Identification of walking strategies of people With osteoarthritis of the knee using insole pressure sensors

Journal article


Munoz-Organero, Mario, Littlewood, Chris, Parker, Jack, Powell, Lauren, Grindell, Cheryl and Mawson, Sue 2017. Identification of walking strategies of people With osteoarthritis of the knee using insole pressure sensors. IEEE Sensors. 17 (12), pp. 3909-3920. https://doi.org/10.1109/jsen.2017.2696303
AuthorsMunoz-Organero, Mario, Littlewood, Chris, Parker, Jack, Powell, Lauren, Grindell, Cheryl and Mawson, Sue
Abstract

Insole pressure sensors capture the different forces exercised over the different parts of the sole when performing tasks standing up. Using data analysis and machine learning techniques, common patterns and strategies from different users to execute different tasks can be extracted. In this paper, we present the evaluation results of the impact that clinically diagnosed osteoarthritis of the knee at early stages has on insole pressure sensors while walking at normal speeds focusing on the effects caused at points, where knee forces tend to peak for normal users. From the different parts of the foot affected at high knee force moments, the forefoot pressure distribution and the heel to forefoot weight reallocation strategies have shown to provide better correlations with the user's perceived pain in the knee for OA users with mild knee pain. This paper shows how the time differences and variabilities from two sensors located in the metatarsal zone while walking provide a simple mechanism to detect different strategies used by users suffering OA of the knee from control users with no knee pain. The weight dynamic reallocation at the midfoot, when moving forward from heel to forefoot, has also shown to positively correlate with the perceived knee pain. The major asymmetries between pressure patterns in both feet while walking at normal speeds are also captured. Based on the described features, automatic evaluation self-management rehabilitation tools could be implemented to continuously monitor and provide personalized feedback for OA patients with mild knee pain to facilitate user adherence to individualized OA rehabilitation.

KeywordsInsole pressure sensors , mild knee pain , osteoarthritis , machine learning , classification
Year2017
JournalIEEE Sensors
IEEE Sensors Journal
Journal citation17 (12), pp. 3909-3920
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN1530-437X
2379-9153
Digital Object Identifier (DOI)https://doi.org/10.1109/jsen.2017.2696303
Web address (URL)http://hdl.handle.net/10545/624986
hdl:10545/624986
Publication dates15 Jun 2017
Publication process dates
Deposited13 Jul 2020, 13:46
Accepted2017
ContributorsCharles III University of Madrid, Madrid, Spain, Keele University and University of Sheffield
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