Entertainment robots for automatic detection and mitigation of cognitive impairment in elderly populations

Abstract

This study showed that using collaborative entertainment robots for human-robot interaction can be a promising way to help manage the health of ageing populations by automatically detecting and mitigating cognitive impairment. The system enhanced spoken interaction with users by using cutting-edge technologies such as state-of-the-art speech recognition, natural language processing, and machine learning. The system was tested on senior participants and gathered, analyzed, and displayed individual interaction models to provide automated user engagement, daily interaction monitoring, and automatic early detection of deteriorating mental health. The findings were presented using bar charts and confusion matrices, incorporating important metrics such as mental workload and speech/non-speech interaction graphic. These visualizations aided individuals in managing their behavior to achieve an optimal cognitive workload, a challenging measure to determine due to cognitive decline. In order to make significant progress in the subject, future advancements need to focus on addressing the unpredictability in human speech sequences, using non-speech modalities such as gestures or facial expressions as supplementary inputs to complement speech and behavior, and effectively managing concerns related to human rights and data protection. In addition to technological constraints, future research should prioritize the examination of the enduring impacts of cognitive therapies facilitated by entertainment robots.