Longitudinal Evolution of Coachees’ Behavioural Responses to Interaction Ruptures in Robotic Positive Psychology Coaching

Robotic mental well-being coaches could be used to help people maintain their well-being, and improve access to mental healthcare. In coaching, the alliance between the coach and coachee is important for the success of the practice. However, this alliance might be negatively affected by interaction ruptures (e.g., the robot making mistakes and the user feeling awkward) that still commonly occur in human-robot interactions. Therefore, robotic coaches should be able to recognize ruptures occurring during their interactions with human users to guarantee the success of the well-being practice. To this aim, we analyse coachee behavioural responses to interaction ruptures during a robotic positive psychology coaching practice and how these behavioural cues evolve over time. We focus our analysis on a dataset we collected in a previous work, where 26 participants interacted with either a QTrobot or a Misty II robot at their workplace over 4 weeks. We undertake a longitudinal analysis of coachees’ multimodal nonverbal cues (i.e., facial expressions, vocal acoustic features, and body pose features) to investigate the contribution of individual modalities for detecting interaction ruptures. Our results show that coachees: i) displayed facial cues of rupture (e.g, laughing at the robot) and suspicion more in the first week than in the last week; ii) talked more and were less silent in the last week than in the previous weeks; and iii) exhibited a higher number of hand-over-face gestures (a cue for self-disclosure) in the last week than in the previous weeks. Our findings aim to inform the development of AI models for multi-modal detection of interaction ruptures which can be used to improve the effectiveness and the success of robotic well-being coaching.