Fall detection monitoring systems: a comprehensive review

The increase in elderly population especially in the developed countries and the number of elderly people living alone can result in increased healthcare costs which can cause a huge burden on the society. With fall being one of the biggest risk among the elderly population resulting in serious inju...

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Veröffentlicht in:Journal of ambient intelligence and humanized computing Jg. 9; H. 6; S. 1809 - 1833
Hauptverfasser: Vallabh, Pranesh, Malekian, Reza
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2018
Springer Nature B.V
Schlagworte:
Accelerometers
Activities of daily living
Algorithms
Artificial Intelligence
Computational Intelligence
Cost control
Decision trees
Engineering
Fall detection
Falls
Injuries
Machine learning
Monitoring systems
Older people
Original Research
Quality of life
Robotics and Automation
Sensors
Taxonomy
User Interfaces and Human Computer Interaction
Healthcare
Elderly care
Fall detection
Machine learning
ISSN:1868-5137, 1868-5145
Online-Zugang:Volltext
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Zusammenfassung:The increase in elderly population especially in the developed countries and the number of elderly people living alone can result in increased healthcare costs which can cause a huge burden on the society. With fall being one of the biggest risk among the elderly population resulting in serious injuries, if not treated quickly. The advancements in technology, over the years, resulted in an increase in the research of different fall detection systems. Fall detection systems can be grouped into the following categories: camera-based, ambient sensors, and wearable sensors. The detection algorithm and the sensors used can affect the accuracy of the system. The detection algorithm used can either be a decision tree or machine learning algorithms. In this paper, we study the different fall detection systems and the problems associated with these systems. The fall detection model which most recent studies implements will be analysed. From the study, it is found that personalized models are the key, for creating an accurate model and not limiting users to specific activities to perform.
Bibliographie:ObjectType-Article-1
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-017-0592-3