Evacuation planning and emergency routing systems are crucial in saving lives during disasters. Traditional emergency routing systems, despite their best efforts, often struggle to accurately capture the dynamic nature of flood conditions, road closures, and other real-time changes inherent in urban disaster logistics. This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL). The model incorporates a unique RL environment that considers multiple criteria, such as traffic conditions, hazardous situations, and the availability of safe routes. The RL agent in this model learns optimal actions through interaction with the environment, receiving feedback in the form of rewards or penalties. The ReinforceRouting model excels in executing prompt and accurate route planning on large road networks, outperforming traditional RL algorithms and shortest-path-based algorithms. A higher safety score and episode reward of the model are demonstrated when compared to these classical methods. This innovative approach to disaster evacuation planning offers a promising avenue for enhancing the efficiency, safety, and reliability of emergency responses in dynamic urban environments.

Transitioning from the military to the civilian lifestyle, especially for military veterans who decide to pursue careers in the civilian workforce, is often a difficult experience. The job interview, a task in which the interviewees meet and discuss their skills and career goals with strangers in a position of authority, is the first step of assimilation into the civilian workplace, which might cause them to experience nervousness or anxiety. This feeling of excessive stress may compromise the interviewee's performance, therefore potentially impeding their successful transition to the workforce. Intelligent interview training technologies would benefit from automated stress detection systems that could assist interviewees in better understanding causes and antecedents of stressors during their interaction with the interviewer. This paper examines self-reported and bio-behavioral measures of stress experienced during mock job interviews conducted with 24 U.S. military veterans. Self-reported measures were captured via a global measure of stress reported by the participant at the conclusion of the interview, and a continuous moment-to-moment annotation of stress resulting from the retrospective inspection of the interview video recording. Bio-behavioral indices of stress include physiological reactivity measures captured via electrodermal activity and electrocardiogram signals, as well as acoustic measures extracted from speech. Results indicate that physiological reactivity measures exhibit moderate-to-strong correlation with self-reported measures of stress, and can be thus used to estimate the self-reported stress measures. Augmenting the feature space with demographic and psychological traits can further improve the accurate detection of stress during the interviews.

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas.

Public speaking is a vital skill for making good impressions, effectively exchanging ideas, and influencing others. Yet, public speaking anxiety (PSA) ranks as a top social phobia. Recent advancements in wearable devices and ubiquitous virtual reality (VR) interfaces can help measure and mitigate PSA. This research quantifies PSA through bio-behavioral markers related to individuals’ physiological and acoustic characteristics. The effect of virtual reality (VR) training on alleviating PSA is measured through self-reported and bio-behavioral indices. Psychological (e.g., general trait anxiety, personality) and demographic (e.g., age, gender, highest education, native language) traits are examined as moderating factors between bio-behavioral indices and PSA, as well as moderating factors for measuring the VR effectiveness in mitigating PSA. These measures are also used as clustering criteria for stratifying participants in group-based models of PSA. Results indicate the significance of such traits to modeling PSA with the proposed group-based models yielding Spearman’s correlation of 0.55 ($p<0.05$) between the actual and predicted outcome. Results further demonstrate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported ($p<0.05$) and physiological ($p<0.05$) indices. Findings from this study will enable researchers to better understand antecedents and causes of PSA and lay the foundation for personalized adaptive feedback for PSA interventions.

Public speaking skills are essential to professional success. Yet, public speaking anxiety (PSA) is considered one of the most common social phobias. Understanding PSA can help communication experts identify effective ways to treat this communication-based disorder. Existing works on PSA rely on self-reports and aggregate multimodal measures which do not capture the temporal variation in PSA. This paper examines temporal trajectories of acoustic and physiological measures throughout the public speaking encounter with real and virtual audiences, and aims to model those in both knowledge- and data-driven ways. Knowledge-driven models leverage theoretically-grounded patterns through fitting interpretable parametric functions to the corresponding signals. Data-driven models consider the functional nature of multimodal signals via functional principal component analysis. Results indicate that the parameters of the proposed models can successfully estimate individuals’ trait anxiety in both real-life and virtual reality settings, and suggest that models trained on data obtained in virtual public speaking stimuli are able to estimate levels of PSA in real-life.

Public speaking anxiety (PSA) is among the top social phobias in the world. Quantifying PSA in a reliable and unobtrusive manner can lay the foundation toward personalized and inexpensive technology-based interventions. Existing work for quantifying PSA often relies on self-reported measures and statistical aggregates of bio-behavioral indices, such as physiology and speech. Such aggregated bio-behavioral indices are not able to capture time-based trajectories of PSA variation, that can be very useful for better understanding and reliably predicting moments of anxiety. We tackle this problem by introducing temporal parametric models to quantify bio-behavioral trajectories of PSA throughout a public speaking encounter. Using data from 55 participants in a real-life public speaking task, the parameters of the proposed models are found to be significantly correlated with individuals' trait characteristics of general and communication-based anxiety, outperforming aggregate mean bio-behavioral measures.

Public speaking is central to socialization in casual, professional, or academic settings. Yet, public speaking anxiety (PSA) is known to impact a considerable portion of the general population. This paper utilizes bio-behavioral indices captured from wearable devices to quantify the effectiveness of systematic exposure to virtual reality (VR) audiences for mitigating PSA. The effect of separate bio-behavioral features and demographic factors is studied, as well as the amount of necessary data from the VR sessions that can yield a reliable predictive model of the VR training effectiveness. Results indicate that acoustic and physiological reactivity during the VR exposure can reliably predict change in PSA before and after the training. With the addition of demographic features, both acoustic and physiological feature sets achieve improvements in performance. Finally, using bio-behavioral data from six to eight VR sessions can yield reliable prediction of PSA change. Findings of this study will enable researchers to better understand how bio-behavioral factors indicate improvements in PSA with VR training.

Public speaking is key to effectively exchanging ideas, persuading others, and making a tangible impact. Yet, public speaking anxiety (PSA) ranks as a top social phobia among many people. This paper leverages bio-behavioural indices captured from wearable devices and virtual reality (VR) interfaces to quantify PSA. The significance of individual-specific factors, such as general trait anxiety and personality, as well as contextual factors, such as age, gender, highest education, and native language, in moderating the association between bio-behavioral indices and PSA is further examined through group-based machine learning models. Results highlight the importance of including such factors for detecting PSA with the proposed group-based PSA models yielding Spearman's correlation of 0.55(p <; 0.05) between the actual and predicted state-based anxiety scores. This work further analyzes whether systematic exposure to public speaking tasks in the VR environment can help alleviate PSA. Results indicate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported (p <; 0.05) and physiological (p <; 0.05) indices. Findings of this study will enable researchers to better understand antedecedents and causes of PSA contributing to behavioral interventions using VR.

This paper presents an introduction to the "Human-Habitat for Health (H3): Human-habitat multimodal interaction for promoting health and well-being in the Internet of Things era" workshop, which was held at the 20th ACM International Conference on Multimodal Interaction on October 16th, 2018, in Boulder, CO, USA. The main theme of the workshop focused on the effect of the physical or virtual environment on individual's behavior, well-being, and health. The H3 workshop included keynote speeches that provided an overview and future directions of the field, as well as presentations including position papers and research contributions. The workshop brought together experts from academia and industry spanning a set of multi-disciplinary fields, including computer science, speech and spoken language understanding, construction science, life-sciences, health sciences, and psychology, to discuss their respective views and identify synergistic and converging research directions and solutions.