An Algorithm for the Automatic Detection and Quantification of Athletes' Change of Direction Incidents Using IMU Sensor Data

Mahir Meghji, Aaron Balloch, Daryoush Habibi, Iftekhar Ahmad, Nicolas Hart, Robert Newton, Jason Weber, Adnan Waqar

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Orientation tracking of a moving object has a wide variety of applications, including but not limited to military, surgical aid, navigation systems, mobile robots, gaming, virtual reality, and gesture recognition. In this paper, a novel algorithm is presented to automatically track and quantify change of direction (COD) incident angles or heading angles (i.e., turning angles) of a moving athlete using the inertial sensor signals from a microtechnology unit [an inertia measurement unit (IMU)] commonly used in elite sport. The algorithm is capable of automatically classifying a COD incident according to the degree of the turn and the direction of the turn (left or right). The system involves 1) the accurate determination of the heading angle using IMU sensor fusion and 2) the use of an algorithm to detect and categorize all changes in angle using various signal computation processing techniques. This paper presents the algorithm to detect changes in angle and subsequent categorization. The algorithm is intended to accurately quantify changes in mechanical loading (angle) during COD incidents, which may present a new perspective in the monitoring of athletes for performance enhancement and injury prevention purposes.

Original languageEnglish
Article number8638798
Pages (from-to)4518-4527
Number of pages10
JournalIEEE Sensors Journal
Volume19
Issue number12
DOIs
Publication statusPublished - 15 Jun 2019
Externally publishedYes

Keywords

  • edge detection
  • gait
  • Inertial measurement unit
  • kinematics
  • sensors
  • sports
  • wearable
  • yaw angle

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