Radar-based sensors do not require optimal lighting and atmospheric conditions and nonocclusion, making them a promising solution for human behavior analysis in complex environments. Existing radar-based models generally retrieve features from either the time-velocity domain or the time-range domain. Such two-dimensional representations cannot fully depict dynamic human motion features. In this paper, we propose a temporal range-Doppler PointNet-based method to analyze human behavior. We transform human echoes to 3D point sets and then feed them into the hierarchical PointNet model for classification. The proposed point network can learn structural features from the micromotion trajectory more effectively than directly processing the raw point cloud. To further improve our model's robustness in practical applications, we design an outlier detection module for detecting anomalies such as in multitarget scenarios. The results of experiments on motion capture databases and range-Doppler radar measurements demonstrate that our method realizes outstanding performance in terms of the classification accuracy, noise robustness, and anomaly detection accuracy.

Under circumstances of fire, panic usually brings uncertainty and unpredictability to evacuation. Therefore, a deep understanding of panic is desired. This study aims to dig into the underlying mechanism of fire evacuation panic by measuring and analysing psycho- and physiological indicators. In the experiment, participants watched a simulated train station within which three sets of stimuli were triggered separately. Eye movement and brain haemodynamic responses were collected during the watch, while questionnaires and interviews of emotions were conducted after. The analysed physiological indicators include the amplitude of pupil dilation, the time ratios of fixation and saccade, the binned entropy of gaze location, and the brain activation coefficients. The results of this research indicate that fire evacuation panic can be broken down into two elements. (1) Unawareness of situation: less knowledge of the situation leads to a higher level of panic; (2) Intensity of visual stimulation: the panic level is escalated with increased severity of fire that is perceived.

How do evacuees behave and what factors influence their egress selection behavior of stairs or an elevator during a high-rise building evacuation? These issues remain unclear given a paucity of research. In this paper, a series of experiments and questionnaires with 45 participants were carried out to examine the effects of non-irritant smoke and participants’ knowledge of evacuation procedures (especially elevator evacuation). After each experiment, participants were asked to respond to a questionnaire. The results of evacuation times for the elevator versus stairs, the proportion of elevator users, elevator wait times, and redirect behaviors are analyzed and discussed. In experiment 1, 26.7% of 45 participants chose the elevator. In experiment 2, non-irritant smoke was released, and the proportion of participants who used the elevator decreased to 24.4% of 45 participants. Before experiment 3, we shared information about the evacuation (i.e., an explanation of evacuation procedures and elevator evacuation experience) with the participants, after which elevator use increased to 40% of 45 participants. As a result, we found that non-irritant smoke and knowledge of evacuations affected evacuees’ behavior; however, the gap between the proportions of participants who reportedly preferred to use the elevator and who actually used the elevator was large. According to the questionnaire, the proportion of participants who wanted to use the elevator was 55.6%. Moreover, elevator overload, physical fatigue, and social bonds were observed in our experiments. In conclusion, several suggestions are given to building designers, and basic data (such as evacuation times and proportion of stairs or elevator use) offer insight for building safety engineers in designing evacuation strategies incorporating stairs and elevators.