Assessment of knee joint motor disability improvement rate using kinect sensor

This study enhances disability recovery detection in knee osteoarthritis patients by evaluating a fuzzy system for diagnosing knee pain severity and tracking recovery. A kinect sensor analysed three-dimensional gait data from 48 patients, with 24 for training and 24 for testing. Based on expert-defined rules, the system categorised mobility impairments and aligned closely with clinical assessments. It provided real-time feedback, improved treatment personalisation, and reduced evaluation subjectivity. The results demonstrate its reliability for clinical integration, offering accurate diagnosis and recovery monitoring. This approach could enhance treatment outcomes and aid in managing knee joint disorders and motor disabilities.

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Publication Details

Journal
International Journal of Biomedical Engineering and Technology
Published
2025-01-01
DOI
https://doi.org/10.1504/ijbet.2025.149325
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Assessment of knee joint motor disability improvement rate using kinect sensor

Masoume Gholizade, Mohammad Zahraei, Hadi Soltanizadeh, Farhad Azadi et al.
International Journal of Biomedical Engineering and Technology
Hand Gesture Recognition Systems
article

Assessment of knee joint motor disability improvement rate using kinect sensor

Masoume Gholizade, Mohammad Zahraei, Hadi Soltanizadeh, Farhad Azadi, Shib Sankar Sana, Esmaeil Khaleghi
article en

Abstract

This study enhances disability recovery detection in knee osteoarthritis patients by evaluating a fuzzy system for diagnosing knee pain severity and tracking recovery. A kinect sensor analysed three-dimensional gait data from 48 patients, with 24 for training and 24 for testing. Based on expert-defined rules, the system categorised mobility impairments and aligned closely with clinical assessments. It provided real-time feedback, improved treatment personalisation, and reduced evaluation subjectivity. The results demonstrate its reliability for clinical integration, offering accurate diagnosis and recovery monitoring. This approach could enhance treatment outcomes and aid in managing knee joint disorders and motor disabilities.

International Journal of Biomedical Engineering and TechnologyVol. 49(1)
Visva-Bharati University (IN), Sina Hospital (IR), University of Social Welfare and Rehabilitation Sciences (IR), Semnan University (IR)
Openalex Percentile: Top 69%
Hand Gesture Recognition Systems
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