Open Access

ARTICLE

Sensors-Based Ambient Assistant Living via E-Monitoring Technology

Sadaf Hafeez¹, Yazeed Yasin Ghadi², Mohammed Alarfaj³, Tamara al Shloul⁴, Ahmad Jalal¹, Shaharyar Kamal¹, Dong-Seong Kim^5,*

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 Electrical Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
4 Department of Humanities and Social Science, Al Ain University, Al Ain, 15551, UAE
5 Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, Korea

* Corresponding Author: Dong-Seong Kim. Email:

Computers, Materials & Continua 2022, 73(3), 4935-4952. https://doi.org/10.32604/cmc.2022.023841

Received 23 September 2021; Accepted 10 January 2022; Issue published 28 July 2022

Abstract

Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, way-point trajectory, Hilbert transform, Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions. These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method. Finally, the Masi entropy classifier is applied for action recognition and classification. Experiments have been performed on three challenging datasets, namely, UTD-MHAD, 50 Salad, and CMU-MMAC. During experimental evaluation, the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1% with UTD-MHAD dataset, 90.6% with 50 Salad dataset, and 89.5% with CMU-MMAC dataset. Hence experimental results validated the proposed system.

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Keywords

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