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Sleep Posture Detection via embedded machine learning on a Reduced Set of Pressure Sensors

SENSORS 2025年第2期25卷 458页

作者： Peruzzi, Giacomo Galli, Alessandra Giorgi, Giada Pozzebon, Alessandro Univ Padua Dept Informat Engn I-35122 Padua Italy Eindhoven Univ Technol Dept Elect Engn NL-5612 AZ Eindhoven Netherlands

Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements.

关键词： artificial intelligence support vector machine pressure sensors embedded machine learning internet of things sensor selection sleep posture recognition obstructive sleep apnea

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Early Driver Fatigue Detection System: A Cost-Effective and Wearable Approach Utilizing embedded machine learning

VEHICLES

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VEHICLES 2025年第1期7卷

作者： Lin, Chengyou Zhu, Xinying Wang, Renpeng Zhou, Wei Li, Na Xie, Yu Fujian Police Coll Publ Secur Dept Fuzhou 350007 Peoples R China Xiamen Univ Pen Tung Sah Inst Micronano Sci & Technol Xiamen 361102 Peoples R China

Driving fatigue is the cause of many traffic accidents and poses a serious threat to road safety. To address this issue, this paper aims to develop a system for the early detection of driver fatigue. The system leverages heart rate variability (HRV) features and embedded machine learning to estimate the driver's fatigue level. The driver's HRV is derived from electrocardiogram (ECG) signals captured by a wearable device for analysis. Time- and frequency-domain HRV features are then extracted and used as the input for a machine learning classifier. A dataset of HRV features is collected from a driving simulation experiment involving 18 participants. Four machine learning classifiers are evaluated, and a backpropagation neural network (BPNN) is selected for its superior performance, achieving up to 94.35% accuracy. The optimized classifier is successfully deployed on an embedded system, providing a cost-effective and portable solution for the early detection of driver fatigue. The results demonstrate the feasibility of using HRV-based machine learning models for the early detection of driver fatigue, contributing to enhanced road safety and a reduced accident risk.

关键词： fatigue driving detection heart rate variability embedded machine learning electrocardiogram wearable device embedded system

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embedded machine learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review

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IEEE ACCESS 2022年 10卷 98450-98474页

作者： Diab, Maha S. Rodriguez-Villegas, Esther Imperial Coll London Dept Elect & Elect Engn Wearable Technol Lab London SW7 2AZ England

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.

关键词： Medical services machine learning Wearable computers Power demand machine learning algorithms Wearable computers Microcontrollers Edge ML embedded machine learning healthcare microcontroller TinyML wearable

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embedded machine learning of IoT streams to promote early detection of unsafe environments

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INTERNET OF THINGS 2024年 25卷

作者： Fernandez, Eduardo Illueca Valera, Antonio Jesus Jara Breis, Jesualdoas Fernandez Univ Murcia Dept Informat & Syst Murcia Spain Libelium LAB SL Ceuti Spain

Indoor particulate matter (PM) are small solid and liquid particles present in the air, and its monitoring is one of the key challenges regarding workplace safety because of its impact on human health. To address this issue, the Internet of Things (IoT) paradigm allows the implementation of hyperlocal monitoring systems, typically using traditional cloud architectures, which can be enhanced using edge computing architectures. For this reason, we propose an IoTEdge-Cloud architecture for a platform which promotes the early detection of unsafe environments through machine learning, composed of a sensing layer that collects all the data, an edge layer that performs the artificial intelligence tasks and a cloud layer orchestrating. This architecture is based on the FogFlow framework and the FIWARE components. Our solution proposes an embedded model that can predict the occurrence of PM values higher than the recommended ones-according to the Occupational Safety and Health Administration (OSHA) indicators-with an 87 % of accuracy and a reduction of latency of 26 %. Our platform is innovative because it is based on the FogFlow framework for edge computing and supported by the Smart Spot device validated in a field test. This step is missing from similar state-of-the-art platforms. Thus, we believe that this work contributes to demonstrating the usefulness of AIoT to monitor workplace safety and make trustable predictions, avoiding risky environments.

关键词： embedded machine learning Edge computing Indoor air quality monitoring Real-time systems Sensor analysis Data management and analytics

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embedded machine learning for EMG-Based Elbow Motion Recognition 7th

Embedded Machine Learning for EMG-Based Elbow Motion Recogni...

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7th International Conference on Green Technology and Sustainable Development

作者： Khang Hoang Vinh Nguyen Xuan Phu Do Vietnamese German Univ Mechatron & Sensor Syst Technol Binh Duong Vietnam

ISBN: (纸本)9783031761966;9783031761973

This research introduces a novel setup for capturing, processing, and analyzing electromyographic (EMG) signals for elbow motion recognition. The setup, comprising a three-lead EMG cable assembly, an amplifier PCB, and an Arduino Nano BLE board, implements embedded machine learning and power conservation. The study applies Discrete Wavelet Transform (DWT) for spectral analysis and feature extraction, aiding in EMG signal denoising. It utilizes the XGBoost Random Forest system for model training and K-Means Clustering for anomaly detection. The research evaluates the performance of the Edge Impulse framework, focusing on the model's performance, live classification results on the Arduino Nano BLE board, and power consumption during continuous operation. The model's offline performance was assessed using thirty additional samples, achieving an overall accuracy of 74.15%. The high performance in the "Idle" category suggests potential for a power-saving method by transitioning the system to a sleep state when idle. This evaluation provides insights for further model optimization and refinement. During the test runs conducted on the Arduino Nano BLE board, the inferencing time was measured to be approximately 50 ms. The program's power consumption was approximately 2 mAh, suggesting that it could be feasibly powered by a small rechargeable battery (approximately 200 mAh) for daily usage. This research affirms the potential of Edge Impulse and similar platforms in the field of embedded machine learning.

关键词： Electromyography Upper Limb Motion Recognition embedded machine learning Low-power Micro-controller Digital Notch Filter

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Real-Time embedded machine learning for Tensorial Tactile Data Processing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2018年第11期65卷 3897-3906页

作者： Ibrahim, Ali Valle, Maurizio Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit I-16145 Genoa Italy

machine learning (ML) has increasingly been recently employed to provide solutions for difficult tasks, such as image and speech recognition, and tactile data processing achieving a near human decision accuracy. However, the efficient hardware implementation of ML algorithms in particular for real time applications is still a challenge. This paper presents the hardware architectures and implementation of a real time ML method based on tensorial kernel approach dealing with multidimensional input tensors. Two different hardware architectures are proposed and assessed. Results demonstrate the feasibility of the proposed implementations for real time classification. The proposed parallel architecture achieves a peak performance of 302 G-ops while consuming 1.14 W for the Virtex-7 XC7VX980T FPGA device overcoming state of the art solutions.

关键词： embedded machine learning real time processing dedicated hardware implementation tensorial kernel tactile sensors FPGA

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A Shallow Neural Network for Real-Time embedded machine learning for Tensorial Tactile Data Processing

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第10期68卷 4232-4244页

作者： Younes, Hamoud Ibrahim, Ali Rizk, Mostafa Valle, Maurizio Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit I-16145 Genoa Italy Lebanese Int Univ Dept Comp & Commun Engn Beirut 146404 Lebanon

This paper presents a novel hardware architecture of the Tensorial Support Vector machine (TSVM) based on Shallow Neural Networks (NN) for the Single Value Decomposition (SVD) computation. The proposed NN achieves a comparable Mean Squared Error and Cosine Similarity to the widely used one-sided Jacobi algorithm. When implemented on an FPGA, the NN offers 324x faster computations than the one-sided Jacobi with reductions up to 58% and 67% in terms of hardware resources and power consumption respectively. When validated on a touch modality classification problem, the NN-based TSVM implementation has achieved a real-time operation while consuming about 88% less energy per classification than the Jacobi-based TSVM with an accuracy loss of at most 3%. Such results offer the ability to deploy intelligence on resource-limited platform for energy-constrained applications.

关键词： embedded machine learning real-time tensorial kernel tactile sensors neural networks singular value decomposition FPGA

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Traffic Level Monitoring in Urban Scenarios with Virtual Sensing Techniques Enabled by embedded machine learning 18

Traffic Level Monitoring in Urban Scenarios with Virtual Sen...

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18th IEEE Sensors Applications Symposium (SAS)

作者： Maccantelli, Francesco Peruzzi, Giacomo Pozzebon, Alessandro Univ Siena Dept Informat Engn & Math Siena Italy Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9798350323078

Urban traffic is one of the most significant causes of pollution, significantly contributing to the level of gaseous emissions and to high noise levels in city centres. Noise can then be assumed as an indicator to estimate traffic levels and the overall air quality. The scope of this paper is to demonstrate the chance of estimating traffic levels from the acquisition of audio samples, exploiting embedded machine learning techniques. To this aim, a set of embedded devices is used to collect an ad-hoc dataset for the training phase and then to implement the algorithm for the traffic level extraction. The prototype of the sensor node used for traffic estimation is based on a microcontroller featuring limited performances and reduced power consumption, allowing to set up an autonomous platform in charge of locally predicting the values without the need to transmit the sampled audio files to a remote data processing unit. Tests demonstrate that the embedded machine learning algorithm is able to correctly detect the traffic level around 95% of times, even with a complexity reduction of the algorithm itself, suggesting the possible implementation of a pervasive monitoring infrastructure based on a number of autonomous virtual sensors.

关键词： Traffic Level Virtual Sensing embedded machine learning Microcontroller Noise Microphone

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Exploring Local and Cloud-Based Training Use Cases for embedded machine learning 13

Exploring Local and Cloud-Based Training Use Cases for Embed...

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13th Mediterranean Conference on embedded Computing (MECO)

作者： Andalib, Nelum Selimi, Mennan South East European Univ Fac Contemporary Sci & Technol Tetovo North Macedonia South East European Univ Max van der Stoel Inst Tetovo North Macedonia

ISBN: (纸本)9798350387568;9798350387575

TinyML is a paradigm that makes machine learning (ML) algorithms to run on low-power devices. This involves training an ML model with pre-collected data and then storing it on a microcontroller. In this paper, we explore two application use cases of ML for TinyML devices. First, we present a weather station with a locally-trained snowfall prediction model using TensorFlow Lite for microcontrollers, leveraging on-device temperature and humidity sensors for localized forecasts. Further, a cloud-trained keyword spotting model deployed on an Arduino Nano 33 BLE device demonstrates the use of ML for embedded speech recognition. Our results highlight the need for optimizing parameters and techniques, opening the door to increased precision and efficiency in future embedded ML applications.

关键词： TinyML embedded machine learning Edge Impulse

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A Novel Methodology to Remotely and Early Diagnose Sleep Bruxism by Leveraging on Audio Signals and embedded machine learning 6

A Novel Methodology to Remotely and Early Diagnose Sleep Bru...

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IEEE International Symposium on Measurements and Networking (M&N)

作者： Peruzzi, Giacomo Galli, Alessandra Pozzebon, Alessandro Univ Siena Dept Informat Engn & Math Siena Italy Univ Padua Dept Informat Engn Padua Italy

ISBN: (数字)9781665483629

ISBN: (纸本)9781665483629

Nowadays, more and more people suffer from sleep bruxism, involving repetitive jaw-muscle activity characterised by teeth clenching or teeth grinding during night. Albeit it is not a life-threatening disease, its timely diagnosis is fundamental to prevent teeth wear and preserve quality of life. However, this condition is usually detected by dentists when its effects are overt, and its remote and early diagnosis is challenging and almost unfeasible. Nevertheless, sleep bruxism entails sounds related to teeth grinding or teeth chattering. Therefore, they can be exploited to detect occurrences of sleep bruxism, resulting in a first stage screening favouring its quick diagnosis. To this end, this paper proposes an innovative methodology for the remote assessment and diagnosis of sleep bruxism. Specifically, a machine learning (ML) model is devised which classifies audio signals in order to distinguish bruxism-related sounds. The classifier is carried out by making use of a Convolutional Neural Network (CNN), while the overall ML model is designed to be deployed on an embedded device featuring a microcontroller and a microphone. In so doing, a tiny, portable and ready to use embedded ML-enable device is set up, that is able to detect bruxism phenomena directly at patients home.

关键词： Audio Signals Bruxism Convolutional Neural Network Early Diagnosis embedded machine learning Artificial Intelligence Internet of Things Microcontroller Sounds Classification

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