Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements.

Abstract

Physiological responses of two interacting individuals contain a wealth of information about the dyad: for example, the degree of engagement or trust. However, nearly all studies on dyadic physiological responses have targeted group-level analysis: e.g., correlating physiology and engagement in a large sample. Conversely, this paper presents a study where physiological measurements are combined with machine learning algorithms to dynamically estimate the engagement of individual dyads. Sixteen dyads completed 15-min naturalistic conversations and self-reported their engagement on a visual analog scale every 60 s. Four physiological signals (electrocardiography, skin conductance, respiration, skin temperature) were recorded, and both individual physiological features (e.g., each participant’s heart rate) and synchrony features (indicating degree of physiological similarity between two participants) were extracted. Multiple regression algorithms were used to estimate self-reported engagement based on physiological features using either leave-interval-out crossvalidation (training on 14 60-s intervals from a dyad and testing on the 15th interval from the same dyad) or leave-dyad-out crossvalidation (training on 15 dyads and testing on the 16th). In leave-interval-out crossvalidation, the regression algorithms achieved accuracy similar to a ‘baseline’ estimator that simply took the median engagement of the other 14 intervals. In leave-dyad-out crossvalidation, machine learning achieved a slightly higher accuracy than the baseline estimator and higher accuracy than an independent human observer. Secondary analyses showed that removing synchrony features and personality characteristics from the input dataset negatively impacted estimation accuracy and that engagement estimation error was correlated with personality traits. Results demonstrate the feasibility of dynamically estimating interpersonal engagement during naturalistic conversation using physiological measurements, which has potential applications in both conversation monitoring and conversation enhancement. However, as many of our estimation errors are difficult to contextualize, further work is needed to determine acceptable estimation accuracies.