Abstract
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.
Highlights
Over 65s are a growing segment of the UK population forecast to rise by another 2 million before 2021
Their assumption of predictable decline refers to the longer-term degradation of elderly ability, and influenced future works by indicating that particular ADLs could be more indicative of morbidity than others
This review identifies and assesses the existing approaches to the use of in-house sensors for monitoring residents’ on-going health
Summary
Over 65s are a growing segment of the UK population forecast to rise by another 2 million before 2021. Identifying the potential risk of falling for people would help prevent falls before they occur. Laboratory testing is costly and not undertaken routinely It is an intrusive method of monitoring, which can present challenges when people suffer from mobility issues or other aversions to hospitals. In this paper sensors that can provide the required level of in-home monitoring are investigated and considered, along with data analysis approaches that can identify specific activities or behaviours that may be considered as precursors to falls. Continuous monitoring in a smart home provides an opportunity for the resident’s activities to be tracked over time (Juarez et al 2015), and changes in behaviours can be identified. The integration of sensors and data analysis for Fall Risk Determination are discussed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
