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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.17472 |
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| _version_ | 1866913705421176832 |
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| author | Benmessaoud, Ahmed. S Kezai, Wassim Medjani, Farida Bouaita, Khalid Kezai, Tahar |
| author_facet | Benmessaoud, Ahmed. S Kezai, Wassim Medjani, Farida Bouaita, Khalid Kezai, Tahar |
| contents | The year 2023 was a key year for tinyML unleashing a new age of intelligent sensors pushing intelligence from the MCU into the source of the data at the sensor level, enabling them to perform sophisticated algorithms and machine learning models in real-time. This study presents an innovative approach to Human Activity Recognition (HAR) using Intelligent Sensor Processing Units (ISPUs), demonstrating the feasibility of deploying complex machine learning models directly on ultra-constrained sensor hardware. We developed a 24-class HAR model achieving 85\% accuracy while operating within an 850-byte stack memory limit. The model processes accelerometer and gyroscope data in real time, reducing latency, enhancing data privacy, and consuming only 0.5 mA of power. To address memory constraints, we employed incremental class injection and feature optimization techniques, enabling scalability without compromising performance. This work underscores the transformative potential of on-sensor processing for applications in healthcare, predictive maintenance, and smart environments, while introducing a publicly available, diverse HAR dataset for further research. Future efforts will explore advanced compression techniques and broader IoT integration to push the boundaries of TinyML on constrained devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In-sensor 24 classes HAR under 850 Bytes Benmessaoud, Ahmed. S Kezai, Wassim Medjani, Farida Bouaita, Khalid Kezai, Tahar Signal Processing The year 2023 was a key year for tinyML unleashing a new age of intelligent sensors pushing intelligence from the MCU into the source of the data at the sensor level, enabling them to perform sophisticated algorithms and machine learning models in real-time. This study presents an innovative approach to Human Activity Recognition (HAR) using Intelligent Sensor Processing Units (ISPUs), demonstrating the feasibility of deploying complex machine learning models directly on ultra-constrained sensor hardware. We developed a 24-class HAR model achieving 85\% accuracy while operating within an 850-byte stack memory limit. The model processes accelerometer and gyroscope data in real time, reducing latency, enhancing data privacy, and consuming only 0.5 mA of power. To address memory constraints, we employed incremental class injection and feature optimization techniques, enabling scalability without compromising performance. This work underscores the transformative potential of on-sensor processing for applications in healthcare, predictive maintenance, and smart environments, while introducing a publicly available, diverse HAR dataset for further research. Future efforts will explore advanced compression techniques and broader IoT integration to push the boundaries of TinyML on constrained devices. |
| title | In-sensor 24 classes HAR under 850 Bytes |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.17472 |