In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human–robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents’ performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human–robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents.

Understanding the human factors governing effective information exchange is increasingly indispensable for the design of day to day human-computer systems. Moreover, effective information exchange becomes a matter of life or death during emergency egress. The complexity of an unknown environment and the unpredictable locations of hazards often prevent evacuees from identifying safe routes. Successful evacuations from locations impacted by fire or earthquakes may depend on user-generated information to increase the chance of collective survival. The present paper employed multi-user virtual reality experiments and an online survey to investigate the mechanisms underlying social influence and collective intelligence during emergencies. Our results demonstrate that information sharing helps to reduce evacuation time and trajectory length. Participants also shared more when given incentives or when there was a lack of knowledge in the public information pool. This work provides further indications of how collective intelligence can be promoted and deployed during emergencies.

BACKGROUND AND OBJECTIVE: Work-related stress affects a large part of today's workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90). METHODS: We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots. RESULTS: The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress. CONCLUSIONS: Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed.

Human path-planning operates differently from deterministic AI-based path-planning algorithms due to the decay and distortion in a human's spatial memory and the lack of complete scene knowledge. Here, we present a cognitive model of path-planning that simulates human-like learning of unfamiliar environments, supports systematic degradation in spatial memory, and distorts spatial recall during path-planning. We propose a Dynamic Hierarchical Cognitive Graph (DHCG) representation to encode the environment structure by incorporating two critical spatial memory biases during exploration: categorical adjustment and sequence order effect. We then extend the "Fine-To-Coarse" (FTC), the most prevalent path-planning heuristic, to incorporate spatial uncertainty during recall through the DHCG. We conducted a lab-based Virtual Reality (VR) experiment to validate the proposed cognitive path-planning model and made three observations: (1) a statistically significant impact of sequence order effect on participants' route-choices, (2) approximately three hierarchical levels in the DHCG according to participants' recall data, and (3) similar trajectories and significantly similar wayfinding performances between participants and simulated cognitive agents on identical path-planning tasks. Furthermore, we performed two detailed simulation experiments with different FTC variants on a Manhattan-style grid. Experimental results demonstrate that the proposed cognitive path-planning model successfully produces human-like paths and can capture human wayfinding's complex and dynamic nature, which traditional AI-based path-planning algorithms cannot capture.

Sensors have become ubiquitous in buildings but are rarely connected to a network, and their potential to analyse the performance, use, and interaction with a building is not yet fully realised. In the coming years, we expect sensors in buildings to become part of the Internet of Things (IoT) and grow in numbers to form a Dense Indoor Sensor Network (DISN) that allows for unprecedented analysis of the performance, use, and interaction with buildings. Multiple technologies vie for leading this transformation. We explore Long Range Wide Area Network (LoRaWAN) as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a DISN with 390 sensor nodes and four gateways and empirically evaluated its performance for two years. Our analysis of more than 86 million transmissions revealed that DISNs achieve a much lower distance coverage compared to estimations from previous research indicating that more gateways are required. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications given sufficient gateways. We also contribute to the modelling of transmissions with our comparison of attenuation models derived from multiple methodologies. Across all models, we find that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. Finally, we also investigate the application of DISNs for the passive sensing and visualisation of human presence using a Digital Twin (DT) and a Fused Twins (FT) representation in Augmented Reality (AR). A passive sensing approach allows us to gather relevant data on human use of a building while still preserving privacy via the aggregation process. Immersive in situ visualisations in FT allow for new interactions and new forms of participation. We conclude that DISNs are already technologically feasible today and basing them on Low Power Wide Area Network (LPWAN) offers intriguing possibilities to reduce energy consumption, maintenance cost, and bandwidth use while also enabling new forms of human-building interaction.

Reinforcement learning (RL) has demonstrated great success in solving navigation tasks but often fails when learning complex environmental structures. One open challenge is to incorporate low-level generalizable skills with human-like adaptive path-planning in an RL framework. Motivated by neural findings in animal navigation, we propose a Successor eNtropy-based Adaptive Path-planning (SNAP) that combines a low-level goal-conditioned policy with the flexibility of a classical high-level planner. SNAP decomposes distant goal-reaching tasks into multiple nearby goal-reaching sub-tasks using a topological graph. To construct this graph, we propose an incremental subgoal discovery method that leverages the highest-entropy states in the learned Successor Representation. The Successor Representation encodes the likelihood of being in a future state given the current state and capture the relational structure of states based on a policy. Our main contributions lie in discovering subgoal states that efficiently abstract the state-space and proposing a low-level goal-conditioned controller for local navigation. Since the basic low-level skill is learned independent of state representation, our model easily generalizes to novel environments without intensive relearning. We provide empirical evidence that the proposed method enables agents to perform long-horizon sparse reward tasks quickly, take detours during barrier tasks, and exploit shortcuts that did not exist during training. Our experiments further show that the proposed method outperforms the existing goal-conditioned RL algorithms in successfully reaching distant-goal tasks and policy learning. To evaluate human-like adaptive path-planning, we also compare our optimal agent with human data and found that, on average, the agent was able to find a shorter path than the human participants.

Long Range Wide Area Network (LoRaWAN) has been advanced as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a Dense Indoor Sensor Network (DISN) with 390 sensor nodes and three gateways and empirically evaluated its performance for half a year. Our analysis of more than 14 million transmissions revealed that DISNs achieve a much lower distance coverage compared to previous research. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors such as concurrently received messages. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications. Our attenuation model indicates that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. We discuss the application of DISNs for the passive sensing and visualization of human presence using a Digital Twin (DT).

Landmarks play a vital role in human wayfinding by providing the structure for mental spatial representations and indicating locations with which to orient. Less research effort has been allocated towards automated landmark identification in indoor environments despite a growing interest in indoor navigation in the scientific community. In this paper, we propose a computational framework to identify indoor landmarks that is based on a hierarchical multi-criteria decision model and grounded in theories of spatial cognition and human information processing. Our model of landmark salience is represented as a hierarchical integration process of low-level features derived from a three-part, higher-level, salience vector (i.e., cognitive, spatial, and subjective salience). We use a fuzzy hierarchical composite-weighted (objective and subjective) Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to derive the rankings for identified objects at decision points (i.e., intersections). The top N objects are then selected and compared to a list of landmarks derived from an eye-tracking based virtual reality (VR) experiment. A substantial overlap of 79% was observed between these two lists. The proposed framework is capable of reliably and accurately detecting indoor landmarks, which can be employed in the development of landmark-based robot/autonomous agent motion and indoor guidance systems.

The focus of this study is on wayfinding in large complex buildings with different wayfinding devices. The interaction of pedestrians of such devices is always also interplay with the surrounding environment and its specific features. Furthermore different wayfinding assistances can elicit different needs for additional information from the environment to make accurate choices at decision points. We aim to shed light on how characteristics of decision points in combination with different wayfinding devices shape wayfinders' visual attention. 60 participants individually looked for three destinations in the same order. They navigated with 1) a printed map, 2) a digital map, or 3) without a map, only using full-coverage numeric signage. To gain first insights fixation frequencies on maps and signage as well as the correct and incorrect route options were recorded with a mobile eyetracker and analyzed for 28 decision points and four decision point categories. The results indicated that starting points play a special role in planning the route ahead. Furthermore points that allow for a floor change lead to a higher attention and information search.

This paper presents a study that investigated the potential effect of an additional sign on people's simulated wayfinding behavior in a transfer situation at an airport. Participants were presented with photographs of the status quo and digitally edited images of the potential redesign. Path choice behavior, gaze behavior and confidence ratings were analyzed. The combination of the three methods proved to capture the situation better than any of the methods alone. The results provide evidence that the re-design has a positive effect on passengers' wayfinding behavior.

No abstract available.