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In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

Engineering and Technology

In-sensor human gait analysis with machine learning in a wearable microfabricated accelerometer

G. Dion, A. Tessier-poirier, et al.

Discover a groundbreaking wearable accelerometer that not only senses human gait patterns in real-time but also incorporates machine learning through innovative in-sensor computing! This revolutionary device, developed by a team of researchers from the Université de Sherbrooke and Université Laval, excels in power efficiency and data security by transmitting only classification labels, ensuring privacy while enhancing edge computing capabilities.

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Playback language: English
Abstract
This paper presents a wearable, microfabricated accelerometer that integrates sensing and machine learning capabilities using in-sensor computing. Leveraging reservoir computing, the device identifies human gait patterns in real-time by coupling the displacement of suspended microstructures. Compared to a conventional system, the in-sensor approach demonstrates significantly better power efficiency, paving the way for more ubiquitous deployment of machine learning in edge computing devices. The device also offers enhanced data security by only transmitting classification labels, not raw acceleration data.
Publisher
Communications Engineering
Published On
Mar 16, 2024
Authors
Guillaume Dion, Albert Tessier-Poirier, Laurent Chiasson-Poirier, Jean-François Morissette, Guillaume Brassard, Anthony Haman, Katia Turcot, Julien Sylvestre
Tags
wearable technology
accelerometer
machine learning
in-sensor computing
real-time gait analysis
power efficiency
data security

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