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A Neural Network for Stance Phase Detection in Smart Cane Users
University of Malaga, Malaga, Spain.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
University of Malaga, Malaga, Spain.
University of Malaga, Malaga, Spain.
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2019 (English)In: Lecture Notes in Computer Science, vol 11506, Springer Verlag , 2019, p. 310-321Conference paper, Published paper (Refereed)
Abstract [en]

Persons with disabilities often rely on assistive devices to carry on their Activities of Daily Living. Deploying sensors on these devices may provide continuous valuable knowledge on their state and condition. Canes are among the most frequently used assistive devices, regularly employed for ambulation by persons with pain on lower limbs and also for balance. Load on canes is reportedly a meaningful condition indicator. Ideally, it corresponds to the time cane users support weight on their lower limb (stance phase). However, in reality, this relationship is not straightforward. We present a Multilayer Perceptron to reliably predict the Stance Phase in cane users using a simple support detection module on commercial canes. The system has been successfully tested on five cane users in care facilities in Spain. It has been optimized to run on a low cost microcontroller. 

Place, publisher, year, edition, pages
Springer Verlag , 2019. p. 310-321
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11506
Keywords [en]
Gait analysis, Multilayer Perceptron, Phase detection, Smart cane
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-44663DOI: 10.1007/978-3-030-20521-8_26Scopus ID: 2-s2.0-85067487178ISBN: 9783030205201 (print)OAI: oai:DiVA.org:mdh-44663DiVA, id: diva2:1331705
Conference
15th International Work-Conference on Artificial Neural Networks, IWANN 2019; Gran Canaria; Spain; 12 June 2019 through 14 June 2019
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-07-11Bibliographically approved

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CiteExportLink to record
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