Published April 11, 2022 | Version v1
Journal article

UCA-EHAR: a dataset for Human Activity Recognition with embedded AI on smart glasses

Description

Human activity recognition can help in elderly care by monitoring the physical activities of a subject and identifying a degradation in physical abilities. Vision-based approaches require setting up cameras in the environment, while most body-worn sensor approaches can be a burdenon the elderly due to the need of wearing additional devices. Another solution consists in using smart glasses, a much less intrusive device that also leverages the fact that the elderly often already wear glasses. In this article, we propose UCA-EHAR, a novel dataset for human activity recognition using smart glasses. UCA-EHAR addresses the lack of usable data from smart glasses for human activity recognition purpose. The data are collected from a gyroscope, an accelerometer and a barometer embedded onto smart glasses with 20 subjects performing 8 different activities (STANDING, SITTING, WALKING, LYING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS, RUNNING, and DRINKING). Results of the classification task are provided using a residual neural network. Additionally, the neural network is quantized and deployed on the smart glasses using the open-source MicroAI framework in order to provide a live human activity recognition application based on our dataset. Power consumption is also analysed when performing live inference on the smart glasses' microcontroller.

Abstract

International audience

Additional details

Identifiers

URL
https://hal.archives-ouvertes.fr/hal-03637445
URN
urn:oai:HAL:hal-03637445v1