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A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning

Damien Bouchabou 1, 2, 3 Sao Mai Nguyen 1, 2 Christophe Lohr 1, 2 Benoit Leduc 3 Ioannis Kanellos 1, 2 
1 Lab-STICC_RAMBO - Equipe Robot interaction, Ambient system, Machine learning, Behaviour, Optimization
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.
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https://hal.inria.fr/hal-03343114
Contributor : Sao Mai Nguyen Connect in order to contact the contributor
Submitted on : Saturday, October 16, 2021 - 10:59:30 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Monday, January 17, 2022 - 6:07:12 PM

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Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Benoit Leduc, Ioannis Kanellos. A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning. Sensors, 2021, 21, ⟨10.3390/s21186037⟩. ⟨hal-03343114⟩

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