Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition

This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from different residents. The second model, BiGRU+Q2L, uses a Query2Label multi-label cl...

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Published in:arXiv.org 2024-04
Main Authors: Chen, Xi, Cumin, Julien, Fano Ramparany, Vaufreydaz, Dominique
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Language:English
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Classification
Human activity recognition
Labels
Sensors
Separation
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Cumin, Julien
Fano Ramparany
Vaufreydaz, Dominique
description This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home. The first model, Seq2Res, uses a sequence generation approach to separate sensor events from different residents. The second model, BiGRU+Q2L, uses a Query2Label multi-label classifier to predict multiple activities simultaneously. Performances of these models are compared to a state-of-the-art model in different experimental scenarios, using a state-of-the-art dataset of two residents in a home instrumented with ambient sensors. These results lead to a discussion on the advantages and drawbacks of resident separation and multi-label classification for multi-person activity recognition.
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subjects Classification
Human activity recognition
Labels
Sensors
Separation
title Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
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