amsys 16(10): e2

Research Article

Muscle Strength Testing using Wearable Wireless Sensors

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  • @ARTICLE{10.4108/eai.28-9-2015.2261461,
        author={DK Arvind and Debadri Mukherjee},
        title={Muscle Strength Testing using Wearable Wireless Sensors},
        journal={EAI Endorsed Transactions on Ambient Systems},
        volume={3},
        number={10},
        publisher={EAI},
        journal_a={AMSYS},
        year={2015},
        month={12},
        keywords={muscle strength testing, orient specks, classification, k-nearest neighbours},
        doi={10.4108/eai.28-9-2015.2261461}
    }
    
  • DK Arvind
    Debadri Mukherjee
    Year: 2015
    Muscle Strength Testing using Wearable Wireless Sensors
    AMSYS
    EAI
    DOI: 10.4108/eai.28-9-2015.2261461
DK Arvind1,*, Debadri Mukherjee1
  • 1: University of Edinburgh
*Contact email: dka@inf.ed.ac.uk

Abstract

Manual muscle testing and its variants have a long history of use for classifying muscle strengths. For the first time, inexpensive wearable wireless sensors combined with machine learning techniques are used to classify different levels of muscle strength, which addresses some limitations of the manual method. A mean accuracy of 93% was obtained across ten subjects using gyroscope and accelerometer data in classifying four distinct levels of strengths of the biceps brachii muscle when performing muscle contraction under appropriate load. This was reduced by 2% for accelerometer-only data, thus offering a potentially inexpensive and viable solution for testing muscle strength.