COMPUFALLS: Computer-Based System to Early Detect Risk of Falling in Older People: An International Collaborative Study
- Research Opportunity
- PhD, Masters by Research, Honours
- Number of Honour Places Available
- Medicine and Radiology
- Western Health
|Professor Gustavo Duquefirstname.lastname@example.org||Personal web page|
COMPUFALLS aims to prolong healthy and independent living of the older population, enhancing functional autonomy by early detection of falls-risk. To do so, COMPUFALLS will develop a system for long term unobtrusive monitoring and intelligent analysis of behavioural patterns of older people, detecting slowly developing anomalies, such as difficulty undertaking activities of daily living and falls and will correlate these behavioural patterns with clinical and functional data. Thus, early diagnostic markers can be obtained as an alternative to obtrusive clinical methods. COMPUFALLS will provide flexible solutions tailored to assessing the risk of falling in older people who have never fallen and to model the risk of re-falling in those who already have a high falls-risk. COMPUFALLS proposes a Decision Support System (DSS) that detects the risk of falling, by combining clinical, functional, and behavioural data and comes up with personalised interventions through a NoFallAction module in order to reduce the risk. The COMPUFALLS DSS’s brain is the risk stratification module that generates a predictive model of falling for the first time (FallRisk); and risk modelling of re-falling for older persons at high risk (ReFallRisk). COMPUFALLS will generate two predictive models combining the information coming from the behavioural monitoring and analysis through a NoFallMonitor accompanying the user. The NoFallMonitor mission is twofold: on the one hand it monitors the user behavioural patterns; and on the other hand it executes the personalised interventions that come from the NoFallAction module. Later, COMPUFALLS will analyse the correlation between behavioural patterns (from the NoFallMonitor) and the risk prediction models obtained from the clinical and functional data (from the FallRisk and ReFallRisk) to establish the link and propose refined models.
School Research Themes
PhD, Masters by Research, Honours
Students who are interested in joining this project will need to consider their elegibility as well as other requirements before contacting the supervisor of this research
For further information about this research, please contact a supervisor.
Research NodeWestern Health
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