See-Through (ST) 2-Hand (2H) Arrow (AR) Vibration (VB) Figure 1: Visual feedback techniques investigated in our studies. ABSTRACTWe investigate visual feedback for virtual grasps, especially cues to improve behavior after real fmgers enter a virtual object. To date, such visual cues have usually been developed in an ad-hoc manner, with minimal or no studies that can guide selection. Existing guidelines are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We compare several different visual feedback types including those most commonly seen for virtual hand interaction and with some novel visual aspects. The visuals were tuned in a pilot study, and our main study evaluated results in terms of objective performance (fmger penetration, release time, and precision) and sUbjective rankings. Performancewise, the most promising techniques all directly reveal penetrating hand configuration in some way. Subjectively, most techniques are better than simple interpenetrating visuals, with color changes being most promising. The results enable selection of the best cues based on the relevant tradeoffs. Results also provide a needed basis for more focused studies of specific visual cues and for better informing studies of multi modal feedback.

We present a haptic feedback technique that combines feedback from a portable force-feedback glove with feedback from direct contact with rigid passive objects. This approach is a haptic analogue of visual mixed reality, since it can be used to haptically combine real and virtual elements in a single display. We discuss device limitations that motivated this combined approach and summarize technological challenges encountered. We present three experiments to evaluate the approach for interactions with buttons and sliders on a virtual control panel. In our first experiment, this approach resulted in better task performance and better subjective ratings than the use of only a force-feedback glove. In our second experiment, visual feedback was degraded and the combined approach resulted in better performance than the glove-only approach and in better ratings of slider interactions than both glove-only and passive-only approaches. A third experiment allowed subjective comparison of approaches and provided additional evidence that the combined approach provides the best experience.

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Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.