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A Textile Embedded Wearable Device for Movement Disorders Quantification.

Ana Oliveira, Duarte Dias, Elodie Murias Lopes

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new wearable device for objective assessment of movement disorders like Parkinson's Disease. The comfortable, textile-embedded device accurately quantifies motor symptoms, improving patient follow-up and treatment.

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    Area of Science:

    • Biomedical Engineering
    • Neurology
    • Wearable Technology

    Background:

    • Wearable devices are increasingly used in healthcare for objective assessment.
    • Current movement disorder evaluation relies on subjective clinical scales.
    • Objective quantification of motor symptoms can enhance disease management.

    Purpose of the Study:

    • To present a novel, low-power, textile-embedded wearable device for movement disorder assessment.
    • To improve the objective quantification of motor symptoms in Parkinson's Disease.
    • To develop a user-friendly system for seamless data collection and analysis.

    Main Methods:

    • Development of a comfortable, versatile wearable device with a 9-axis inertial sensing unit.
    • Integration of the wearable device with a web platform for data visualization and recording.
    • Validation of inertial sensor data (accelerometer, gyroscope) against theoretical behavior.

    Main Results:

    • The developed wearable device demonstrated high comfort and ease-of-use.
    • Validation tests confirmed the reliability and accuracy of the collected inertial data.
    • The system, named iHandU, showed improved wrist rigidity quantification for Parkinson's Disease patients.

    Conclusions:

    • The novel wearable device offers a promising solution for objective motor symptom assessment in movement disorders.
    • This technology facilitates accurate disease progression monitoring and personalized treatment strategies.
    • The seamless integration of hardware and software enables efficient data management for clinical applications.