The Limb Movement Analysis of Rehabilitation Exercises using Wearable Inertial Sensors
|Title:||The Limb Movement Analysis of Rehabilitation Exercises using Wearable Inertial Sensors||Authors:||Huang, Bingquan; Giggins, Oonagh M.; Kechadi, Tahar; Caulfield, Brian||Permanent link:||http://hdl.handle.net/10197/7857||Date:||20-Aug-2016||Online since:||2016-09-02T12:32:23Z||Abstract:||Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2016 IEEE||Keywords:||Personal sensing; Exercise therapy||DOI:||10.1109/EMBC.2016.7591773||Other versions:||http://embc.embs.org/2016/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)||Conference Details:||38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Florida, United States of America, 16-20 August 2016||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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