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Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT

Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT

Siham Boukhalfa, Abdelmalek Amine, Reda Mohamed Hamou
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 19
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781683181385|DOI: 10.4018/IJOCI.2022010104
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MLA

Boukhalfa, Siham, et al. "Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT." IJOCI vol.12, no.1 2022: pp.1-19. http://doi.org/10.4018/IJOCI.2022010104

APA

Boukhalfa, S., Amine, A., & Hamou, R. M. (2022). Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT. International Journal of Organizational and Collective Intelligence (IJOCI), 12(1), 1-19. http://doi.org/10.4018/IJOCI.2022010104

Chicago

Boukhalfa, Siham, Abdelmalek Amine, and Reda Mohamed Hamou. "Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT," International Journal of Organizational and Collective Intelligence (IJOCI) 12, no.1: 1-19. http://doi.org/10.4018/IJOCI.2022010104

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Abstract

The IoT is a new concept that provides a world where smart, connected, embedded systems operate, giving rise to the amount of data from different sources that are considered to have highly useful and valuable information. Data mining would play a critical role in creating smarter IoT. Traditional care of an elderly person is a difficult and complex task. The need to have a caregiver with the elderly person almost all the time drains the human and financial resources of the health care system. The emergence of Artificial intelligence has allowed the conception of technical assistance where it helps and reduces the time spent by the caregiver with the elderly person. This work aims to focus on analyzing techniques that are used for prediction purposes of falls in the elderly. We examine the applicability of three classification algorithms for IoT data. These algorithms are analyzed and a comparative study is undertaken to find the classifier that performs the best analysis on the dataset using a set of predefined performance metrics to compare the results of each classifier.

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