Multimodal data collection for social interaction analysis in-the-wild

Conference paper (2019)

Authors

H.S. Hung Pattern Recognition and Bioinformatics -

C.A. Raman Pattern Recognition and Bioinformatics -

E. Gedik Pattern Recognition and Bioinformatics -

S. Tan Pattern Recognition and Bioinformatics -

J.D. Vargas Quiros Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

DOI: https://doi.org/10.1145/3343031.3351320

Wearable Sensors ConfLab Multimodal Synchronization Social Behaviour Analysis

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http://resolver.tudelft.nl/uuid:359d464e-d876-42f0-930c-fc5310898332

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Published Date

15-10-2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

The benefits of exploiting multi-modality in the analysis of human-human social behaviour has been demonstrated widely in the community. An important aspect of this problem is the collection of data-sets that provide a rich and realistic representation of how people actually socialize with each other in real life. These subtle coordination patterns are influenced by individual beliefs, goals, and, desires related to what an individual stands to lose or gain in the activities they perform in their every day life. These conditions cannot be easily replicated in a lab setting and require a radical re-thinking of both how and what to collect. This tutorial provides a guide on how to create such multi-modal multi-sensor data sets when holistically considering the entire experimental design and data collection process.

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