Abstract

People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavior in PLwD causes distress and increases the risk of injury to both patients and caregivers. In this paper, we present the use of a multi-modal wearable device that captures motion and physiological indicators to detect agitation in PLwD. We identify features extracted from sensor signals that are the most relevant for agitation detection. We hypothesize that combining multi-modal sensor data will be more effective to identify agitation in PLwD in comparison to a single sensor. The results of this unique pilot study are based on 17 participants' data collected during 600 days from PLwD admitted to a Specialized Dementia Unit. Our findings show the importance of using multi-modal sensor data and highlight the most significant features for agitation detection.

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