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research-article

Measurement and Validation of Exercise-induced Fatigue Through Inertial Motion Analysis

[+] Author and Article Information
Sina Ameli

School of Electrical, Computer & Telecommunication (SECTE), Faculty of Engineering, University of Wollongong
sameli@uow.edu.au

Fazel Naghdy

School of Electrical, Computer & Telecommunication (SECTE), Faculty of Engineering, University of Wollongong
fazel@uow.edu.au

David Stirling

School of Electrical, Computer & Telecommunication (SECTE), Faculty of Engineering, University of Wollongong
stirling@uow.edu.au

Golshah Naghdy

School of Electrical, Computer & Telecommunication (SECTE), Faculty of Engineering, University of Wollongong
golshah@uow.edu.au

Morteza Aghmesheh

Wollongong hospital, Illawarra Cancer Care Centre
Morteza.Aghmesheh@SESIAHS.HEALTH.NSW.GOV.AU

Ryan Anthony

Graduate School of Medicine, Faculty of Science, Medicine and Health (SMAH), University of Wollongong
ranthony.nps@gmail.com

Peter McLennan

Graduate School of Medicine, Faculty of Science, Medicine and Health (SMAH), University of Wollongong
petermcl@uow.edu.au

Gregory Peoples

Graduate School of Medicine, Faculty of Science, Medicine and Health (SMAH), University of Wollongong
peoples@uow.edu.au

1Corresponding author.

ASME doi:10.1115/1.4039211 History: Received September 10, 2017; Revised January 30, 2018

Abstract

Objective: Exercise-induced fatigue evolves from the initiation of physical work. Nonetheless, the development of an objective method for detecting fatigue based on variation in ambulatory motion parameters measured during exercise is yet to be explored. Approach: In this study, the ambulatory motion parameters consisting of kinematic parameters of 23 body segments in addition to muscle tissue oxygen saturation (SmO2), heart rate and vertical work of 8 healthy male subjects during Stair Climbing Tests (SCT) were measured before and after a fatigue protocol utilizing Wingate cycling test. The impacts of fatigue on ambulatory motion and postural behaviors were analyzed using an unsupervised machine learning method classifying angular joint motions. Main results: The average of total distance travelled by subjects and the overall body postural behavior showed about 25% decline and 90% variation after fatigue protocol, respectively. Also, higher relative desaturation in SCT1 -64.0 (1.1) compared SCT2 -54.8 (1.1) was measured. Significance: Measurements of differences in metabolic indexes and motion postural states proved a strong correlation.

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