research-article

On-line human activity recognition from audio and home automation sensors: : Comparison of sequential and non-sequential models in realistic Smart Homes

Authors: Pedro Chahuara, Anthony Fleury, François Portet, Michel VacherAuthors Info & Claims

Journal of Ambient Intelligence and Smart Environments, Volume 8, Issue 4

Pages 399 - 422

https://doi.org/10.3233/AIS-160386

Published: 01 January 2016 Publication History

Abstract

Automatic human Activity Recognition (AR) is an important process for the provision of context-aware services in smart spaces such as voice-controlled smart homes. This paper presents an on-line Activities of Daily Living (ADL) recognition method for automatic identification within homes in which multiple sensors, actuators and automation equipment coexist, including audio sensors. Three sequence-based models are presented and compared: a Hidden Markov Model (HMM), Conditional Random Fields (CRF) and a sequential Markov Logic Network (MLN). These methods have been tested in two real Smart Homes thanks to experiments involving more than 30 participants. Their results were compared to those of three non-sequential models: a Support Vector Machine (SVM), a Random Forest (RF) and a non-sequential MLN. This comparative study shows that CRF gave the best results for on-line activity recognition from non-visual, audio and home automation sensors.

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On-line human activity recognition from audio and home automation sensors: Comparison of sequential and non-sequential models in realistic Smart Homes¹

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cover image Journal of Ambient Intelligence and Smart Environments

Journal of Ambient Intelligence and Smart Environments Volume 8, Issue 4

Human-centric computing and intelligent environments

2016

104 pages

ISSN:1876-1364

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Cited By

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https://dl.acm.org/doi/10.3233/AIS-230144
Achirei EAlboaie L(2024)Development and Evaluation of a Passive Monitoring System for the Elderly Using the Matter ProtocolProcedia Computer Science10.1016/j.procs.2024.09.564246:C(2274-2283)Online publication date: 1-Jan-2024
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Rahimi Taghanaki SRainbow MEtemad A(2021)Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional MotionProceedings of the 2021 ACM International Symposium on Wearable Computers10.1145/3460421.3480417(23-27)Online publication date: 21-Sep-2021
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https://dl.acm.org/doi/10.3233/AIS-180509

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