1. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review
- Author
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Abdul Saboor, Heemin Park, Sara Usmani, Muneeb Ahmed Khan, and Muhammad Haris
- Subjects
Technology ,Computer science ,Wearable computer ,Review ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Biochemistry ,Analytical Chemistry ,Constant false alarm rate ,Machine Learning ,Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Instruments & Instrumentation ,Instrumentation ,review paper ,Atomic and Molecular Physics, and Optics ,fall prevention ,Chemistry ,fall detection ,machine learning ,DEPRESSIVE SYMPTOMS ,Physical Sciences ,020201 artificial intelligence & image processing ,Seasons ,Algorithms ,Fall prevention ,Emerging technologies ,ACTIVITY RECOGNITION ,TP1-1185 ,Machine learning ,Quality of life (healthcare) ,Age groups ,Humans ,Electrical and Electronic Engineering ,Health risk ,Aged ,Science & Technology ,business.industry ,Chemical technology ,Chemistry, Analytical ,010401 analytical chemistry ,Engineering, Electrical & Electronic ,ADULTS ,0104 chemical sciences ,Quality of Life ,Accidental Falls ,Fall detection ,Artificial intelligence ,WEARABLE SENSORS ,business ,computer - 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. ispartof: SENSORS vol:21 issue:15 ispartof: location:Switzerland status: published
- Published
- 2021