Towards The Development of A Wearable Feedback System for Monitoring the Activities of the Upper-Extremities

Resource type
Date created
2014
Authors/Contributors
Author: Xiao, Zhen
Author: Menon, Carlo
Abstract
BackgroundBody motion data registered by wearable sensors can provide objective feedback to patients on the effectiveness of the rehabilitation interventions they undergo. Such a feedback may motivate patients to keep increasing the amount of exercise they perform, thus facilitating their recovery during physical rehabilitation therapy. In this work, we propose a novel wearable and affordable system which can predict different postures of the upper-extremities by classifying force myographic (FMG) signals of the forearm in real-time.MethodsAn easy to use force sensor resistor (FSR) strap to extract the upper-extremities FMG signals was prototyped. The FSR strap was designed to be placed on the proximal portion of the forearm and capture the activities of the main muscle groups with eight force input channels. The non-kernel based extreme learning machine (ELM) classifier with sigmoid based function was implemented for real-time classification due to its fast learning characteristics. A test protocol was designed to classify in real-time six upper-extremities postures that are needed to successfully complete a drinking task, which is a functional exercise often used in constraint-induced movement therapy. Six healthy volunteers participated in the test. Each participant repeated the drinking task three times. FMG data and classification results were recorded for analysis.ResultsThe obtained results confirmed that the FMG data captured from the FSR strap produced distinct patterns for the selected upper-extremities postures of the drinking task. With the use of the non-kernel based ELM, the postures associated to the drinking task were predicted in real-time with an average overall accuracy of 92.33% and standard deviation of 3.19%.ConclusionsThis study showed that the proposed wearable FSR strap was able to detect eight FMG signals from the forearm. In addition, the implemented ELM algorithm was able to correctly classify in real-time six postures associated to the drinking task. The obtained results therefore point out that the proposed system has potential for providing instant feedback during functional rehabilitation exercises.
Document
Published as
Journal of NeuroEngineering and Rehabilitation 2014, 11:2 doi:10.1186/1743-0003-11-2The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/11/1/2
Publication title
Journal of NeuroEngineering and Rehabilitation
Document title
Towards The Development of A Wearable Feedback System for Monitoring the Activities of the Upper-Extremities
Date
2014
Volume
11
Publisher DOI
10.1186/1743-0003-11-2
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Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
Member of collection
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1743-0003-11-2.pdf 2.35 MB