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Smartphone Sensors Based Physical Life-Routine for Health Education

Tamara al Shloul1, Usman Azmat2, Suliman A. Alsuhibany3, Yazeed Yasin Ghadi4, Ahmad Jalal2, Jeongmin Park5,*

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

* Corresponding Author: Jeongmin Park. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 715-732.


The physical and the mental health of a human being largely depends upon his physical life-routine (PLR) and today’s much advanced technological methods make it possible to recognize and keep track of an individual’s PLR. With the successful and accurate recognition of PLR, a sublime service of health education can be made copious. In this regard, smartphones can play a vital role as they are ubiquitous and have utilitarian sensors embedded in them. In this paper, we propose a framework that extracts the features from the smartphone sensors data and then uses the sequential feature selection to select the most useful ones. The system employs a novel approach of codebook assignment that uses vector quantization to efficiently manipulate the data coming from the smartphone sensors of different nature and serve as a data compression module at the same time. The proposed system uses a multilayer perceptron classifier to differentiate among different PLRs. The experimentation was performed on the benchmark Real-life HAR dataset. It provides the data of four sensors: accelerometer, gyroscope, magnetometer, and global positioning system (GPS) for the recognition of four activities namely active, inactive, walking, and driving. The performance of the proposed system was validated using 10-fold cross-validation and the confidence of the system was recorded to be 91.80%.


Cite This Article

T. Al Shloul, U. Azmat, S. A. Alsuhibany, Y. Yasin Ghadi, A. Jalal et al., "Smartphone sensors based physical life-routine for health education," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 715–732, 2022.

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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