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Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Mouazma Batool1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

1 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
2 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 15551, UAE
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia
4 Department of Humanities and Social Science, Al Ain University, Al Ain, 15551, UAE
5 Department of Computer Engineering, Korea Polytechnic University, 237 Sangidaehak-ro Siheung-si, Gyeonggi-do, 15073, Korea

* Corresponding Author: Jeongmin Park. Email: email

Computers, Materials & Continua 2022, 72(1), 1747-1764. https://doi.org/10.32604/cmc.2022.025112

Abstract

Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene. Based on these silhouettes, full body features and point based features have been extracted which are further optimized with probability based incremental learning (PBIL) algorithm. Finally, random forest classifier has been used to classify activities into different categories. The n-fold cross-validation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71% over other state-of-the-art methodologies.

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APA Style
Batool, M., Ghadi, Y.Y., Alsuhibany, S.A., Shloul, T.A., Jalal, A. et al. (2022). Self-care assessment for daily living using machine learning mechanism. Computers, Materials & Continua, 72(1), 1747-1764. https://doi.org/10.32604/cmc.2022.025112
Vancouver Style
Batool M, Ghadi YY, Alsuhibany SA, Shloul TA, Jalal A, Park J. Self-care assessment for daily living using machine learning mechanism. Comput Mater Contin. 2022;72(1):1747-1764 https://doi.org/10.32604/cmc.2022.025112
IEEE Style
M. Batool, Y.Y. Ghadi, S.A. Alsuhibany, T.A. Shloul, A. Jalal, and J. Park "Self-Care Assessment for Daily Living Using Machine Learning Mechanism," Comput. Mater. Contin., vol. 72, no. 1, pp. 1747-1764. 2022. https://doi.org/10.32604/cmc.2022.025112



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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