An autonomous wearable system for predicting and detecting localised muscle fatigue

Mohamed R Al-Mulla, Francisco Sepulveda, Martin Colley, Mohamed R Al-Mulla, Francisco Sepulveda, Martin Colley

Abstract

Muscle fatigue is an established area of research and various types of muscle fatigue have been clinically investigated in order to fully understand the condition. This paper demonstrates a non-invasive technique used to automate the fatigue detection and prediction process. The system utilises the clinical aspects such as kinematics and surface electromyography (sEMG) of an athlete during isometric contractions. Various signal analysis methods are used illustrating their applicability in real-time settings. This demonstrated system can be used in sports scenarios to promote muscle growth/performance or prevent injury. To date, research on localised muscle fatigue focuses on the clinical side and lacks the implementation for detecting/predicting localised muscle fatigue using an autonomous system. Results show that automating the process of localised muscle fatigue detection/prediction is promising. The autonomous fatigue system was tested on five individuals showing 90.37% accuracy on average of correct classification and an error of 4.35% in predicting the time to when fatigue will onset.

Keywords: classification; feature extraction; muscle fatigue; sEMG.

Figures

Figure 1.
Figure 1.
An overview of the hardware architecture and the actual system components. (a) The Autonomous fatigue detection and prediction system setup; (b) System Components.
Figure 2.
Figure 2.
Subject During a trial. (a) Subject during a testing trial, showing the subject with the autonomous device; (b) During a testing trial.
Figure 3.
Figure 3.
The use of elbow angle to label and classify the signal.
Figure 4.
Figure 4.
The use of angular oscillation to label and classify the signal.
Figure 5.
Figure 5.
The 1-D Spectro feature, illustrating the onset of Transition To-Fatigue by the fuzzy classifier.
Figure 6.
Figure 6.
Illustration indicating were in time the onset of Tranition-To-Fatigue and the onset of Fatigue occurred during a trial.
Figure 7.
Figure 7.
Classification over time during one of the testing trial.

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