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Design and Analysis for Fall Detection System Simplification
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Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
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    Summary

    This study presents a wearable pre-fall alarm system that accurately detects falls in elderly individuals using motion data. The reliable system offers timely assistance, enhancing safety for seniors living alone.

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    Area of Science:

    • Biomedical Engineering
    • Gerontology
    • Wearable Technology

    Background:

    • Accidental falls pose significant health risks, particularly for elderly individuals living alone, necessitating timely intervention systems.
    • Existing fall detection methods often lack the accuracy and reliability required for effective elderly care.
    • The development of proactive health monitoring solutions is crucial for improving the quality of life and safety of the aging population.

    Purpose of the Study:

    • To propose and evaluate a pre-fall alarm system designed for early detection of falling events in elderly individuals.
    • To assess the reliability and accuracy of a novel fall detection algorithm integrated into a wearable device.
    • To explore the potential of the system in monitoring physical function and providing timely assistance to prevent fall-related injuries.

    Main Methods:

    • Development of a wearable device to capture acceleration and angular velocity time series data during human motion.
    • Utilizing a Hidden Markov Model (HMM)-based Support Vector Machine (SVM) classifier to distinguish between falls and Activities of Daily Living (ADLs).
    • Conducting experiments with eighteen subjects wearing the device to collect motion data for algorithm training and validation.

    Main Results:

    • The fall detection system achieved a high accuracy rate of 94.91%.
    • The system demonstrated excellent performance with 97.22% Sensitivity and 93.75% Specificity in fall event recognition.
    • The proposed device is characterized by its small size, low power consumption, and ability to accurately recognize falls.

    Conclusions:

    • The developed pre-impact fall alarm system effectively detects falls with high accuracy, offering a promising solution for elderly safety.
    • The system's ability to recognize fall events and its advantageous features (small size, low power) make it suitable for continuous monitoring.
    • This technology holds significant potential for monitoring the physical function of the elderly population and enabling prompt assistance, thereby reducing fall-related complications.