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Review
. 2021 Sep 9;21(18):6037.
doi: 10.3390/s21186037.

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 , Sao Mai Nguyen  1 , Christophe Lohr  1 , Benoit LeDuc  2 , Ioannis Kanellos  1
Affiliations
Review

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

Damien Bouchabou et al. Sensors (Basel). .

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. However, 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, as well as missing and needed contributions. However, we also propose directions, research opportunities, and solutions to accelerate advances in this field.

Keywords: ambient assisting living; challenges; deep learning; human activity recognition; opportunities; smart home; survey; taxonomies.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Human Activity Recognition approaches.
Figure 2
Figure 2
Challenges for Human Activity Recognition in smart homes.
See this image and copyright information in PMC

References

    1. Chan M., Estève D., Escriba C., Campo E. A review of smart homes—Present state and future challenges. Comput. Methods Programs Biomed. 2008;91:55–81. doi: 10.1016/j.cmpb.2008.02.001. - DOI - PubMed
    1. Hussain Z., Sheng M., Zhang W.E. Different Approaches for Human Activity Recognition: A Survey. arXiv. 20191906.05074
    1. Dang L.M., Min K., Wang H., Piran M.J., Lee C.H., Moon H. Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognit. 2020;108:107561. doi: 10.1016/j.patcog.2020.107561. - DOI
    1. Beddiar D.R., Nini B., Sabokrou M., Hadid A. Vision-based human activity recognition: A survey. Multimed. Tools Appl. 2020;79:30509–30555. doi: 10.1007/s11042-020-09004-3. - DOI
    1. Singh D., Psychoula I., Kropf J., Hanke S., Holzinger A. International Conference on Smart Homes and Health Telematics. Springer; Berlin/Heidelberg, Germany: 2018. Users’ perceptions and attitudes towards smart home technologies; pp. 203–214.

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