Assessing the communication attitude of the elderly using prosodic information and head motions

In order to provide a watching service for the elderly with dementia, recognizing and assessing their cognitive and health status are indispensable. In this study, we propose a prediction model for assessing the communication attitude of the elderly during interacting with a virtual agent. We define speech features and head motion features using frequency analysis and apply them to a linear regression analysis. The coefficient of determination for the model using only speech features was 0.413 and that for a model that exploited both speech and head movement features was 0.505. This result suggests that combining speech and head movement data is useful in predicting the communication attitude of the elderly, and the model is can be applied for automatic assessment in watching services.