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- Title
Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.
- Authors
Usmani, Sara; Saboor, Abdul; Haris, Muhammad; Khan, Muneeb A.; Park, Heemin
- 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.
- Subjects
ACCIDENTAL fall prevention; MACHINE learning; QUALITY of life; OLDER people; AGE groups
- Publication
Sensors (14248220), 2021, Vol 21, Issue 15, p5134
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21155134