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Vision-based human fall detection systems using deep learning: A review.

Ekram Alam1, Abu Sufian2, Paramartha Dutta3

  • 1Department of Computer Science, Gour Mahavidyalaya, West Bengal, India.

Computers in Biology and Medicine
|June 9, 2022
PubMed
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Human fall detection is crucial for elder care. This review explores deep learning vision-based techniques for non-intrusive fall detection, analyzing datasets and performance metrics for improved assistive living.

Area of Science:

  • Computer Science
  • Gerontology
  • Biomedical Engineering

Background:

  • Human falls pose significant health risks, particularly for elderly and disabled individuals living alone.
  • The global elder population is growing, increasing the need for effective assistive living technologies.
  • Non-intrusive monitoring systems are essential for maintaining independence and safety in home environments.

Purpose of the Study:

  • To review state-of-the-art deep learning (DL)-based, vision-based human fall detection techniques.
  • To provide a survey of benchmark datasets used for evaluating fall detection systems.
  • To discuss performance metrics and future directions in vision-based fall detection.

Main Methods:

  • Comprehensive literature review of deep learning algorithms applied to human fall detection.
Keywords:
AccuracyFall Detection MetricsHuman Fall DatasetsHuman Fall DetectionLe2i Fall Detection DatasetMultiple Camera Fall DatasetSensitivitySpecificityURFD

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  • Analysis and categorization of publicly available fall detection benchmark datasets.
  • Discussion of evaluation metrics including accuracy, sensitivity, and specificity.

    • Main Results:

    • Identified key DL architectures and approaches for vision-based fall detection.
    • Summarized characteristics and limitations of prominent fall detection datasets.
    • Highlighted the importance of appropriate metrics for system performance assessment.

    Conclusions:

    • Deep learning-based vision systems offer promising non-intrusive solutions for human fall detection.
    • Standardized datasets and evaluation metrics are critical for advancing the field.
    • Future research should focus on improving robustness and real-world applicability of these systems.