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

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Shakerian, Ali, Douet, Victor, Shoaraye Nejati, Amirhossein and 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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Abstract

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

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Landry, René Jr
Affiliation: Génie électrique
Date Deposited: 08 Nov 2023 15:24
Last Modified: 18 Dec 2023 15:58
URI: https://espace2.etsmtl.ca/id/eprint/27956

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