Elderly Care

Using Deep Learning for Multi-Domain Activity Classification

Conference paper (2020)

Authors

Shaoxuan Li University of Glasgow
Mu Jia University of Glasgow
Julien Le Kernec University of Glasgow
Shufan Yang University of Glasgow
Olivier Romain University of Cergy-Pontoise
Copyright: © 2020 Shaoxuan Li, Mu Jia, Julien Le Kernec, Shufan Yang, F. Fioranelli, Olivier Romain
DOI: https://doi.org/10.1109/UCET51115.2020.9205464
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Published Date

2020

Language

English

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Abstract

Nowadays, health monitoring issues are increasing as the worldwide population is aging. In this paper, the radar modality is used to classify with radar signature automatically. The classic approach is to extract features from micro-Doppler signatures for classification. This data representation domain has its limitations for activities presenting similar accelerations like a frontal fall and picking up an object from the floor that lead to wrongly labeled activities. In this work, we propose to combine multiple radar data domains with deep learning. Features are extracted from four domains, namely, Range-Time, Range-Doppler, Doppler-Time, and Cadence Velocity Diagram. The extracted features are set as the input of a Convolutional Neural Network, yielding 91% accuracy with 10-fold cross-validation based on the University of Glasgow “Radar signatures of human activities” open dataset.

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