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

Autor: Shakerian A; Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada., Douet V; Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada., Shoaraye Nejati A; Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada., Landry R Jr; Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Sep 28; Vol. 23 (19). Date of Electronic Publication: 2023 Sep 28.
DOI: 10.3390/s23198127
Abstrakt: 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.
Databáze: MEDLINE
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