Fusion of Radar Data Domains for Human Activity Recognition in Assisted Living

Conference paper (2022)

Authors

Julien Le Kernec University of Glasgow
F. Fioranelli Microelectronics ,
Olivier Romain Observatoire de Paris
Alexandre Bordat Observatoire de Paris
Copyright: © 2022 Julien Le Kernec, F. Fioranelli, Olivier Romain, Alexandre Bordat
DOI: https://doi.org/10.1007/978-3-030-98886-9_7
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Published Date

2022

Language

English

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Abstract

Radar has long been considered an important technology for indoor monitoring and assisted living. As ageing has become a worldwide problem, it causes a huge burden on the government’s healthcare expenses and infrastructure. Radar-based human activity recognition (HAR) is foreseen to become a widespread sensing modality for health monitoring at home. Conventional radar-based HAR task usually adopts the amplitude of spectrograms as input to a convolutional neural network (CNN), which can limit the achieved performances. A hybrid fusion model is here proposed, which can integrate multiple radar data domains. The result shows that the proposed framework can achieve superior classification accuracy of 92.1% (+2.5% higher than conventional CNN) and a lighter computational load than the state-of-the-art techniques with 3D-CNN.

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