Inertial sensor and cluster analysis for discriminating agility run technique and quantifying changes across load. (February 2017)

• Record Type: Journal Article
• Authors: McGinnis, Ryan S.; Cain, Stephen M.; Davidson, Steven P.; Vitali, Rachel V.; McLean, Scott G.; Perkins, Noel C.
• Is Part Of: Biomedical signal processing and control. Volume 32(2017)
• Page Start: 150
• Page End: 156
• Publication Date: 2017-02
• Subjects: Inertial measurement units -- Optimization -- Drift correction -- Human performance -- Agility -- Load carriage; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.10.013
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Wearable sensor and optimization algorithm enable subject trajectory estimation. K-means cluster analysis reveals two distinct groups based on turning technique. Groups exhibit different adaptations to 20.5 kg load carriage. Load carriage eliminates group differences in turning technique. Future use of method includes informing training plans and equipment modifications. Abstract: Performance in an agility run drill is often used to characterize an athlete's ability to quickly and explosively change direction. Beyond athletic applications, agility tasks are also used to assess the physical readiness of warfighters for battle and the influence that their equipment has on their performance. However, in all of these applications, performance is currently assessed solely by the time it takes to complete the drill. While completion time meaningfully discriminates bottom-line performance, it does not reveal the underlying biomechanics that contributes to or limits that performance. Biomechanical metrics that accurately identify performance strengths and weaknesses could promote rapid performance gains via tailored training programs and inform equipment design. To these ends, we propose a belt-worn wireless inertial measurement unit (IMU) to quantify biomechanical metrics underlying speed and agility performance in agility tasks. A drift correction methodology is introduced that yields estimates of displacement, velocity, and acceleration of a subject's sacrum in a course with known waypoints. We apply this methodology on a large data set collected from 32 subjects completing a slalom run with and without a 20.5 kg load. A k-means cluster analysis of proposed performance metrics reveals two groups of subjects who use fundamentally distinct techniques to negotiate the turns of the course in the unloaded condition. The groups exhibit different adaptations following application of the load, ultimately erasing group differences in the loaded condition. We believe that this measurement methodology can be used widely for agility assessment to provide athletes, trainers and researchers with actionable data to inform training plans and equipment modifications.