Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks

Towards multimodal analysis of human behavior in crowded mingling scenarios using movement cues from wearable sensors and cameras

Mingling events are scenarios where people come together to socialize, which present a high concentration of social interactions in what are called free-standing conversations groups [1]. This makes them interesting study cases for analyzing social human behavior in a natural and uncontrolled setting. Examples of such events are drink parties, networking events or conference poster sessions (see Figure 1 for examples)

Filling the Gaps: Predicting Missing Joints of Human Poses Using Denoising Autoencoders

human pose estimation, autoencoder