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This paper describes the development of a reliable multi-sensor data fusion system for monitoring patient vital-signs in the home. Initial investigatory work has taken place using ambulatory hospital patients, in the Oxford Cancer Hospital. Our monitoring approach is based on a probabilistic model of normality learned from a data-set of vital signs acquired from a representative group of high-risk patients. Alerts are provided to carers whenever patient vital signs are deemed "abnormal" with respect to the model of normality. We show examples of how this approach correctly detects physiological deterioration in the target patient group, and describe future work in further validation of the technology in home monitoring applications.

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Conference paper

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