Developing a Clinical Evaluation and Application Framework
Over the next 10 years in Australia the population of those aged 65 years and older will increase, placing additional stress on society's ability to care for the elderly. Serious falls impact around one in three elderly people and often result in hospitalisation costing approximately $1 billion per annum.
New approaches and supporting technology are required to start addressing this area and improve outcomes. Evidence suggests that intelligent personalised sensor systems can monitor behaviour - detecting unusual sequences, altered activities, or lack of movement. When installed in the home or residential setting, these non-contact sensors alert carers or staff to potentially dangerous behaviours.
This pilot project will monitor pre- and post-fall movement
patterns using unobtrusive, privacy-preserving 3D sensors. Multiple
sensors will be located in individuals' residences, to track their usual
activities over a fixed time period. The resulting data will be
aggregated so that researchers using big data analysis techniques can
investigate whether the monitoring accurately provides warning of falls
and behaviour indicating deterioration in older people.
The research will also evaluate the feasibility of the sensor technology and will attempt to provide a framework for its implementation into the clinical environment.
- Fernando Martin-Sanchez - Health and Biomedical Informatics Centre
- Kathleen Gray - Health and Biomedical Informatics Centre
- Cecily Gilbert - Health and Biomedical Informatics Centre
- Catherine Said - Austin Health
- Michael McGrath - Semantrix
- Udaya Parampali - Computing and Information Systems
- Frank Smolenaers - Centre for Health Innovation
- Damien Malone - Ti Tree Lodge Pty Ltd
IBES Seed Funding 2013