IMPACT OF FRACTALS EMERGING FROM THE FITNESS ACTIVITIES ON THE RETAIL OF SMART WEARABLE DEVICES

Fuzhang Wang, Ayesha Sohail, Qiwei Tang and Zhiwu Li
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Fuzhang Wang: School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou, Jiangsu 221018, P. R. China†College of Education, Nanchang Normal College of Applied Technology, Nanchang, Jiangxi 330108, P. R. China
Ayesha Sohail: ��Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
Qiwei Tang: �Hitachi Building Technology (Guangzhou) Co., Ltd., Guangzhou 510700, P. R. China
Zhiwu Li: �Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau SAR, China

FRACTALS (fractals), 2024, vol. 32, issue 01, 1-10

Abstract: The smart wearable devices that can track the fitness activities are getting famous these days due to their easy-to-use features. The fitness trackers can work for an individual in a promising manner, provided that the user is well familiar with the device and is committed with the timelines. Several reports have provided evidence that these smart wearable devices have not showed promising results and in most of the cases, people have stopped using them, few weeks after the purchase. There are several reasons linked with this response. During this research, we have worked on the correlations of weight loss via smart device with the age, gender, body mass index (BMI) and ideal body weight (IBW), with the aid of gradient boosted decision trees (XGBoost) and support vector machine (SVM) learning tools. XGBoost and SVM are capable of dealing with complex datasets, with higher frequencies, and for data emerging from multiple sources. These machine learning tools use kernel functions for the clustering and other classification measures, and are thus better as compared to the logistic methods. Next, the time series forecasting tools are discussed with the Bayesian hyperparametric optimization. The time series of the weight loss monitoring of each individual, depicted in this manner, provided complex fractal patterns, with reduction in amplitude, with the passage of time.

Keywords: AI-Nonlinear Model; Dynamical Analysis; Support Vector Machine Learning; Smartwatch; Weight Loss; Fractals (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X22401120

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