title: Monitoring changes in behaviour from multi-sensor systems.
abstract: Behavioural patterns are important indicators of health status in a number of conditions and changes in behaviour can often indicate a change in health status. Currently, limited behaviour monitoring is carried out using paper-based assessment techniques. As technology becomes more prevalent and low-cost, there is an increasing movement towards automated behaviour-monitoring systems. These systems typically make use of a multi-sensor environment to gather data. Large data volumes are produced in this way, which poses a significant problem in terms of extracting useful indicators. Presented is a novel method for detecting behavioural patterns and calculating a metric for quantifying behavioural change in multi-sensor environments. The data analysis method is shown and an experimental validation of the method is presented which shows that it is possible to detect the difference between weekdays and weekend days. Two participants are analysed, with different sensor configurations and test environments and in both cases, the results show that the behavioural change metric for weekdays and weekend days is significantly different at 95% confidence level, using the methods presented.
authors.affiliation: Warwick Engineering in Biomedicine , School of Engineering , University of Warwick , Coventry CV4 7AL , UK.
authors.forename: James D
authors.initials: JD
authors.lastname: Amor
authors.affiliation: Warwick Engineering in Biomedicine , School of Engineering , University of Warwick , Coventry CV4 7AL , UK.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: James
keywords: automated behaviour-monitoring systems; behavioural patterns; behavioural sciences computing; data analysis method; health status; medical signal processing; multisensor systems; patient monitoring; sensor fusion