Dynamic Bayesian Network Modelling of User Affect and Perceptions of a Teleoperated Robot Coach during Longitudinal Mindfulness Training

TL;DR

This study employs a dynamic Bayesian network to model and analyze longitudinal interactions between participants and a teleoperated robot coach during mindfulness training, revealing complex temporal dependencies and enabling predictions.

Contribution

It introduces a novel DBN-based approach to understand and predict longitudinal social robot interactions, capturing complex dependencies in multi-dimensional data.

Findings

DBN accurately predicts session ratings over time.

Personality traits influence facial expressions and ratings.

Model enables imputation and personalized rating estimation.

Abstract

Longitudinal interaction studies with Socially Assistive Robots are crucial to ensure that the robot is relevant for long-term use and its perceptions are not prone to the novelty effect. In this paper, we present a dynamic Bayesian network (DBN) to capture the longitudinal interactions participants had with a teleoperated robot coach (RC) delivering mindfulness sessions. The DBN model is used to study complex, temporal interactions between the participants self-reported personality traits, weekly baseline wellbeing scores, session ratings, and facial AUs elicited during the sessions in a 5-week longitudinal study. DBN modelling involves learning a graphical representation that facilitates intuitive understanding of how multiple components contribute to the longitudinal changes in session ratings corresponding to the perceptions of the RC, and participants relaxation and calm levels. The learnt model captures the following within and between sessions aspects of the longitudinal interaction study: influence of the 5 personality dimensions on the facial AU states and the session ratings, influence of facial AU states on the session ratings, and the influences within the items of the session ratings. The DBN structure is learnt using first 3 time points and the obtained model is used to predict the session ratings of the last 2 time points of the 5-week longitudinal data. The predictions are quantified using subject-wise RMSE and R2 scores. We also demonstrate two applications of the model, namely, imputation of missing values in the dataset and estimation of longitudinal session ratings of a new participant with a given personality profile. The obtained DBN model thus facilitates learning of conditional dependency structure between variables in the longitudinal data and offers inferences and conceptual understanding which are not possible through other regression methodologies.