Detecting Conversing Groups Using Social Dynamics from Wearable Acceleration: Group Size Awareness.

In this paper, we propose a method for detecting conversing groups. More specifically, we detect pairwise F-formation membership using a single worn accelerometer. We focus on crowded real life scenarios, specifically mingling events, where groups of different sizes naturally occur and evolve over time. Our method uses the dynamics of interaction, derived from people's coordinated social actions and movements. The social actions, speaking, head and hand gesturing, are inferred from wearable acceleration with a transfer learning approach. These automatically labeled actions, together with the raw acceleration, are used to define joint representations of interaction between people through the extraction of pairwise features. We present a new feature set based on the overlap patterns of social actions and utilize some others that were previously proposed in other domains. Our approach considers various interaction patterns of different sized groups by training multiple classifiers with respect to cardinality. The final estimation is then dynamically performed by meta-classifier learning using the local neighborhood of the current test sample. We experimentally show that the proposed method outperforms state of the art approaches. Finally, we show how the accuracy of the social action detection affects group detection performance, analyze the effectiveness of features for different group sizes in detail, discuss how different types of features contribute to the final performance and evaluate the effects of using the local neighborhood for meta-classifier learning.