Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review
1
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
2
Department of Electrical Engineering (ESAT), Katholieke Universiteit (KU) Leuven, 3000 Leuven, Belgium
3
Department of Software, Sangmyung University, Cheonan 31066, Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2021, 21(15), 5134; https://doi.org/10.3390/s21155134
Received: 24 June 2021
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Revised: 16 July 2021
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Accepted: 24 July 2021
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Published: 29 July 2021
(This article belongs to the Special Issue Sensors and Technologies for Fall Risk Awareness)
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.
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Keywords:
fall detection; fall prevention; machine learning; review paper
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MDPI and ACS Style
Usmani, S.; Saboor, A.; Haris, M.; Khan, M.A.; Park, H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors 2021, 21, 5134. https://doi.org/10.3390/s21155134
AMA Style
Usmani S, Saboor A, Haris M, Khan MA, Park H. Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review. Sensors. 2021; 21(15):5134. https://doi.org/10.3390/s21155134
Chicago/Turabian StyleUsmani, Sara, Abdul Saboor, Muhammad Haris, Muneeb A. Khan, and Heemin Park. 2021. "Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review" Sensors 21, no. 15: 5134. https://doi.org/10.3390/s21155134
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