Enhancing athlete tracking using data fusion in wearable technologies
Abstract
In recent years, the use of wearable devices to track athlete performance has increased sharply. Using onboard sensors, wearable devices can provide critical information about athlete's performance and well-being. Athlete tracking is an important functionality of wearable devices that rely on positioning data, which also influences the accuracy of numerous other attributes. However, accurate athlete tracking is a challenging task due to the nonlinear nature of the problem and the presence of non-Gaussian noise. In the literature, researchers have used the particle filter (PF) to improve athlete tracking accuracy. While the PF algorithm, in general, works well, they perform poorly when athletes take the sharp change of direction (COD), a common and important movement in the sport that is not currently captured. In this article, we introduce a sensor fusion technique to address this challenge. Our proposed solution combines the positioning data and inertial sensor data to accurately track an athlete's movements. We then analyze the accuracy using data collected from a commercially used athlete tracking wearable device. We have found that the obtained results are very promising, and the proposed solution performs up to five times better than a conventional PF sensor fusion algorithm for positioning.
RAS ID
38896
Document Type
Journal Article
Date of Publication
2021
Volume
70
School
School of Engineering / School of Medical and Health Sciences
Copyright
subscription content
Publisher
IEEE
Comments
Waqar, A., Ahmad, I., Habibi, D., Hart, N., & Phung, Q. V. (2021). Enhancing athlete tracking using data fusion in wearable technologies. IEEE Transactions on Instrumentation and Measurement, 70, article 4004013. https://doi.org/10.1109/TIM.2021.3069520