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As people live longer, more elderly individuals require care. This study surveys machine learning-based fall detection systems (FDSs) for geriatric healthcare, analyzing their requirements and challenges.

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Area of Science:

  • Geriatric Healthcare
  • Medical Technology
  • Machine Learning Applications

Background:

  • Increased life expectancy leads to a growing elderly population needing care.
  • Falls are a significant risk for older adults, especially in care settings.
  • Machine learning (ML) offers potential solutions for improving geriatric healthcare, particularly in fall detection.

Purpose of the Study:

  • To examine the requirements of a typical fall detection system (FDS).
  • To survey recent advancements in ML-based FDSs.
  • To analyze the challenges associated with current FDS systems.

Main Methods:

  • Literature review of existing fall detection systems.
  • Analysis of machine learning applications in FDS.
  • Examination of FDS requirements and identified challenges.

Main Results:

  • The study identifies key requirements for effective fall detection systems.
  • A comprehensive survey of ML techniques applied to FDS is presented.
  • Significant challenges in the development and implementation of FDS were analyzed.

Conclusions:

  • Machine learning plays a crucial role in the advancement of geriatric fall detection systems.
  • Addressing identified challenges is essential for improving the efficacy and adoption of FDS.
  • Further research is needed to optimize ML-based FDS for enhanced elderly care.