Shakerian, Ali, Douet, Victor, Shoaraye Nejati, Amirhossein et Landry, René Jr.
2023.
« Real-time sensor-embedded neural network for human activity recognition ».
Sensors, vol. 23, nº 19.
Compte des citations dans Scopus : 3.
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Résumé
This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject’s chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer’s activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Landry, René Jr |
| Affiliation: | Génie électrique |
| Date de dépôt: | 08 nov. 2023 15:24 |
| Dernière modification: | 18 déc. 2023 15:58 |
| URI: | https://espace2.etsmtl.ca/id/eprint/27956 |
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