Biblio

Stress-related disorders are increasing because of work load, forces in teamwork, surroundings pressures and health related conditions. Thus, to avoid people living under heavy stress and develop more severe stress-related disorders, different internet and applications of stress management interventions are offered. Mobile applications with self-assessed health, burnout-scores and well-being are commonly used as outcome measures. Few studies have used sickleave to compare effects of stress interventions. A new approach is to use nature and garden in a multimodal stress management context. This study aimed to explore the effects of immersive and non-immersive games application by using nature theme virtual stress therapy in reducing stress level. Two weeks’ of experiments had involved 18 participants. Nine (9) of them were invited to join the first experiment which focused on immersive virtual reality (VR) experience. Their Blood Volume Pulse with Heart Rate (BVP+HR) and Skin Conductance (SC) were recorded using BioGraph Infiniti Biofeedback System that comes with three (3) sensors attached to the fingers. The second experiment were joined by another nine (9) participants. This experiment was testing on non-immersive desktop control experience. The same protocol measurements were taken which are BVP+HR and SC. Participants were given the experience to feel and get carried into the virtual nature as a therapy so that they will reduce stress. The result of this study points to whether immersive or non-immersive VR display using nature theme virtual therapy would reduce individuals stress level. After conducted series of experiments, results showed that both immersive and non-immersive VR display reduced stress level. However, participants were satisfied of using the immersive version as it provided a 360 degree of viewing, immersed experiences and feeling engaged. Thus, this showed and proved that applications developed with nature theme affect successfully reduce stress level no matter it is put in immersive or non-immersive display.

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a mission team, has been widely used for emergent scenarios such as criminal tracking and victim assistance. These scenarios are related to human safety and require a robot team to quickly transit from the current undergoing task into the new emergent task. This sudden mission change brings difficulty in robot motion adjustment and increases the risk of performance degradation of the swarm. Trust in human-human collaboration reflects a general expectation of the collaboration; based on the trust humans mutually adjust their behaviors for better teamwork. Inspired by this, in this research, a trust-aware reflective control (Trust-R), was developed for a robot swarm to understand the collaborative mission and calibrate its motions accordingly for better emergency response. Typical emergent tasks “transit between area inspection tasks”, “response to emergent target - car accident” in social security with eight fault-related situations were designed to simulate robot deployments. A human user study with 50 volunteers was conducted to model trust and assess swarm performance. Trust-R's effectiveness in supporting a robot team for emergency response was validated by improved task performance and increased trust scores.

How team members are treated influences their performance in the team and their desire to be a part of the team in the future. Prior research in human-robot teamwork proposes fairness definitions for human-robot teaming that are based on the work completed by each team member. However, metrics that properly capture people’s perception of fairness in human-robot teaming remains a research gap. We present work on assessing how well objective metrics capture people’s perception of fairness. First, we extend prior fairness metrics based on team members’ capabilities and workload to a bigger team. We also develop a new metric to quantify the amount of time that the robot spends working on the same task as each person. We conduct an online user study (n=95) and show that these metrics align with perceived fairness. Importantly, we discover that there are bleed-over effects in people’s assessment of fairness. When asked to rate fairness based on the amount of time that the robot spends working with each person, participants used two factors (fairness based on the robot’s time and teammates’ capabilities). This bleed-over effect is stronger when people are asked to assess fairness based on capability. From these insights, we propose design guidelines for algorithms to enable robotic teammates to consider fairness in its decision-making to maintain positive team social dynamics and team task performance.

As robotic capabilities improve and robots become more capable as team members, a better understanding of effective human-robot teaming is needed. In this paper, we investigate failures by robots in various team configurations in space EVA operations. This paper describes the methodology of extending and the application of Work Models that Compute (WMC), a computational simulation framework, to model robot failures, interruptions, and the resolutions they require. Using these models, we investigate how different team configurations respond to a robot's failure to correctly complete the task and overall mission. We also identify key factors that impact the teamwork metrics for team designers to keep in mind while assembling teams and assigning taskwork to the agents. We highlight different metrics that these failures impact on team performance through varying components of teaming and interaction that occur. Finally, we discuss the future implications of this work and the future work to be done to investigate function allocation in human-robot teams.