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Real-Time Sensor-Embedded Neural Network for Human Activity Recognition

Authors :
Ali Shakerian
Victor Douet
Amirhossein Shoaraye Nejati
René Landry
Source :
Sensors, Vol 23, Iss 19, p 8127 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
19
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.1ef00e4175425a8690907966739061
Document Type :
article
Full Text :
https://doi.org/10.3390/s23198127