Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: a review

The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system’s requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work addresses the gap in literature targeting the use of MCUs as edge inference devices for healthcare wearables. Thus, can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables

Smart Modular Home System

A Smart Home incorporates advanced automation systems providing its home owners comfort, security, energy efficiency, and convenience at all times, regardless of whether anyone is at home. However, the use of smart technology and IoT is accompanied by high costs and lack of security. This project aims to propose a smart home system prototype named Smart Modular Home System (SMHS) that monitors IoT devices through a Wi-Fi network. The system is composed of a central device and different smart home modules synched using NFC technology. The central device and sensor controllers are based on the NXP Kinetis Cortex M0+/M4 and LPC Cortex M4 MCU families, the different modules are composed of the NXP sensors portfolio and the Wi-Fi communication is achieved using the ESP8266 controller. The smart home modules monitors door locks, lights, thermostats, and other artifacts within a limited network area with a highly customizable setup depending on the user's needs. The SMHS aims to provide security and home automation to any homeowner, being a low cost and a low power solution. Setting up the SMHS home will be intuitive, by choosing among the different modular devices available based on the necessities of the area.Una casa inteligente incorpora sistemas de automatización avanzados para sus propietarios; estos sistemas ofrecen comodidad, seguridad, eficacia de energía y conveniencia en todo momento, aunque nadie se encuentre en casa. A pesar de esto, el uso de tecnologías inteligentes e internet de las cosas está acompañado de altos costos y ausencia de seguridad. Este proyecto tiene como propósito proponer un prototipo llamado Smart Modular Home System (SMHS), el cual monitorea dispositivos inteligentes por medio de una red Wi-Fi. El sistema está compuesto de un dispositivo central y diferentes módulos inteligentes los cuales son sincronizados por medio de la tecnología de NFC. El dispositivo central y los controladores de los sensores están basados en las familias de microcontroladores Kinetis Cortex M0+/M4 y LPC Cortex M4, los diferentes módulos están compuestos del portafolio de sensores de NXP y la comunicación Wi-Fi se logra con el controlador ESP8266. Los módulos inteligentes monitorean puertas, focos, termostatos y otros dispositivos dentro de una red de área limitada con diversas opciones de configuración para cualquier usuario dentro de casa dependiendo de sus necesidades. El SMHS tiene como objetivo ofrecer seguridad y automatización para todos los usuarios dentro de sus casas a un bajo costo y bajo uso de energía. Configurar el SMHS será intuitivo, se podrá seleccionar entre una gran variedad de módulos para las necesidades del hogar.Consejo Nacional de Ciencia y Tecnologí

Wearable Fall Detector Using Recurrent Neural Networks

Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Design and implementation of a low-power low-cost smart embedded system for remote animal monitoring

This Master’s thesis serves as the foundation for an innovative wildlife monitoring system, encompassing hardware design, firmware and software development, and offering insights into future directions. Leveraging the research group’s extensive experience in research, development, and field deployment of wildlife technology solutions, the thesis has culminated in a device with versatile capabilities suitable for a wide range of applications. A central focus of this work is on energy efficiency, prioritizing low-power operation to facilitate extended field deployments and reduce maintenance requirements. The integration of AI capabilities is a core component, enabling real-time data analysis within the embedded system. The system’s architecture is thoughtfully designed to seamlessly integrate data from diverse sources, including visual, acoustic, and environmental inputs, providing comprehensive insights into the natural world. Modularity in communication networks empowers the system to adapt to varying project requirements and network environments. The successful integration of hardware and software components enhances system performance, ensuring seamless data flow and efficient communication between different modules. The thesis underscores the importance of comprehensive testing, performance characterization, and real-world field testing for future research. In summary, this work represents a crucial step in the development of a versatile, energy-efficient, and AI-enhanced wildlife monitoring system with the potential to make substantial contributions to the field of conservation technology.Universidad de Sevilla. Máster Universitario en Microelectrónica: Diseño y Aplicaciones de Sistemas Micro/Nanométrico

Implement a chorus effect in a real time embedded system

In this project, a chorus effect for audio signals will be developed. Factors to analyze: - Number of voices - Number and waveshape of oscillators - Frequency control based on input signal parameters.Design and implementation of a chorus effect in a real time embedded system has been devised, introducing novel concepts in the canonical architecture of such effects.Se ha llevado a término el diseño e implementación de un efecto de tipo chorus en un sistema empotrado en tiempo real, introduciendo algunas novedades en la arquitectura canónica de este tipo de efectos.El disseny i implementació d'un efecte de chorus en un sistema encastat en temps real s'ha dut a terme, introduint algunes novetats en l'arquitectura canònica d'aquest tipus d'efectes

Secure Sensor Prototype Using Hardware Security Modules and Trusted Execution Environments in a Blockchain Application: Wine Logistic Use Case

The security of Industrial Internet of Things (IIoT) systems is a challenge that needs to be addressed immediately, as the increasing use of new communication paradigms and the abundant use of sensors opens up new opportunities to compromise these types of systems. In this sense, technologies such as Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs) become crucial for adding new layers of security to IIoT systems, especially to edge nodes that incorporate sensors and perform continuous measurements. These technologies, coupled with new communication paradigms such as Blockchain, offer a high reliability, robustness and good interoperability between them. This paper proposes the design of a secure sensor incorporating the above mentioned technologies—HSMs and a TEE—in a hardware device based on a dual-core architecture. Through this combination of technologies, one of the cores collects the data extracted by the sensors and implements the security mechanisms to guarantee the integrity of these data, while the remaining core is responsible for sending these data through the appropriate communication protocol. This proposed approach fits into the Blockchain networks, which act as an Oracle. Finally, to illustrate the application of this concept, a use case applied to wine logistics is described, where this secure sensor is integrated into a Blockchain that collects data from the storage and transport of barrels, and a performance evaluation of the implemented prototype is providedEuropean Union’s Horizon Europe research and innovation program through the funding project “Cognitive edge-cloud with serverless computing” (EDGELESS) under grant agreement number 101092950FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades under Project B-TIC-588-UGR2