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Automated Analysis and Quantification of Human Mobility Using a Depth Sensor.

Daniel Leightley, Jamie S McPhee, Moi Hoon Yap

    IEEE Journal of Biomedical and Health Informatics
    |June 3, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework using Kinect One to automatically detect and assess human mobility impairments. It compares patient movements to models of healthy individuals, aiding in personalized rehabilitation strategies.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Science
    • Computer Science

    Background:

    • Clinical decision-making often requires objective human motion analysis.
    • Developing representative statistical models of human mobility without human interpretation is challenging.
    • Current methods may lack unbiased quantification of mobility impairments.

    Purpose of the Study:

    • To propose a framework for automatic recognition and evaluation of human mobility impairments.
    • To utilize the Microsoft Kinect One depth sensor for motion analysis.
    • To provide clinically relevant feedback for stratified rehabilitation.

    Main Methods:

    • Implementing a two-part framework: motion recognition and temporal evaluation.
    • Employing abstract feature representation and machine learning for motion recognition (e.g., sit-to-stand, walking).
    • Generating a statistical mobility model from healthy individuals' movements for comparison.

    Main Results:

    • Demonstrated the framework's ability to recognize specific human motions.
    • Successfully evaluated motion sequences against a statistical mobility model.
    • Developed an automatic method for unbiased labeling of motion capture data.
    • Provided clinically relevant feedback highlighting mobility concerns.

    Conclusions:

    • The proposed framework offers an automated and unbiased approach to assess human mobility impairments.
    • This technology supports clinicians in decision-making for personalized rehabilitation.
    • Enables stratified rehabilitation pathways and clinician-led interventions based on objective data.