Smart insoles-based gait symmetry detection for people with lower-limb amputation

Luigi D'Arco, Haiying Wang, Carolyn Wilson, Ezio Preatoni, Elena Seminati, Grant Trewartha, Jill Cundell, Huiru Zheng

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

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Abstract

Lower limb prostheses offer mobility restoration to individuals who underwent amputation, yet they often introduce movement alterations that can affect physical health over time. While monitoring and understanding these alterations are crucial for designing tailored rehabilitation plans, existing technologies are primarily confined to clinical settings and lack representation of real-world mobility scenarios. This study investigates the use of smart insoles as a cost-effective means to assess walking symmetry and effectiveness in individuals with prostheses. Ten participants, including six lower-limb prosthesis users and four healthy subjects, were recruited to compare gait parameters and symmetry during a 2-minute walking test. The proposed methodology involves employing a Finite State Machine (FSM) to extract gait phases and subsequent kinematic and kinetic parameters. States of the FSM correspond to gait subphases, while transitions are managed by a fuzzy c-means clustering model. The solution demonstrated robust step count recognition, with an error rate of 1.24%. Additionally, when benchmarked against the GAITRite mat, a commonly used device for gait analysis, a mean absolute error of 0.05 seconds was identified in terms of stride time. Comparison between prosthetic and healthy subjects revealed distinct patterns. Specifically, primary differences have been identified in the symmetry of stance and swing times, where healthy subjects exhibited a higher symmetry percentage, with values of 93.75% and 92.95% respectively, against percentages of 88.82% and 83.05% for prosthetic subjects. These findings underscore the potential of smart insoles for ubiquitous monitoring of walking dynamics in daily life. By facilitating the early detection of asymmetries and anomalies, this study lays the foundations for the development of future solutions aimed at improving the quality of life of lower limb prosthesis users by sharing these insights with healthcare professionals who can define tailored rehabilitation strategies.
Original languageEnglish
Title of host publicationProceedings of the 35th Irish Systems and Signals Conference, ISSC 2024
Subtitle of host publication35th Irish Signals and Systems Conference, ISSC 2024 (2024). Belfast, UK, June 13-14, 2024
EditorsHuiru Zheng, Ian Cleland, Adrian Moore, Haiying Wang, David Glass, Joe Rafferty, Raymond Bond, Jonathan Wallace
Pages1-7
Number of pages7
ISBN (Electronic)9798350352986
DOIs
Publication statusPublished - 29 Jul 2024
EventIrish Signals and Systems Conference - Belfast, UK United Kingdom
Duration: 13 Jun 202414 Jun 2024
https://issc.ie/index.html

Publication series

NameProceedings of the 35th Irish Systems and Signals Conference, ISSC 2024

Conference

ConferenceIrish Signals and Systems Conference
Country/TerritoryUK United Kingdom
CityBelfast
Period13/06/2414/06/24
Internet address

Funding

This research has been supported by the EPSRC Rehabilitation Technologies Network grant agreement No. 20001397. The authors would like to thank the NI Limbless Association for helping in the recruitment process and the Belfast Disability Service for useful clinal discussion.

FundersFunder number
NI Limbless Association
EPSRC Rehabilitation Technologies Network20001397

    Keywords

    • Amputee
    • Finite State Machine
    • Gait Analysis
    • Gait Symmetry
    • Smart Insoles

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Artificial Intelligence
    • Computer Networks and Communications
    • Information Systems
    • Signal Processing
    • Safety, Risk, Reliability and Quality
    • Control and Optimization

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