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Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset.

Soonjae Ahn1, Jongman Kim2, Bummo Koo3

  • 1Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea. asj8652@yonsei.ac.kr.

Sensors (Basel, Switzerland)
|February 21, 2019
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New fall detection algorithms using inertial measurement units (IMUs) show 100% accuracy in identifying falls before impact. Angle-based algorithms offer improved detection, especially for elderly individuals.

Keywords:
ADLsIMUfall detection algorithmfallslead timepublic dataset

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Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Gerontology

Background:

  • Falls are a major health risk, particularly for the elderly.
  • Accurate and timely fall detection is crucial for effective intervention.
  • Existing fall detection systems often lack pre-impact detection capabilities.

Purpose of the Study:

  • To develop and validate pre-impact fall detection algorithms using inertial measurement unit (IMU) data.
  • To compare the performance of two distinct algorithms: one based on vertical angles (VA) and another on a triangle feature (TF).
  • To assess the algorithms' accuracy and lead time in detecting simulated falls.

Main Methods:

  • Developed custom pre-impact fall detection algorithms utilizing IMU data (acceleration and angular velocity).
  • Collected data from 40 healthy subjects performing simulated falls and activities of daily living (ADL).
  • Validated algorithms using blind testing and a public fall dataset (SisFall), analyzing sensitivity and specificity.

Main Results:

  • Both VA and TF algorithms achieved 100% accuracy in detecting simulated falls with significant pre-impact lead times (401 ± 46.9 ms and 427 ± 45.9 ms, respectively).
  • On the SisFall dataset, both algorithms demonstrated 100% sensitivity, with the TF algorithm showing higher specificity (83.9%) than the VA algorithm (78.3%).
  • Algorithm specificity improved when interpreting data from elderly subjects.

Conclusions:

  • Pre-impact fall detection algorithms, particularly those using angle-based calculations, can accurately identify falls before impact.
  • The developed algorithms show promise for enhancing fall prevention and response systems.
  • Further validation with larger, diverse datasets, including those from elderly populations, is recommended to improve algorithm robustness.