In high-pressure environments where human individuals must simultaneously monitor multiple entities, communicate effectively, and maintain intense focus, the perception of time becomes a critical factor influencing performance and well-being. One indicator of well-being can be the person's subjective time perception. In our project ChronoPilot, we aim to develop a device that modulates human subjective time perception. In this study, we present a method to automatically assess the subjective time perception of air traffic controllers, a group often faced with demanding conditions, using their physiological data and eleven state-of-the-art machine learning classifiers. The physiological data consist of photoplethysmo-gram, electrodermal activity, and temperature data. We find that the support vector classifier works best with an accuracy of 79 % and electrodermal activity provides the most descriptive biomarker. These findings are an important step towards closing the feedback loop of our ChronoPilot-device to automatically modulate the user's subjective time perception. This technological advancement may promise improvements in task management, stress reduction, and overall productivity in high-stakes professions.

Many large-scale multi-robot systems require human input during operation in different applications. To still minimize the human effort, interaction is intermittent or restricted to a subset of robots. Despite this reduced demand for human interaction, the mental load and stress can be challenging for the human operator. A specific effect of human-swarm interaction may be a hypothesized change of subjective time perception in the human operator. In a series of simple human-swarm interaction experiments with robot swarms of up to 15 physical robots, we study whether human operators have altered time perception due to the number of controlled robots or robot speeds. Using data gathered by questionnaires, we found that increased swarm size shrinks perceived time and decreased robot speeds expand the perceived time. We introduce the concept of subjective time perception to human-swarm interaction. Future research will enable swarm systems to autonomously modulate subjective timing to ease the job of human operators.

Computational saliency models aim at predicting, in a bottom-up fashion, where human attention is drawn in the presented (visual, auditory or audiovisual) scene and have been proven useful in applications like robotic navigation, image compression and movie summarization. Despite the fact that well-established auditory and visual saliency models have been validated in behavioral experiments, e.g., by means of eye-tracking, there is no established computational audiovisual saliency model validated in the same way. In this work, building on biologically-inspired models of visual and auditory saliency, we present a joint audiovisual saliency model and introduce the validation approach we follow to show that it is compatible with recent findings of psychology and neuroscience regarding multimodal integration and attention. In this direction, we initially focus on the "pip and pop" effect which has been observed in behavioral experiments and indicates that visual search in sequences of cluttered images can be significantly aided by properly timed non-spatial auditory signals presented alongside the target visual stimuli.

A good data corpus lies at the heart of progress in both perceptual/cognitive science and in computer vision. While there are a few datasets that deal with simple actions, creating a realistic corpus for complex, long action sequences that contains also human-human interactions has so far not been attempted to our knowledge. Here, we introduce such a corpus for (inter)action understanding that contains six everyday scenarios taking place in a kitchen / living-room setting. Each scenario was acted out several times by different pairs of actors and contains simple object interactions as well as spoken dialogue. In addition, each scenario was first recorded with several HD cameras and also with motion-capturing of the actors and several key objects. Having access to the motion capture data allows not only for kinematic analyses, but also allows for the production of realistic animations where all aspects of the scenario can be fully controlled. We also present results from a first series of perceptual experiments that show how humans are able to infer scenario classes, as well as individual actions and objects from computer animations of everyday situations. These results can serve as a benchmark for future computational approaches that begin to take on complex action understanding.