Current care models have been deficient in anticipating health problems and in the design of personalised interventions to improve well-being of the aging population. Care models must be designed not only to improve the physical health of elderly but also to improve their overall wellbeing. Using a combination of quantitative and qualitative data sources spanning across multiple interrelated domains, we are abletosythesise personalised profiles of the elderly, such as in terms of their social and emotional needs,. The Socio-Behavioural Engine developed as a result,allows us to accurately identify elderly individuals in need, detect their health problems at an early stage and design personalised interventions to improve their wellbeing.
TheSocio-Behavioral Engine is testedthrough a series of stages tousing data gathered through multiple methods. The engine is able to: a) Combine multiple sources of data from various sensors b) Detect behavioural patterns of individuals c) Compare against personalised benchmarks and/or regional benchmarks d) Identify health and behavioural anomalies
TheSocio-Behavioural Enginewill support customised monitoring inthe changing needs of the elderly care industry.The insights derived fromit will facilitate the information and knowledge flow between the elderly and community. Therefore, elderly care organisations, Voluntary Welfare Organisations (VWO), governmental agencies, and health professionals will immensely benefit from such an elderly-centric solution. This technology will enable the users to identify elderly individuals who require early intervention.
The majority of current solutions that aim to addresssimilar problemsare deficient because their designers did not correlate the technical data collected with the ground truth. The Socio-Behavioural Engine istested using data gathered from 100 homes over a peroid of 18 months and beyond, thus reliability greatly improves.