Social Robots for Evaluating Attention State in Older Adults

Trauma-informed care approach in developing companion robots: a preliminary observational study

Introduction

This study explores the integration of Trauma-Informed Care (TIC) principles into the development of companion robots for elderly trauma survivors, particularly those with Post-Traumatic Stress Disorder (PTSD). The psychological effects of trauma, especially in aging populations, can complicate mental and physical health, highlighting the need for tailored technological solutions.

Methods

The research involved two focus groups with Holocaust survivors (N = 12) who engaged directly with a social robot. Discussions explored their needs, barriers, and coping strategies based on their longitudinal experiences of trauma, resilience, and aging. Transcripts were thematically analyzed, identifying key TIC-related themes-safety, trust, self-compassion, and self-efficacy-in relation to engagement with companion robots.

Results

Findings revealed that trauma survivors face significant challenges in communication and technology use. Safe and trusting environments were fundamental for meaningful engagement with robots. Self-compassion and self-efficacy emerged as essential to overcoming initial barriers, indicating that TIC-informed design features could facilitate better uptake and acceptance.

Discussion

The study emphasizes the importance of incorporating TIC principles to ensure these robots meet the complex needs of trauma survivors. Findings also underscore the personal histories, ongoing changes in recollecting the trauma, and the need for stable, empathetic interactions highlights the complexity of designing assistive robots for profoundly affected populations. This study contributes to digital mental health and aging technologies by emphasizing stability, empathy, and user-centered design in developing assistive robotics as a universal, scalable solution.

When Eyes Wander Around: Mind-Wandering as Revealed by Eye Movement Analysis with Hidden Markov Models

Mind-wandering has been shown to largely influence our learning efficiency, especially in the digital and distracting era nowadays. Detecting mind-wandering thus becomes imperative in educational scenarios. Here, we used a wearable eye-tracker to record eye movements during the sustained attention to response task. Eye movement analysis with hidden Markov models (EMHMM), which takes both spatial and temporal eye-movement information into account, was used to examine if participants' eye movement patterns can differentiate between the states of focused attention and mind-wandering. Two representative eye movement patterns were discovered through clustering using EMHMM: centralized and distributed patterns. Results showed that participants with the centralized pattern had better performance on detecting targets and rated themselves as more focused than those with the distributed pattern. This study indicates that distinct eye movement patterns are associated with different attentional states (focused attention vs. mind-wandering) and demonstrates a novel approach in using EMHMM to study attention. Moreover, this study provides a potential approach to capture the mind-wandering state in the classroom without interrupting the ongoing learning behavior.