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. 2021 Jul 29;21(15):5134.
doi: 10.3390/s21155134.

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Sara Usmani  1 , Abdul Saboor  2 , Muhammad Haris  1 , Muneeb A Khan  3 , Heemin Park  3
Affiliations

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Sara Usmani et al. Sensors (Basel). .

Abstract

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

Keywords: fall detection; fall prevention; machine learning; review paper.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
An Overview of Fall Detection and Prevention System.
Figure 2
Figure 2
Procedure for fall detection and prevention.
Figure 3
Figure 3
Support vector machine algorithm.
Figure 4
Figure 4
Artificial neural network algorithm.
Figure 5
Figure 5
Random Forest Algorithm.
Figure 6
Figure 6
Proposed Methodology.
Figure 7
Figure 7
Methods of Data Collection.
Figure 8
Figure 8
Age of Participants.
Figure 9
Figure 9
Types of Sensors.
Figure 10
Figure 10
Placement of Sensors.
Figure 11
Figure 11
Number of Sensors.
Figure 12
Figure 12
Use of Multiple or single machine learning algorithms.
Figure 13
Figure 13
Frequency of Different Machine Learning Algorithms.
See this image and copyright information in PMC

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