Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study
|Title:||Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study||Authors:||Bevilacqua, Antonio; Huang, Bingquan; Argent, Rob; Caulfield, Brian; Kechadi, Tahar||Permanent link:||http://hdl.handle.net/10197/9947||Date:||5-Apr-2018||Online since:||2019-04-15T10:16:38Z||Abstract:||Inertial measurement units have the ability to accurately record the acceleration and angular velocity of human limb segments during discrete joint movements. These movements are commonly used in exercise rehabilitation programmes following orthopaedic surgery such as total knee replacement. This provides the potential for a biofeedback system with data mining technique for patients undertaking exercises at home without physician supervision. We propose to use machine learning techniques to automatically analyse inertial measurement unit data collected during these exercises, and then assess whether each repetition of the exercise was executed correctly or not. Our approach consists of two main phases: signal segmentation, and segment classification. Accurate pre-processing and feature extraction are paramount topics in order for the technique to work. In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity. The results obtained from experimental datasets of both clinical and healthy subjects, for a set of 4 knee exercises commonly used in rehabilitation, are very promising.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2018 IEEE||Keywords:||Feature extraction; Biological control systems; Knee; Surgery; Androids; Humanoid robots; Measurement units||DOI:||10.1109/BSN.2018.8329649||Other versions:||https://bhi-bsn.embs.org/2018/
|Language:||en||Status of Item:||Peer reviewed||Is part of:||2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)||Conference Details:||IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 4-7 March 2018, Las Vegas, Nevada, USA||ISBN:||978-1-5386-1110-4||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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