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Design and Analysis for Fall Detection System Simplification
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Review of fall detection techniques: A data availability perspective.

Shehroz S Khan1, Jesse Hoey1

  • 1David R. Cheriton School of Computer Science, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada.

Medical Engineering & Physics
|November 28, 2016
PubMed
Summary
This summary is machine-generated.

Fall detection is challenging due to rare occurrences and limited data. This study proposes a data-availability-focused taxonomy for fall detection methods, highlighting anomaly detection as a promising research direction.

Keywords:
Anomaly detectionCost-sensitive learningFall detectionOne-class classificationOutlier detection

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Falls are rare abnormal events with significant health implications.
  • Limited training data for falls hinders standard supervised machine learning.
  • Existing fall detection methods face challenges due to data scarcity.

Purpose of the Study:

  • To propose a novel taxonomy for fall detection research based on data availability.
  • To categorize existing fall detection classification methods according to data requirements.
  • To identify promising research avenues for improved fall detection systems.

Main Methods:

  • Developed a taxonomy for fall detection independent of sensor type or feature extraction.
  • Classified fall detection methods based on data availability during classifier training.
  • Conducted a comprehensive literature review within the proposed taxonomy categories.

Main Results:

  • The proposed taxonomy categorizes fall detection approaches based on data availability.
  • Identified treating falls as abnormal activities (anomaly detection) as a viable research direction.
  • Highlighted the limitations of standard supervised learning due to data rarity.

Conclusions:

  • A data-centric taxonomy provides a new perspective on fall detection research.
  • Anomaly detection offers a promising approach to address data scarcity in fall detection.
  • Further research is needed to address open challenges in robust fall detection.