Dementia Scale Classification Based on Ubiquitous Daily Activity and Interaction Sensing

This paper investigates the integration of different approaches to automatically predict high/low-score on the dementia scale. We propose two different approaches to predict this value by capturing the following: (1) the participant's interaction behavior with a humanoid robot and (2) the indoor daily activity in the residence using ubiquitous sensors. The interaction and indoor activity data set were obtained by recording 32 participants living in common residences, including 17 with symptoms of dementia, as indicated through a cognitive test (Revised Hasegawa Dementia Scale). To obtain the interaction features, we extracted the turn-taking features of interaction with a mobile-typed humanoid robot. To extract the indoor activity features, we collected the location data of each participant in the residence using the received signal strength indicators (RSSIs) of Bluetooth signals from different access points (e.g., shared spaces or the participant's room). In the experimental evaluation, we trained binary classification models for classifying the score on the dementia scale from these datasets. The results show that the best classification accuracy (0.875) is achieved when interaction and activity features are fused using a random forest classifier.