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Real-Time Human Activity Recognition in Smart Home on Embedded Equipment: New Challenges

Houda Najeh 1, 2, 3, * Christophe Lohr 2, 3 Benoit Leduc 1 
* Corresponding author
3 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 : Building Energy Management (BEM) and monitoring systems should not only consider HVAC systems and building physics but also human behaviors. These systems could provide information and advice to occupants about the significance of their practices with regard to the current state of a dwelling. It is also possible to provide services such as assistance to the elderly, comfort and health monitoring. For this, an intelligent building must know the daily activities of its residents and the algorithms of the smart environment must track and recognize the activities that the occupants normally perform as part of their daily routine. In the literature, deep learning is one of effective supervised learning model and cost-efficient for real-time HAR, but it still struggles with the quality of training data (missing values in time series and non-annotated event), the variability of data, the data segmentation and the ontology of activities. In this work, recent research works, existing algorithms and related challenges in this field are firstly highlighted. Then, new research directions and solutions (performing fault detection and diagnosis for drift detection, multi-label classification modeling for multi-occupant classification, new indicators for training data quality, new metrics weighted by the number of representations in dataset to handle the issue of missing data and finally language processing for complex activity recognition) are suggested to solve them respectively and to improve this field.
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Submitted on : Wednesday, September 21, 2022 - 12:20:37 PM
Last modification on : Saturday, September 24, 2022 - 3:56:17 AM


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Distributed under a Creative Commons Attribution 4.0 International License



Houda Najeh, Christophe Lohr, Benoit Leduc. Real-Time Human Activity Recognition in Smart Home on Embedded Equipment: New Challenges. ICOST 2022: 19th International Conference on Smart Homes and Health Telematics, Jun 2022, Paris, France. pp.125-138, ⟨10.1007/978-3-031-09593-1_10⟩. ⟨hal-03782651⟩



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