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Related Experiment Video

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Design and Analysis for Fall Detection System Simplification
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Comparing fall detection methods in people with multiple sclerosis: A prospective observational cohort study.

Andrea Hildebrand1, Peter G Jacobs2, Jonathon G Folsom2

  • 1Department of Neurology, VA Portland Health Care System, Oregon Health and Science University, 3710 SW US Veterans Hospital Rd., Mail Code P3MSCOE, Portland, OR 97239, United States.

Multiple Sclerosis and Related Disorders
|September 25, 2021
PubMed
Summary

Accurate fall detection in multiple sclerosis (MS) is crucial. While automated devices detect more falls, paper calendars are more reliable, highlighting the need for improved fall counting methods in MS research.

Keywords:
Accidental fallsAmbulatory monitoringMultiple sclerosisSelf reportWearable electronic devices

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Falls are common in the general population but disproportionately affect individuals with multiple sclerosis (MS), leading to significant negative consequences.
  • Accurate measurement of fall frequency is essential for understanding disease impact and evaluating interventions in MS.
  • Existing fall detection methods, including paper fall calendars, require comparison with newer technologies for improved accuracy.

Purpose of the Study:

  • To compare the sensitivity and false discovery rates of three fall detection methods in people with MS.
  • To evaluate prospective paper fall calendars against real-time self-reporting and automated detection using a novel body-worn device.

Main Methods:

  • Twenty-five adults with MS participated in an eight-week study, concurrently using paper fall calendars, a body-worn device for self-reporting, and an automated body-worn fall detector.
  • Participants met specific criteria including a history of falls/near-falls and Expanded Disability Status Scale ≤ 6.0.
  • True falls were defined as those reported by at least two methods; false reports were those identified by only one method.

Main Results:

  • Across 1,276 person-days, 1,344 unique fall events were recorded, with only 8.5% classified as true falls.
  • Paper fall calendars demonstrated the lowest sensitivity (0.614) and false discovery rate (0.067).
  • The automated detector exhibited the highest sensitivity (0.921) but also the highest false discovery rate (0.919).

Conclusions:

  • Paper fall calendars may underestimate fall frequency in MS by approximately 40%.
  • The evaluated automated detector is highly sensitive but may overestimate fall events by about one per day.
  • Further research is necessary to develop optimal fall detection and counting methodologies for clinical and research applications in MS.