Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies

Adnan Waqar, Iftekhar Ahmad, Daryoush Habibi, Nicolas Hart, Quoc Viet Phung

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number9389629
Number of pages13
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Inertial measurement unit (IMU)
  • information fusion
  • motion tracking
  • multisensor fusion
  • particle filter (PF)
  • sports
  • wearable technology

Fingerprint

Dive into the research topics of 'Enhancing Athlete Tracking Using Data Fusion in Wearable Technologies'. Together they form a unique fingerprint.

Cite this