Journal article
Inertial sensor and cluster analysis for discriminating agility run technique and quantifying changes across load
Biomedical signal processing and control, Vol.32, pp.150-156
02/2017
DOI: 10.1016/j.bspc.2016.10.013
Abstract
•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 20.5kg load carriage.•Load carriage eliminates group differences in turning technique.•Future use of method includes informing training plans and equipment modifications.
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 20.5kg 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.
Details
- Title: Subtitle
- Inertial sensor and cluster analysis for discriminating agility run technique and quantifying changes across load
- Creators
- Ryan S McGinnis - University of VermontStephen M Cain - University of Michigan–Ann ArborSteven P Davidson - University of Michigan–Ann ArborRachel V Vitali - University of Michigan–Ann ArborScott G McLean - University of Michigan–Ann ArborNoel C Perkins - University of Michigan–Ann Arbor
- Resource Type
- Journal article
- Publication Details
- Biomedical signal processing and control, Vol.32, pp.150-156
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.bspc.2016.10.013
- ISSN
- 1746-8094
- eISSN
- 1746-8108
- Language
- English
- Date published
- 02/2017
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984196659902771
Metrics
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