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    This study estimates elderly age groups using postural sway analysis from video footage to assess fall risk. Early intervention through this method can help prevent injuries and promote health.

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    Area of Science:

    • Gerontology
    • Biomedical Engineering
    • Computer Vision

    Background:

    • Human postural sway, essential for balance, changes with aging, increasing fall risk.
    • Altered sway patterns reflect neurological and physical health changes in older adults.
    • Falls in the elderly lead to significant injuries and healthcare costs.

    Purpose of the Study:

    • To develop a method for estimating elderly age groups using postural sway measurements.
    • To utilize estimated age and sway data for a personalized fall risk assessment.
    • To enable early interventions and preventative measures against falls in the elderly population.

    Main Methods:

    • Extracting postural sway signals from video footage.
    • Computing sway measurements using machine learning techniques.
    • Correlating estimated age groups with real age and fall risk factors.

    Main Results:

    • The proposed method reliably estimates age groups of elderly individuals based on sway patterns.
    • The system successfully defines fall risk by integrating estimated age and sway data.
    • Experimental results demonstrate the method's effectiveness as a fall risk analysis tool.

    Conclusions:

    • Postural sway analysis from video is a viable tool for age estimation in the elderly.
    • This technology can serve as an early warning system for fall risk.
    • The findings support the development of targeted interventions to enhance elderly safety and well-being.