Quantifying Behavioral Mimicry by Automatic Detection of Nonverbal Cues from Body Motion

Abstract

Effective leadership can increase team performance, however the underlying micro-level behaviors that support team performance are still unclear. At the same time, traditional behavioral observation methods rely on manual video annotation which is a time consuming and costly process. In this work, we employ wearable motion sensors to automatically extract nonverbal cues from body motion. We utilize activity recognition methods to detect relevant nonverbal cues such as head nodding, gesticulating and posture changes. Further, we combine the detected individual cues to quantify behavioral mimicry between interaction partners. We evaluate our methods on data that was acquired during a psychological experiment in which 55 groups of three persons worked on a decision-making task. Group leaders were instructed to either lead with individual consideration orin an authoritarian way. We demonstrate that nonverbal cues can be detected with a F1-measure between 56% and 100%. Moreover, we show how our methods can highlight nonverbal behavioral differences of the two leadership styles. Our findings suggest that individually considerate leaders mimic head nods of their followers twice as often and that their face touches are mimicked three times as often by their followers when compared with authoritarian leaders.