Open Access

ARTICLE

Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

by Muhammad Atif Hanif¹, Tallha Akram¹, Aamir Shahzad², Muhammad Attique Khan³, Usman Tariq⁴, Jung-In Choi⁵, Yunyoung Nam^6,*, Zanib Zulfiqar⁷

1 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, 47040, Pakistan
2 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, 22040, Pakistan
3 Department of Computer Science, HITEC University Taxila, Taxila, Pakistan
4 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
5 Department of Applied Artificial Intelligence, Ajou University, Suwon, Korea
6 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
7 Department of Electrical and Computer Engineering, CUI, Sahiwal, Pakistan

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 70(2), 3221-3234. https://doi.org/10.32604/cmc.2022.019815

Received 26 April 2021; Accepted 17 June 2021; Issue published 27 September 2021

Abstract

Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly categorized as basic and complex human activities. Recognition of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart watches. Other reasons include lack of sensor-based labeled dataset having several complex human daily life activities. Some of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition, whereas a few of them used both pocket and wrist positions. In this research, we have proposed a novel framework which is capable to recognize both basic and complex human activities using built-in-sensors of smart phone and smart watch. We have considered 25 physical activities, including 20 complex ones, using smart device’s built-in sensors. To the best of our knowledge, the existing literature consider only up to 15 activities of daily life.

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Citations