Understanding and predicting falls of people living with dementia

Falls are a serious health and social care concern, as they are related to lack of social interaction (going out), hospitalisation, hip fractures, death, immobilisation, etc. There is published evidence that cognitive impairment, especially dementia, is an increased risk factor for falls due to reduced ability to dual-task and share attention. Recent research has focused on developing AI-based methods for detecting falls. However, this research is yet to be set in wider social and psychological contexts.

The project aims at addressing this shortage by combining computer science and psychology methods in a study that seeks to understand the fall mechanisms, and the wider context within which people have falls. This new understanding should allow devising intervention strategies. Key research questions are likely to include:

Can AI-based methods be developed to detect mobility and stability losses, and be deployed in real environments?
What are the environmental factors associated with the detected falls, and that favour attention shift?
Can interventions be designed to alleviate these identified factors?
The successful applicant will design AI-based methods to detect instabilities in gait and posture that are indicative of higher fall risk. These methods will be based on, and extend, the main supervisor's successful works on identifying movements that deviate from a learnt 'normal' model in home environments. They will be used to identify areas in homes that present a higher risk of fall.

Participants aged 65+ with early and middle dementia stages will be recruited, for studies either at home or in care-homes. Focus groups and neuropsychological tests will help linking the detected fall-prone areas with the likely causes of increased fall risk, such as causes of attention shifts. Possible interventions to reduce or suppress these elements may be discussed during focus groups as well.

An iterative development strategy will favour user-involvement in the design and evaluation of methods. This will ensure that the developed methods are useable in real scenarios and realise impact. HCRW will help gain direct exposure to Welsh policymakers to help influencing policies and enable a maximum impact in Wales.