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.
翻译:与社会辅助机器人的纵向互动研究至关重要,以确保机器人与长期使用相关,其感知不易产生新效果。在本论文中,我们展示了一个动态的巴伊西亚网络(DBN),以捕捉参与者与远程操作机器人教练(RC)的纵向互动,提供注意课;DBN模型用于研究参与者自我报告的个性特征、每周基线福利评分、届会评级和面部AU在为期5周的纵向研究期间产生的复杂、时间互动。DBN模型包括学习一个图形代表,帮助人们直观了解多种组成部分如何促进与RC和参与者的感知、放松和平静水平相对应的届会评级的纵向变化。所学模型捕捉了纵向互动研究的以下部分:5个个个个个性层面对非盟面部和届会评级的影响,面部国家对届会评级的影响,以及会议评级项目的影响。DBNBN结构使用前3个时间点进行学习,所获取的模型有助于直观了解与RC和参与者的放松度水平水平水平相关的届会,因此,通过最后2个日历数据评级,也用于预测最后2个方向的数据评级。