Complex conversational scene analysis using wearable sensors

Abstract

When aspiring to achieve 'in the wild' behavior analysis, we come across a number of conceptual and practical issues. In this chapter, we focus primarily on describing the data collection process for the automated analysis of human social behavior. Specifically, we address the task of analyzing social interaction during conversations. Most research in this area has focused largely on seated scenarios such as a small group having a meeting. In this chapter, we address the challenges that are faced when analyzing complex conversational scenes; crowded social settings where mingling occurs such as networking events, cocktail parties or conferences.We discuss and provide definitions of what 'in the wild' means for the context of wearable sensors. We provide a case study detailing different concerns that can emerge as a result of 'in the wild' social behavior analysis. More concretely, we address this in terms of how the concept of ecological validity coming from experimental psychology links with the concept of 'in the wild', practical and conceptual issues related to data collection, and finally how this influences social behavior analysis.Importantly in the presentation of the behavior analysis, we address key questions when an entire dataset is recorded from continuous natural behavior 'in the wild': When do we have enough data? Do we need a different machine learning approach for different amounts of data? Are social behaviors (e.g. speaking) more difficult to characterize than activities (e.g. walking/stepping) when the setting is so uncontrolled? We try to answer this question by considering the extent to which the nature of this problem becomes more personalized or person-independent as the size of the dataset increases.