Predicting Activation Liking of People With Dementia - Steinert, Lars; Putze, Felix; Küster, Dennis; Schultz, Tanja (Frontiers in Computer Science, 2022)
Physical, social and cognitive activation is an important cornerstone in non-pharmacological therapy for People with Dementia (PwD). To support long-term motivation and wellbeing, activation contents first need to be perceived positively. Prompting for explicit feedback, however, is intrusive and interrupts the activation flow. Automated analyses of verbal and nonverbal signals could provide an unobtrusive means to recommend suitable contents based on implicit feedback. In this study, we investigate the correlation between engagement responses and self-reported activation ratings. Next, predict activation ratings of PwD based on verbal and nonverbal signals in an unconstrained care setting. Applying Long-Short-Term-Memory (LSTM) networks, we can show that our classifier outperforms chance level. We further investigate which features are the most promising indicators for the prediction of activation ratings of PwD.