtering. They are state estimation techniques that are used to combine noisy measurements from multiple sensors to estimate the true state of a system. The Kalman filter, a recursive algorithm, is tailored for linear dynamic systems. It shines in environments with Gaussian distributed noise, predicting future states based on past measurements. Conversely, Particle filters are the go-to for nonlinear systems. They operate by representing a system's posterior distribution using a set of random samples, offering a more flexible approach to state estimation.<h2 dir="ltr">Challenges in Real-world Implementation</h2>While the promise of sensor fusion is undeniable, it's not without its challenges. Temporal Alignment is a significant hurdle. With sensors operating at different sampling rates, ensuring that data is synchronized is crucial for accurate fusion. Another challenge lies in Spatial Calibration. Given that sensors might have varied orientations or fields of view, aligning them to a common spatial frame is essential.&nbsp;Furthermore, as the number of sensors in a system grows, scalability becomes a concern, demanding efficient algorithms that can handle increased data loads without compromising on processing speed.<h2 dir="ltr">Evaluating Fusion Systems</h2>Lastly, the efficacy of a sensor fusion system is often determined by a set of quality metrics. Accuracy, which measures how close the fused data is to the actual state of the environment, is paramount. However, robustness, the system's resilience against anomalies, and computational efficiency, indicating the system's resource utilization, are equally vital.Further reading:&nbsp;<a href="https://www.wevolver.com/article/what-is-sensor-fusion-everything-you-need-to-know" target="_blank">Sensor Fusion: The Ultimate Guide to Combining Data for Enhanced Perception and Decision-Making</a><h1 dir="ltr">Applications of Sensor Fusion</h1>Sensor Fusion facilitates a variety of applications across various domains by amalgamating data from diverse sensors to derive precise and actionable insights. Here are some of the applications where sensor fusion shines:<ul><li dir="ltr">Autonomous Vehicles:&nbsp;One of the most well-known applications of sensor fusion is in self-driving cars. These vehicles use a combination of cameras, LiDAR, radar, ultrasonic sensors, and more to perceive their surroundings, detect obstacles, and navigate safely. By fusing data from these diverse sensors, autonomous vehicles can operate safely in a wide range of conditions, from bright sunlight to nighttime and from clear weather to fog.</li><li dir="ltr">Robotics:&nbsp;Robots, especially those designed for complex tasks in variable environments, use sensor fusion to combine data from cameras, infrared sensors, tactile sensors, and gyroscopes. This allows them to navigate, interact with objects, and perform tasks with higher precision and reliability.</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0ODM3OTc4LWltYWdlNy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensor-fusion-wearable">Fig. 2: Sensor fusion is also used in wearable technology, enhancing the capabilities of devices to provide comprehensive insights and improved user experiences.<ul><li dir="ltr">Augmented Reality (AR) and Virtual Reality (VR):&nbsp;AR and VR systems often rely on sensor fusion to track user movements and adjust the displayed visuals accordingly. By combining data from accelerometers, gyroscopes, and cameras, these systems can provide a more immersive and responsive user experience.</li><li dir="ltr">Military and Defense:&nbsp;Advanced defense systems use sensor fusion to combine data from radars, sonars, infrared cameras, and other sensors. This provides a comprehensive situational awareness, enabling quicker and more informed decision-making in complex scenarios.</li><li dir="ltr">Healthcare:&nbsp;Wearable devices that monitor health metrics often use sensor fusion to provide more accurate readings. For instance, a smartwatch might combine data from a heart rate sensor, accelerometer, and gyroscope to better understand a user's activity level and overall health.</li></ul>Other notable applications include environmental monitoring, where sensor fusion aids in forest fire detection and prediction; smart cities, which utilize the technique for traffic management and public safety; agriculture, where precision farming is enhanced through fused data; industrial automation for monitoring equipment health and product quality; and navigation and mapping, where devices like smartphones and drones benefit from improved location accuracy.<h1 dir="ltr">Sensor Fusion with CommonSense addon for Sony Spresense Microcontroller and Edge Impulse</h1>In the rapidly evolving world of IoT and edge computing, the synergy between robust hardware and intelligent software becomes crucial. The <a href="https://www.sensiedge.com/commonsense" target="_blank">SensiEDGE CommonSense</a> add-on, tailored for the <a href="https://developer.sony.com/spresense/" target="_blank">Sony Spresense</a> board, epitomizes this synergy, setting new standards in sensor fusion.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0ODY5OTExLWltYWdlNC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="commonsense-addon-module-for-spreesense">Fig. 3: CommonSense module mounted over a Sony Spresense board. Source:&nbsp;<a href="https://edgeimpulse.com/blog/access-augmented-sensor-capabilities-for-sony-spresense-with-our-new-commonsense-integration" target="_blank" rel="noopener noreferrer">Introducing CommonSense Integration - Edge Impulse</a>The CommonSense add-on is a testament to the advancements in sensor technology within the IoT landscape. Despite its compact and unassuming design, it boasts a plethora of capabilities. Central to its operation is an array of 10 unique sensors that allow the add-on to monitor, measure, and perceive the environment when attached to a Sony Spresense development board.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTI4MzkxLWltYWdlMy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensiedge-features">Fig. 4: Infographic showcasing the features of SensiEDGE CommonSense boardIt's adept at capturing a myriad of environmental parameters, from air quality and temperature to humidity. Additionally, its motion tracking capabilities, encompassing an accelerometer, gyroscope, and magnetometer, allow for a broad spectrum of data collection. Augmented by features like audio detection through its microphone and versatile connectivity options via Bluetooth and Wi-Fi, it emerges as an invaluable asset for developers aspiring to craft comprehensive IoT solutions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTUzNDQ4LWltYWdlNS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="sensiedge-commonsense-applications">Fig. 5: &nbsp;CommonSense add-on by SensiEDGE is a compact yet powerful module designed for diverse applicationsThe power of the CommonSense add-on is further magnified when its data is fused, offering a holistic view of the monitored environment or subject. Such consolidated data, when interpreted through machine learning models, paves the way for precise predictions and actionable insights.&nbsp;This is where <a href="https://edgeimpulse.com/" target="_blank">Edge Impulse</a>, a platform renowned for its expertise in embedded machine learning, comes into play. The amalgamation of the CommonSense add-on and Sony's Spresense board with Edge Impulse's software expertise results in a blend of sensor fusion and edge AI. This empowers developers to design, refine, and implement machine learning models right on the device, leveraging AI with no reliance on cloud connectivity.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI0OTg4Mzg3LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="edge-impulse-stufio-platform">Fig. 6: Edge Impulse is a developer-first integrated platform for designing, training, testing and deploying edge AI applications.For a deeper dive into this integration and its potential, consider exploring this webinar that accentuates the potential of sensor fusion on the Sony Spresense:<h2><iframe width="640" height="360" src="https://www.youtube.com/embed/5HyGcLK0bQM?&amp;ab_channel=EdgeImpulse&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" 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>Conclusion</h2>The integration of SensiEDGE CommonSense add-on with Sony Spresense and Edge Impulse makes for a powerful combination that enhances the reliability, accuracy, and efficiency of data interpretation in various applications, all on the edge.&nbsp;Leveraging advanced hardware and intelligent software promises to drive innovation, offering a more holistic and nuanced understanding of our environment. In essence, this collaboration is set to redefine the boundaries of sensor fusion, paving the way for groundbreaking advancements in the interconnected world of modern technology.<h1 dir="ltr">About the sponsor: Edge Impulse</h1><a href="https://www.edgeimpulse.com/" target="_blank">Edge Impulse</a> is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.&nbsp;From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk3NzI1MTI5ODY4LWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="edge-impulse"><h2 dir="ltr"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" 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><h2 dir="ltr">References</h2>[1] Mitchell, H. (2007). Multi-Sensor Data Fusion. Springer, Berlin, Heidelberg. <a href="https://doi.org/10.1007/978-3-540-71559-7_1" target="_blank">https://doi.org/10.1007/978-3-540-71559-7_1</a>[2] Chui, C.K., Chen, G. (2017). Kalman Filtering. Springer, Cham.&nbsp;<a href="https://doi.org/10.1007/978-3-319-47612-4_1" target="_blank">https://doi.org/10.1007/978-3-319-47612-4_1</a>[3] ElProCus. What is Sensor Fusion: Working &amp; Its Applications. ElProCus. [Online]. Available:&nbsp;<a href="https://www.elprocus.com/sensor-fusion/" target="_blank">https://www.elprocus.com/sensor-fusion/</a>[4] Sony, Spresense 6-core microcontroller board with ultra-low power consumption [Internet]. Available from:&nbsp;<a href="https://developer.sony.com/spresense/" target="_blank">https://developer.sony.com/spresense/</a>[5] SensiEDGE, CommonSense: Sensor AI Board [Internet]. Available from:&nbsp;<a href="https://www.sensiedge.com/commonsense" target="_blank">https://www.sensiedge.com/commonsense</a>

Sensor fusion enables the seamless integration of data from multiple sensors, paving the way for advanced Edge AI implementations that optimize real-time processing, enhance decision-making, and boost system responsiveness in dynamic environments.

Redefining Sensor Fusion with CommonSense addon for Sony Spresense and Edge Impulse

<h1 dir="ltr" id="isPasted">Introduction</h1>Gesture recognition is not just a buzzword; it's an indispensable technology that's changing the way we interact with devices. From smartphones to smart homes, the ability to control devices through simple hand movements offers an unparalleled level of convenience and accessibility. However, implementing robust gesture recognition systems is fraught with challenges, from data acquisition and preprocessing to machine learning model training and deployment.In this article, we will look into the technical aspects of developing a gesture recognition system using Infineon's PSoC 6 microcontroller. We'll explore each stage of the development process, from sensor data acquisition to neural network model deployment. We'll also share a link to a GitHub project detailing a step-by-step guide on implementing gesture classification, empowering you to create your own gesture recognition application. By the end of this article, you'll have a comprehensive understanding of the complexities involved and how specialized machine learning platforms such as Edge Impulse can simplify the process.<iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" 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> <h1 dir="ltr">The Need for Gesture Classification</h1>The demand for intuitive human-machine interfaces is growing exponentially across various sectors. Whether it's automotive infotainment systems, medical devices, or consumer electronics, the ability to interact through gestures adds a layer of convenience and user-friendliness. However, implementing these systems on embedded platforms like the PSoC 6 involves challenges such as computational limitations, real-time processing requirements, and power constraints.To address these challenges, developers often have to make trade-offs between system performance and complexity. For instance, simpler algorithms may require fewer computational resources but may not provide the desired level of accuracy. On the other hand, complex algorithms may offer high accuracy but may be too resource-intensive for real-time applications. This delicate balance makes the choice of hardware and software platforms crucial in the development process. With these considerations in mind, let's delve into the capabilities of the Infineon PSoC 6 and see how it stands out as a viable solution for these challenges.<h1 dir="ltr">Infineon PSoC 6: An Overview</h1>Infineon's PSoC 6 is a versatile microcontroller unit (MCU) designed with low-power IoT applications in mind. It features a dual-CPU architecture, combining an ARM Cortex-M4 and an ARM Cortex-M0+ processor, allowing for efficient task partitioning. The PSoC 6 also offers programmable analog and digital blocks, making it highly customizable for specific application needs. Its rich set of features makes it an ideal candidate for implementing complex tasks like gesture recognition.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0OTU2NjAwMjk1LWltYWdlMy5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="infineon-psoc-6-prototyping-kit">Fig. 1: Infineon Technologies PSoC™ 6 Wi-Fi® BT Prototyping Kit. Source:&nbsp;<a href="https://www.mouser.in/new/infineon/cypress-psoc-6-wifi-bt-proto-kit/" target="_blank" rel="noopener noreferrer">Mouser Electronics</a>The PSoC 6 is not just a powerful MCU; it's a complete ecosystem that provides developers with a range of tools and libraries to accelerate the development process. From ModusToolbox, a comprehensive software development environment, to a rich set of middleware components, the PSoC 6 ecosystem is designed to simplify complex tasks.<h1 dir="ltr">Sensor Data Acquisition</h1>For gesture recognition, sensor data is the raw material. PSoC 6 can interface with a variety of motion sensors, such as accelerometers and gyroscopes, using SPI and UART protocols. These sensors capture the 3D spatial movements that are then classified into specific gestures. However, data acquisition is not just about reading sensor values; it's about doing so reliably and efficiently. Specialized machine learning platforms like Edge Impulse can offer features like sensor fusion and automated data collection, ensuring that the raw data is as accurate as possible.Suggested Reading:&nbsp;<a href="https://www.wevolver.com/article/what-is-sensor-fusion-everything-you-need-to-know" target="_blank">Sensor Fusion: The Ultimate Guide to Combining Data for Enhanced Perception and Decision-Making</a>Data acquisition in real-world applications is often noisy and inconsistent. Factors like sensor drift, environmental conditions, and hardware limitations can introduce errors into the collected data. Therefore, it's essential to implement robust data acquisition strategies that can mitigate these issues. Techniques like sensor calibration, data validation, and outlier detection are often employed to improve the quality of the collected data.<h1 dir="ltr">Data Preprocessing and Filtering</h1>Once the raw sensor data is acquired, it needs to be preprocessed before feeding it into a machine learning model. This involves filtering out noise and normalizing the data. Techniques like Infinite Impulse Response (IIR) filtering and min-max normalization are commonly employed for this purpose. While these preprocessing steps are essential for the accuracy of the final model, they can be time-consuming to implement and optimize. Specialized machine learning platforms can automate much of this, allowing developers to focus on other critical aspects of the system.Data preprocessing is not a one-size-fits-all operation; it often requires fine-tuning based on the specific application requirements. For instance, the choice of filter parameters, the window size for normalization, and the sampling rate can all impact the performance of the gesture recognition system. Therefore, it's crucial to perform iterative testing and validation to fine-tune these parameters, a process that can be significantly streamlined using specialized machine learning platforms. Edge Impulse&nbsp;offers&nbsp;intuitive tools for data preprocessing, allowing developers to easily filter and normalize data, ensuring it's ready for model training.<h1 dir="ltr">Neural Network Models for Gesture Recognition</h1>Gesture recognition typically employs Convolutional Neural Networks (CNNs) due to their ability to process spatial hierarchies in the data. A standard CNN model for gesture recognition would include multiple layers, such as convolutional layers, Rectified Linear Unit (ReLU) activation functions, max pooling, and batch normalization. These layers work in tandem to extract features from the raw sensor data and classify them into specific gestures. However, training these models requires a deep understanding of machine learning algorithms, data science, and optimization techniques. Specialized machine learning platforms can simplify this process by offering pre-trained models and automated training pipelines.The complexity of the neural network model often depends on the number of gestures to be recognized and the required accuracy. More complex models with additional layers and nodes can offer higher accuracy but may be computationally intensive, making them unsuitable for real-time applications on resource-constrained devices like the PSoC 6. Therefore, model optimization techniques like pruning, quantization, and layer fusion are often employed to reduce the computational complexity without significantly compromising accuracy.<h1 dir="ltr">Implementing Gesture Recognition on PSoC 6</h1>Developing a gesture recognition system involves more than just machine learning models. The development environment needs to be set up, the hardware configured, and the software architecture designed. In the case of PSoC 6, the ModusToolbox software environment provides a range of tools for configuration and development. The code structure for implementing gesture recognition involves various tasks, such as data acquisition, preprocessing, and classification. FreeRTOS is often used for managing these tasks, offering the benefits of real-time operation and future scalability.The software architecture for implementing gesture recognition on PSoC 6 often involves multiple tasks running concurrently. For instance, one task may be responsible for data acquisition, another for data preprocessing, and yet another for gesture classification. Managing these tasks efficiently requires a robust multitasking environment, which is where real-time operating systems like FreeRTOS come into play. FreeRTOS allows for efficient task scheduling, inter-task communication, and resource management, making it easier to develop complex applications like gesture recognition.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0OTU2MTc1NTA3LWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 302px;" class="fr-fic fr-dib" alt="edge-AI-gesture-recognition">Fig. 2: Through edge AI techniques, gestures can be translated into data formats comprehensible by computers, enabling efficient real-time processing and decision-makingBeyond the software architecture, the deployment phase is also crucial. Once the model is trained and optimized, it needs to be converted into a format that can be loaded onto the PSoC 6 MCU. This often involves converting high-level model formats like H5 into C or C++ code that can be integrated into the ModusToolbox environment. This step can be cumbersome and error-prone if done manually. However, specialized machine learning platforms offer automated tools for model conversion and deployment, ensuring that the transition from development to production is seamless.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0OTU2MjQ4MjM5LXNwYWNlc19HRWdjQ2s0UGtTNVBhNnVCYWJsZF91cGxvYWRzX2dpdC1ibG9iLWQyYjE4YjVmYTMzM2YwYTJjNjMwY2MzYTBlZmM3ZGY3YTNiNDYzYTBfdHV0b3JpYWwtY29udGludW91cy1tb3Rpb24tY3JlYXRlLWltcHVsc2Uud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 304px;" class="fr-fic fr-dib" alt="edge-impulse-studio-gesture-recognition">Fig. 3: Designing an Edge AI application for motion recognition using Edge Impulse studio. Source:&nbsp;<a href="https://docs.edgeimpulse.com/docs/tutorials/end-to-end-tutorials/continuous-motion-recognition" target="_blank" rel="noopener noreferrer">Edge Impulse</a>The final piece of the puzzle is real-time performance monitoring and updates. Once deployed, the system's performance needs to be monitored to ensure it meets the desired accuracy and latency requirements. Any necessary updates or optimizations can be rolled out as firmware updates, a process that can be automated and streamlined using specialized machine learning platforms.As we've seen, the journey from concept to deployment in gesture recognition involves multiple steps, each with its own set of challenges and complexities. While the PSoC 6 provides a robust hardware platform, the software complexities often require specialized tools and platforms for efficient development and deployment.Here is a project by Infineon explaining the step by step process to implementing gesture classification using Infineon PSoC 6: <a href="https://github.com/Infineon/mtb-example-ml-gesture-classification" target="_blank">GitHub - Infineon/mtb-example-ml-gesture-classification</a>. For in-depth exploration of more Edge AI projects, don't forget to check out: <a href="https://edgeimpulse.com/blog" target="_blank">Edge Impulse Blog</a>.<h1 dir="ltr" id="isPasted">Conclusion</h1>Gesture recognition is poised to become a standard feature in the next generation of human-machine interfaces. While platforms like Infineon's PSoC 6 provide the hardware capabilities for such advanced applications, the development process involves several complexities. These complexities can be significantly simplified by leveraging the power of specialized machine learning platforms like Edge Impulse, making it easier to develop, deploy, and scale robust gesture recognition systems.Further reading:&nbsp;<a href="https://www.wevolver.com/article/spotlight-on-innovations-in-edge-computing-and-machine-learning-predictive-maintenance" target="_blank">Spotlight on Innovations in Edge Computing and Machine Learning: Predictive Maintenance</a><h1 dir="ltr">About the sponsor: Edge Impulse</h1><a href="https://www.edgeimpulse.com/" target="_blank">Edge Impulse</a> is the leading development platform for embedded machine learning, used by over 1,000 enterprises across 200,000 ML projects worldwide. We are on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.&nbsp;From getting started in under five minutes to MLOps in production, we enable highly optimized ML deployable to a wide range of hardware from MCUs to CPUs, to custom AI accelerators. With Edge Impulse, developers, engineers, and domain experts solve real problems using machine learning in embedded solutions, speeding up development time from years to weeks. We specialize in industrial and professional applications including predictive maintenance, anomaly detection, human health, wearables, and more.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjk0OTU2NDM1MDUxLWltYWdlMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="edge-impulse-logo"><iframe width="540" height="600" src="https://c8056756.sibforms.com/serve/MUIFAMyA6BGksOUPEokHuBnEmZRHhYgJ4RXb4BV0gvzN98ymNIvQdTcBFQnRVfviT6yZSFW_R2g1hENBu7p7Afnyv9Kt0unAAbfFU5mMQhBwI-8hQnKf2r1mmRKhUgDLmtk8bpVSZ7kZGpleCPqtYRv8_kkvODL7F4neGLNGgdpFDm9pPzoYpwbQkc0BU5GVOFoq8CSmD0DDvDjG" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" 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>

Gesture recognition, leveraging the advanced capabilities of embedded devices and streamlined through specialized platforms is creating new means of human-machine interactions, paving the way for more intuitive and user-friendly device interfaces.

Gesture Recognition and Classification Using Infineon PSoC 6 and Edge AI

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This article provides a comprehensive comparison between UART and SPI by delving into their working principles, advantages, disadvantages, and other factors that influence their performance in embedded systems. By the end of this article, you will have a better understanding of these communication protocols and be able to make an informed decision when selecting the right protocol for your project.

UART vs SPI: A Comprehensive Comparison for Embedded Systems

It&rsquo;s easy to feel overwhelmed when you&rsquo;re building low power electronics. There are so many different components and technologies to learn that it can be hard to know where to begin.Recently, one of our clients wanted to redesign their field-deployed IoT environmental monitor to a smaller, lighter, and more power-efficient package with longer battery life and could be placed more easily into remote locations. In order to achieve this, they faced a choice of going with a Linux-based single-board-computer with a microprocessor, or a microcontroller-based compute platform.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5NDYzLTE2Nzk5NDgyNjk0NjMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2.jpg 1000w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2-300x200.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-environmental-monitoring-2-768x512.jpg 768w" sizes="(max-width: 1000px) 100vw, 1000px" class="fr-fic fr-dii">Environmental sensors may require either a microcontroller or microprocessor to compute&nbsp;In this post, we look at the differences between single board computers and microcontroller kits when it comes to <a href="https://www.mistywest.com/posts/5-reasons-to-use-an-iot-framework/" target="_blank">designing a successful remote sensor</a>, so that you have the information you need to choose the right solution for future applications.&nbsp;<h2>A summary of differences between microcontroller kits and single-board computers (SBCs)</h2>A microcontroller is a small all-in-one computing platform with features like onboard memory, built-in timers, IO handling, and other key features for interacting with electrical hardware.&nbsp;Microcontrollers&rsquo; common uses are devices like remote controls, toys, industrial equipment, cars, and implantable medical devices. You can find them in microcontroller kits like <a href="https://www.arduino.cc/" target="_blank">Arduino</a>.&nbsp;<a data-rel="lightbox-image-0" data-rl_caption="An Arduino Uno, one of the microcontroller boards in the Arduino family" data-rl_title="" href="https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3.jpg" target="_blank" title=""><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MjAzLTE2Nzk5NDgyNjkyMDMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3.jpg 600w, https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3-300x300.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/Arduino_Uno_-_R3-150x150.jpg 150w" sizes="(max-width: 600px) 100vw, 600px" class="fr-fic fr-dii">An Arduino Uno, one of the microcontroller boards in the Arduino family</a>&nbsp;A microprocessor is similar to a conventional computer CPU, which is only the processor, and needs to be connected to external memory, timers, storage, and IO peripherals in order to function. Microprocessors are generally more powerful, and allow for a more granular hardware design by selecting the exact memory, timers and other features that one needs. You&rsquo;ll find microprocessors in consumer computing equipment, and the increase of small and cheap single-board computers (SBCs) like <a href="https://www.raspberrypi.org/" target="_blank">Raspberry Pi</a> has enabled them to be used in new applications like smart devices.&nbsp;<a href="https://opensource.com/resources/raspberry-pi" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5NDk4LTE2Nzk5NDgyNjk0OTcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-raspberry-pi.jpg 675w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-raspberry-pi-300x205.jpg 300w" sizes="(max-width: 675px) 100vw, 675px" class="fr-fic fr-dii">Raspberry Pi single-board computer</a>&nbsp;<h2>Strengths and Weaknesses</h2>When it comes to computing power, how resource-intensive is your problem? Will it be simple sensor reading (low intensity), image processing (high intensity), or machine vision/AI (very intense)? For development, can you use an off-the-shelf operating system or reuse other people&rsquo;s code? And how much power consumption or battery life will you require?To help you evaluate the strengths and weaknesses between your options, we&rsquo;ve added them to Table 1 below, followed by a detailed breakdown of each feature.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MDgyLTE2Nzk5NDgyNjkwODIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-microcontroller-microprocessor-compare-v3-01.jpg 717w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-microcontroller-microprocessor-compare-v3-01-300x193.jpg 300w" sizes="(max-width: 717px) 100vw, 717px" class="fr-fic fr-dii">Table 1: strengths and weaknesses between the computing devices&nbsp;<h3>Computing power</h3>Some microcontrollers are designed with low power consumption in mind and are specified to be run at reduced clock frequencies, meaning the chip computes more slowly but with less power consumption. This makes microcontrollers good for less compute-intensive applications like sensor reading, serial communication, or mechanical control systems, but they don&rsquo;t tend to have the processing power for computationally intensive tasks, like image processing.In comparison, there&rsquo;s often much more computing power available on commercial microprocessor boards like Raspberry Pi, which can support more complicated tasks like streaming video or running a local website. There are also microprocessors with additional modules, such as the <a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/" target="_blank">Nvidia Jetson Nano</a>, which has a graphics processing unit for tasks like machine vision and AI.&nbsp;<a href="https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/education-projects/" target="_blank"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY5MDA3LTE2Nzk5NDgyNjkwMDcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia.jpg 800w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia-300x169.jpg 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-jetson-nano-nvdia-768x432.jpg 768w" sizes="(max-width: 800px) 100vw, 800px" class="fr-fic fr-dii">Nvidia Jetson Nano</a>&nbsp;<h3>Available packages, development resources, and support</h3>If you pick an established platform like Arduino or Raspberry Pi, there are many existing open-source libraries and a community that can help debug issues. However, If you pick a more specialized and less common platform, you may be on your own when sorting through documentation and trying to fix the issue. It is important for you to decide if you need the specialized features of a particular platform and trade that against the supportWith embedded Linux solutions, most of the basic drivers for common connections like ethernet, audio, and video are already available, tested and stable. In contrast, you may need to write the drivers for your particular microcontroller to connect with peripherals, which can be time-consuming and challenging.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY4NzkwLTE2Nzk5NDgyNjg3OTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-power-consumption-graphic-01.png 612w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-power-consumption-graphic-01-300x74.png 300w" sizes="(max-width: 612px) 100vw, 612px" class="fr-fic fr-dii">&nbsp;<h3>Power consumption</h3>Microcontroller-based boards usually use less energy than microprocessor-based SBCs. One of the reasons is that microcontrollers generally run at lower clock frequencies than microprocessors which in turn also means reduced computing power.&nbsp;<h3>Coding language support</h3>If you&rsquo;re running your code on a microcontroller, you&rsquo;re likely going to need to build your software with a lower level language like C or C++. On a microprocessor with a Linux distribution, you will have the choice between many more languages. This can help to create POCs (Proof Of Concepts) at a much faster pace.&nbsp;There are many libraries for peripherals like cameras, sensors, etc. written in higher-level languages like Python, for instance. By picking a platform that can run those languages you will be able to leverage those open source libraries. However, If your solution requires a lot of low-level hardware manipulation, this may not be helpful.&nbsp;<h3>Code portability</h3>How tightly integrated is the code with the hardware? If you write a bare-metal application for a microcontroller, it will be challenging to switch over to another platform &ndash; but projects that are running FreeRTOS, for example, make switching easier. If you&rsquo;re using a Linux-based SBC and peripherals over protocol like USB, it will be even easier to change the compute platform because there&rsquo;s often libraries and drivers available. In summary, depending on the tools you are planning to use some platforms might be more flexible to be interchangeable than others.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5OTQ4MjY4ODY2LTE2Nzk5NDgyNjg4NjYucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" width="100%" height="auto" srcset="https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01.png 892w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01-300x100.png 300w, https://www.mistywest.com/wp-content/uploads/2022/06/mistywest-boot-time-graphic-01-768x257.png 768w" sizes="(max-width: 892px) 100vw, 892px" class="fr-fic fr-dii">&nbsp;<h3>Boot time</h3>The boot time of a SBC with embedded Linux on it is significantly greater than that of a microcontroller, be it bare-metal or running an RTOS. When utilizing an SBC, an application must not depend on fixed time slots during start up and a few seconds for it to come up must be acceptable.&nbsp;<h3>Cost</h3>In general, microcontrollers cost less than microprocessors. As an example, the <a href="https://www.pjrc.com/teensy/" target="_blank">top of the line Teensy</a> costs $26 and the flagship <a href="https://store-usa.arduino.cc/collections/boards/products/arduino-uno-rev3" target="_blank">Arduino board (Uno)</a> costs $25, whereas the flagship <a href="https://www.raspberrypi.com/products/raspberry-pi-4-model-b/" target="_blank">Raspberry Pi board (Pi 4)</a> starts at $35 and more expensive boards like the <a href="https://store.nvidia.com/en-us/jetson/store/?page=1&limit=9&locale=en-us&product=117&product_filter=117~3,118~3,119~4,120~3,173~1" target="_blank">Jetson Nano</a> may range up to $130. There is a wide range of costs in both domains and plenty of choices available on the market.&nbsp;<h2>Conclusion</h2>Ultimately, SBCs and microcontroller kits are both solutions for embedded applications that require computing; it&rsquo;s the use case that will inform which selection is the right one to make.SBCs with an embedded Linux OS, even though more costly, offer greater computing power, and allow a quick start to your project, with a wide range of options for your application&rsquo;s programming language, very active online communities and advantageous portability. Microcontrollers, on the other hand, are much more energy efficient and allow full control and fine tuning of their internal configuration &ndash; but additional time for development of features might need to be considered.In the case for our client, there were tradeoffs for both; a microcontroller-based platform/MCU could achieve lower power consumption, but would require more custom firmware development to integrate. The SBC accelerated development by providing a lot of open-source software packages and drivers that could be rapidly configured, but at the expense of higher power consumption.When it comes to making the right selection, if a quick prototype for a proof of concept is needed or if the device is going to be used for inference, object localization or other demanding computing tasks, an SBC is likely the better solution. The portability of the code and the flexibility of the system will simplify development and maintenance significantly. This article was originally written by Malcolm Dimeglio, Software Developer at MistyWest, <a href="https://www.mistywest.com/posts/choosing-the-right-computing-device-for-a-low-power-sensor-application/" rel="noopener noreferrer" target="_blank">for the MistyWest blog</a>.

Here's what you should consider when designing a successful remote sensor.

Choosing The Right Computing Device For A Low Power Sensor Application

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