To well understand crowd behavior, microscopic models have been developed in recent decades, in which an individual's behavioral/psychological status can be modeled and simulated. A well-known model is the social-force model innovated by physical scientists (Helbing and Molnar, 1995; Helbing, Farkas and Vicsek, 2000; Helbing et al., 2002). This model has been widely accepted and mainly used in simulation of crowd evacuation in the past decade. A problem, however, is that the testing results of the model were not explained in consistency with the psychological findings, resulting in misunderstanding of the model by psychologists. This paper will bridge the gap between psychological studies and physical explanation about this model. We reinterpret this physics-based model from a psychological perspective, clarifying that the model is consistent with psychological theories on stress, including time-related stress and interpersonal stress. Based on the conception of stress, we renew the model at both micro-and-macro level, referring to multi-agent simulation in a microscopic sense and fluid-based analysis in a macroscopic sense. The cognition and behavior of individual agents are critically modeled as response to environmental stimuli. Existing simulation results such as faster-is-slower effect will be reinterpreted by Yerkes-Dodson law, and herding and grouping effect as well as oscillation phenomenon are further discussed for pedestrian crowd. In brief the social-force model exhibits a bridge between the physics laws and psychological principles regarding crowd motion, and this paper will renew and reinterpret the model on the foundation of psychological studies.

This article introduces a modeling framework to characterize evacuee response to environmental stimuli during emergency egress. The model is developed in consistency with stress theory, which explains how an organism reacts to environmental stressors. We integrate the theory into the well-known social-force model, and develop a framework to simulate crowd evacuation behavior in multi-compartment buildings. Our method serves as a theoretical basis to study crowd movement at bottlenecks, and simulate their herding and way-finding behavior in normal and hazardous conditions. The pre-movement behavior is also briefly investigated by using opinion dynamics. The algorithms have been partly tested in FDS+EVAC as well as our simulation platform crowdEgress.

Tracking interpersonal distances is essential for real-time social distancing management and {\em ex-post} contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach.

In this paper, we study if modeling can help discriminate actions which in turn can be used to select an appropriate behavior for a mobile robot. For human-human interaction, significant social and communicative information can be derived from interpersonal distance between two or more people. If Human-Robot Interaction reflects this human-human interaction property, then interpersonal distance between a human and a robot may contain similar social and communicative information. An effective robot's actions, including actions associated with interpersonal distance, must be suitable for a given social circumstance. Studying interpersonal distance between a robot and a human is very important for assistive robots. We use autonomously detected features to develop such an interpersonal model using Gaussian Mixture Model (GMM) and demonstrate that such a learned model can discriminate different human actions. The proposed approach can be used in a socially-aware planner to weight trajectories and select actions that are socially appropriate for a given social situation.

In this paper, we discuss the design of a new “intelligent” system capable of selecting the most “outstanding” user from a group of people in a scene. This ability to select a user to interact with is very important in natural interfaces and in emergency-related applications where several people can ask to communicate simultaneousely. The system uses both static and dynamic features such as speed, height and social features (interpersonal distances) which are all acquired using a RGB-Depth camera (Kinect). Those features are combined and a contrast-based approach is able to focus the system’ attention on a specific user without complex rules. People position with respect to the Kinect sensor and learning of the previous people behavior are also used in a top-down way to influence the decision on the most interesting people. This application is represented by a wall of HAL9000's eyes that search in the scene who is the most different person then track and focus at him until someone more "different'' shows up.