In a question-and-answer setting, the respondent is often not only communicating the requested information but also indicating their confidence in the answer through various behavioral cues. Humans excel at interpreting these cues and monitoring the uncertainty of other persons. Being able to detect human uncertainty in human-robot interactions in a similar way can enable future robotic systems to better recognize uncertain and error-prone human input. Additionally, automatic human uncertainty detection can enhance the responsiveness of robots to the user in moments of uncertainty by providing help or clarification. While there is some work on uncertainty detection based on a single modality, only a few works focus on multi-modal uncertainty detection. Even fewer works explore how human uncertainty manifests through behavioral cues in human-robot interactions. In this work, we analyze occurrences of behavioral cues related to self-reported uncertainty on experimental data from 27 participants across two decision-making tasks. Additionally, in the first task, we varied if participants interacted with a human or a robot. On the recorded data, we extract features accessible via a webcam and a microphone and train a multi-modal classifier. Experimental evaluation of our developed classifier shows that it significantly outperforms third-person annotators in accuracy and F1 score. Humans report feeling less observed when responding to a robot compared to a human. Nevertheless, we found that the behavioral differences did not significantly affect the performance of our proposed uncertainty classification.

Virtual Reality (VR) bicycle simulations aim to recreate the feeling of riding a bicycle and are commonly used in many application areas. However, current solutions still create mismatches between the visuals and physical movement, which causes VR sickness and diminishes the cycling experience. To reduce VR sickness in bicycle simulators, we conducted two controlled lab experiments addressing two main causes of VR sickness: (1) steering methods and (2) cycling trajectory. In the first experiment (N = 18) we compared handlebar, HMD, and upper-body steering methods. In the second experiment (N = 24) we explored three types of movement in VR (1D, 2D, and 3D trajectories) and three countermeasures (airflow, vibration, and dynamic Field-of-View) to reduce VR sickness. We found that handlebar steering leads to the lowest VR sickness without decreasing cycling performance and airflow suggests to be the most promising method to reduce VR sickness for all three types of trajectories.