Open AccessBody-worn IMU array reveals effects of load on performance in an outdoor obstacle course
Author(s) -
Rachel V. Vitali,
Stephen M. Cain,
Lauro Ojeda,
Michael V. Potter,
Antonia M. Zaferiou,
Steven P. Davidson,
Megan Coyne,
Clifford L. Hancock,
Alyssa Mendoza,
Leia Stirling,
Noel C. Perkins
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0214008
Subject(s) - obstacle , inertial measurement unit , computer science , wearable computer , task (project management) , wearable technology , units of measurement , physical medicine and rehabilitation , simulation , medicine , artificial intelligence , embedded system , engineering , systems engineering , physics , quantum mechanics , political science , law
This study introduces a new method to understand how added load affects human performance across a broad range of athletic tasks (ten obstacles) embedded in an outdoor obstacle course. The method employs an array of wearable inertial measurement units (IMUs) to wirelessly record the movements of major body segments to derive obstacle-specific metrics of performance. The effects of load are demonstrated on (N = 22) participants who each complete the obstacle course under four conditions including unloaded (twice) and with loads of 15% and 30% of their body weight (a total of 88 trials across the group of participants). The IMU-derived performance metrics reveal marked degradations in performance with increasing load across eight of the ten obstacles. Overall, this study demonstrates the significant potential in using this wearable technology to evaluate human performance across multiple tasks and, simultaneously, the adverse effects of body-borne loads on performance. The study addresses a major need of military organizations worldwide that frequently employ standardized obstacle courses to understand how added loads influence warfighter performance. Importantly, the findings and conclusions drawn from IMU data would not be possible using traditional timing metrics used to evaluate task performance.