LAUSR

Dementia is becoming more prevalent due to the aging population in which there is deterioration in memory, thinking, behaviour, and the ability to perform everyday activities. A significant challenge in dementia is achieving an accurate and timely diagnosis. If the patient can have proper medical treatment at an early stage, then the dementia growth can be delayed by months to years. Inefficient travel patterns are one of the first indicators of progressive dementia. In this paper, we propose an early dementia detection system using inhabitant travel pattern classification. We use the environmental passive sensor signals for sensing the movement of the inhabitant. The system segments the movements into travel episodes and classifies them using a recurrent neural network. The advantage of using a recurrent neural network is that it directly deals with the raw movement sensory data and does not require any domain-specific knowledge. Finally, the system handles the unbalanced classes of travel patterns by using the focal loss and enhances the discriminative power of the deeply learned features by the center loss function. We conduct several experiments on real-life datasets to verify the accuracy of the system.