Fall prediction using behavioural modelling from sensor data in smart homes

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the...

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Bibliographic Details
Published in:The Artificial intelligence review 2020-02, Vol.53 (2), p.1071-1091
Main Authors: Forbes, Glenn, Massie, Stewart, Craw, Susan
Format: Article
Language:English
Subjects:
Activity recognition
Aged
Analysis
Artificial Intelligence
Computer Science
Falling
Falls
Identification methods
Machine learning
Older people
Risk assessment
Sensors
Smart buildings
Smart houses
Test procedures
Viability
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Summary:The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-019-09687-7