Real-time sensor-embedded neural network for human activity recognition

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 : 1.

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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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