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.
|Title of host publication||BodyNets '15 Proceedings of the 10th EAI International Conference on Body Area Networks|
|Publisher||ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)|
|Number of pages||4|
|Publication status||Published - 2015|