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Posturography stability score generation for stroke patient using Kinect: Fuzzy based approach.

Oishee Mazumder, Kingshuk Chakravarty, Debatri Chatterjee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
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    Summary

    This study introduces a novel fuzzy logic-based posturography stability score for stroke patients. This automated, cost-effective method accurately assesses stability and predicts fall risk, aiding tele-rehabilitation.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Fuzzy Logic Systems

    Background:

    • Postural instability is a significant symptom in stroke and other neurological conditions, increasing fall risk.
    • Current methods for assessing postural stability often require manual intervention and expert supervision.
    • There is a need for objective, automated, and cost-effective tools for stability assessment, particularly for remote rehabilitation.

    Purpose of the Study:

    • To develop and validate a novel fuzzy logic-based posturography stability score for stroke patients.
    • To create an automated, non-invasive, and cost-effective method for assessing static postural stability.
    • To establish a tool suitable for tele-rehabilitation and fall risk prediction.

    Main Methods:

    • Utilized Kinect sensor technology to acquire posturography features during Single Limb Stance (SLS) exercises.
    • Calculated key features: SLS duration, vibration index (mean vibration of 20 joints), and Center of Mass (CoM) sway area.
    • Developed a fuzzy rule base to generate a static stability score based on feature variations.

    Main Results:

    • The proposed fuzzy stability score demonstrated reliability, validated by One-Way Analysis of Variance (ANOVA) comparing stroke patients and healthy individuals.
    • The generated fuzzy scores showed comparability with established tools like the Berg Balance Scale and Johns Hopkins fall risk assessment.
    • The study confirmed the score's utility as an index of overall stability and a predictor of fall risk.

    Conclusions:

    • The novel fuzzy logic-based posturography score offers an objective, automated, and cost-effective solution for assessing static stability in stroke patients.
    • This scoring system is well-suited for tele-rehabilitation applications, overcoming limitations of traditional assessment methods.
    • The developed score serves as a valuable tool for both evaluating current stability and predicting future fall risk.