Human–robot interactions are often affected by error situations that are caused by either the robot or the human. Therefore, robots would profit from the ability to recognize when error situations occur. We investigated the verbal and non-verbal social signals that humans show when error situations occur in human–robot interaction experiments. For that, we analyzed 201 videos of five human–robot interaction user studies with varying tasks from four independent projects. The analysis shows that there are two types of error situations: social norm violations and technical failures. Social norm violations are situations in which the robot does not adhere to the underlying social script of the interaction. Technical failures are caused by technical shortcomings of the robot. The results of the video analysis show that the study participants use many head movements and very few gestures, but they often smile, when in an error situation with the robot. Another result is that the participants sometimes stop moving at the beginning of error situations. We also found that the participants talked more in the case of social norm violations and less during technical failures. Finally, the participants use fewer non-verbal social signals (for example smiling, nodding, and head shaking), when they are interacting with the robot alone and no experimenter or other human is present. The results suggest that participants do not see the robot as a social interaction partner with comparable communication skills. Our findings have implications for builders and evaluators of human–robot interaction systems. The builders need to consider including modules for recognition and classification of head movements to the robot input channels. The evaluators need to make sure that the presence of an experimenter does not skew the results of their user studies.

Virtual Reality (VR) provides great opportunities for police officers to train decision-making and acting (DMA) in cognitively demanding and stressful situations. This paper presents a summary of findings from a three-year project, including requirements collected from experienced police trainers and industry experts, and quantitative and qualitative results of human factor studies and field trials. Findings include advantages of VR training such as the possibility to safely train high-risk situations in controllable and reproducible training environments, include a variety of avatars that would be difficult to use in real-life training (e.g., vulnerable populations or animals) and handle dangerous equipment (e.g., explosives) but also highlight challenges such as tracking, locomotion and intelligent virtual agents. The importance of strong alignment between training didactics and technical possibilities is highlighted and potential solutions presented. Furthermore training outcomes are transferable to real-world police duties and may apply to other domains that would benefit from simulation-based training.

We present the collected findings of a user-centred approach for developing a tele-operated robot for remote echocardiography examinations. During the three-year development of the robot, we involved users in all development stages of the robot, to increase the usability of the system for the doctors. For requirement compilation, we conducted a literature review, observed two traditional examinations, arranged focus groups with doctors and patients, and conducted two online surveys. During the development of the robot, we regularly involved doctors in usability tests to receive feedback from them on the user interface for the robot and on the robot’s hardware. For evaluation of the robot, we conducted two eye tracking studies. In the first study, doctors executed a traditional echocardiography examination. In the second study, the doctors conducted a remote examination with our robot. The results of the studies show that all doctors were able to successfully complete a correct ultrasonography examination with the tele-operated robot. In comparison to a traditional examination, the doctors on average only need a short amount of additional time to successfully examine a patient when using our remote echocardiography robot. The results also show that the doctors fixate considerably more often, but with shorter fixation times, on the USG screen in the traditional examination compared to the remote examination. We found further that some of the user-centred design methods we applied had to be adjusted to the clinical context and the hectic schedule of the doctors. Overall, our experience and results suggest that the usage of user-centred design methodology is well suited for developing medical robots and leads to a usable product that meets the end users’ needs.

In medical simulation training two approaches are currently rather disjunct: realistic manikins are used to teach physical skills and procedures and VR systems are used to train situation assessment and decision making. We propose a mixed reality approach, which allows trainees to use real tools and their hands when interacting with a physical manikin overlaid with a responsive virtual avatar. In close exchange with first responder organizations, we developed and evaluated an MR training scenario. In the scenario, users can talk to injured people in a car accident, assess the threat of the environment, and utilize real medical equipment. Participants experienced high levels of physical- and self-presence, increased stress levels, and reported a high technology acceptance. The proposed main requirements of first responders regarding haptic multi-sensory skill training in MR and the lessons learned from the workshop aim to guide the design of training solutions for medical training in MR.