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Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review

F. Xavier Gaya-Morey, Cristina Manresa-Yee, Jose M. Buades-Rubio

TL;DR

This systematic review examines the literature pertaining to fall detection and Human Activity Recognition for the elderly, two critical tasks for ensuring their safety when living alone, and emphasizes the utilization of Deep Learning approaches on computer vision data, reflecting current trends in the field.

Abstract

As the percentage of elderly people in developed countries increases worldwide, the healthcare of this collective is a worrying matter, especially if it includes the preservation of their autonomy. In this direction, many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data. In addition, different collections of data like DL models, datasets or hardware (e.g. depth or thermal cameras) are gathered from the reviewed studies and provided for reference in future studies. Strengths and weaknesses of existing approaches are also discussed and, based on them, our recommendations for future works are provided.

Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review

TL;DR

This systematic review examines the literature pertaining to fall detection and Human Activity Recognition for the elderly, two critical tasks for ensuring their safety when living alone, and emphasizes the utilization of Deep Learning approaches on computer vision data, reflecting current trends in the field.

Abstract

As the percentage of elderly people in developed countries increases worldwide, the healthcare of this collective is a worrying matter, especially if it includes the preservation of their autonomy. In this direction, many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data. In addition, different collections of data like DL models, datasets or hardware (e.g. depth or thermal cameras) are gathered from the reviewed studies and provided for reference in future studies. Strengths and weaknesses of existing approaches are also discussed and, based on them, our recommendations for future works are provided.
Paper Structure (21 sections, 9 figures, 7 tables)

This paper contains 21 sections, 9 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Review Questions
  4. Review Methods
  5. Data sources
  6. Search strategy
  7. Study selection
  8. Quality assessment
  9. Data extraction and synthesis
  10. Results
  11. RQ1: Fall detection and Human Activity Recognition
  12. RQ1.1: data type
  13. RQ1.2: DL models
  14. RQ1.3: Datasets
  15. RQ2: Framework integration
  16. ...and 6 more sections

Figures (9)

  • Figure 1: Number of publications obtained from each database, before duplicate removal and study selection (2616 in total).
  • Figure 2: Accumulated publications from 2013 to 2023 (both included) after study selection and quality assessment.
  • Figure 3: Articles collected at each phase of the systematic search process, including acquisition from various sources, removal of duplicates, and study selection, which also involved quality assessment.
  • Figure 4: Distribution of studies by target task: fall detection, HAR or both.
  • Figure 5: Distribution of studies by data type used. Note that more than one data type was used in some studies.
  • ...and 4 more figures