Tracking and Monitoring Fitness of Athletes Using IoT Enabled Wearables for Activity Recognition and Random Forest Algorithm for Performance Prediction

Authors

  • Krishna Prasad K. Associate Professor & Post-Doctoral Research Fellow, College of Computer & Information Sciences, Srinivas University, Mangalore, Karnataka, India
  • Aithal P. S. Vice Chancellor, Srinivas University, Karnataka, India
  • Geetha Poornima K. Research Scholar, College of Computer & Information Sciences, Srinivas University, Mangalore, Karnataka, India and Assistant Professor, Dept of Computer Science, St Philomena College, Puttur, Karnataka, India
  • Vinayachandra Research Scholar, College of Computer & Information Sciences, Srinivas University, Mangalore, Karnataka, India and Assistant Professor, Dept of Computer Science, St Philomena College, Puttur, Karnataka, India

DOI:

https://doi.org/10.47992/IJHSP.2581.6411.0062

Keywords:

Physical Activity, IoT, Wearable Devices, Fitness Tracking, Performance Prediction

Abstract

Purpose:   The progression in technology is made the best use of in every field. Sports analytics is an essential sector that has gained importance in this technology-driven era. It is used to determine the hidden relationships among different quantitative parameters that affect the performance of athletes. This type of analysis requires a large amount of data to be stored periodically. Cloud acts as a scalable centralized repository that can store the massive data essential for analysis purpose.   From the technological perspective there are numerous wearable activity tracking devices, which will be able to provide feedback of physical activities. With the help of random forest (RF) algorithm it is possible to classify huge datasets to perform predictions. In this paper, different smart devices that can be used to measure physical activity, use of RF algorithm for converting data obtained from smart devices into knowledge are explored. A conceptual model that uses   wearable devices for tracking and monitoring    and RF algorithm to predict the performance is suggested.

Methodology:  The study was conducted by referring to scholarly documents available online and by referring to websites of companies offering healthcare and sports related services. A conceptual model is developed based on the theoretical perception that incorporates the components needed for measuring the physical activities to predict the performance of athletes.  

Findings/Result: In this paper the proposed system contains four major activities as Capture, Store, Analyze, and Predict. The model considers use of IoT-enabled wearable devices to measure the physical activities of athletes and the information collected will in turn be used to analyze predict their performance and suggest them how to increase the chances of winning. However, the outcome of a game does not only depend upon the PA of athletes. It depends also upon the physical, mental, emotional health, nutrition and many other factors.

Originality: In this paper, a theoretical model is deduced to integrate IoT and RF Algorithm to track and monitor fitness of athletes using wearables for activity recognition and performance prediction.

Paper Type: Conceptual Paper

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Published

2021-04-21

How to Cite

Krishna Prasad K., Aithal P. S., Geetha Poornima K., & Vinayachandra. (2021). Tracking and Monitoring Fitness of Athletes Using IoT Enabled Wearables for Activity Recognition and Random Forest Algorithm for Performance Prediction. International Journal of Health Sciences and Pharmacy (IJHSP), 5(1), 72–86. https://doi.org/10.47992/IJHSP.2581.6411.0062

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