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Article Dans Une Revue Applied Sciences Année : 2022

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

Résumé

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 burden on 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.

Dates et versions

hal-03637445 , version 1 (11-04-2022)

Identifiants

Citer

Pierre-Emmanuel Novac, Alain Pegatoquet, Benoit Miramond, Christophe Caquineau. UCA-EHAR: a dataset for Human Activity Recognition with embedded AI on smart glasses. Applied Sciences, 2022, Sensor-Based Human Activity Recognition in Real-World Scenarios, pp.22. ⟨10.3390/app12083849⟩. ⟨hal-03637445⟩
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