The visual sensory system is a well-researched system. While virtual reality (VR) is a predominantly visual experience, there is still the auditory and tactile aspects that can be considered. This study examines finding invisible static and dynamic targets in virtual reality with the help of vibrotactile and auditory cues. Using a ghost hunting setting, a game was developed to investigate the different types of cues as search indicators. The game consisted of three levels, the first with a static ghost, the second with one dynamic ghost and the third with two dynamic ghosts. Forty-two participants received vibrotactile feedback cues, auditory feedback cues and the combination of both. They played the game in three trials, one per each feedback condition, in three different levels. Each participant played the game twice. The results suggest that the combination of both types of cues might be the best to use in a simple non-visual search setting, but the non-visual cues do not matter in a complex non-visual search. Further implications on the real world are discussed, i.e. search of and navigation to temporarily out-of-view buildings in the real world.

There is substantial evidence that young adults aren't exercising enough, which harms their health. Although occupational health services for students are expanding, they're often not used due to time, motivation, and accessibility constraints. Research has demonstrated that gamification and mobile solutions can effectively address these challenges. In light of these, we have developed a gamified mobile application, comprising various game levels and components, which was refined through successive design iterations. This article illustrates the steps in the game development process that have led to the greatest advances in user-friendliness and determines which optimizations achieve a top rating. The analysis was conducted after each game round, with the game elements optimized for the target audience. The results of the study, derived from four iterative design loops with N = 455 participants, are empirically proven and provide actionable recommendations for the efficient development of mobile health applications.

Biometric systems to measure inherent human characteristics in combination with methods of Artificial Intelligence (AI), can result in innovative applications, especially in the fields of remote access systems and human-machine interfaces. One of these involves the use of Electroencephalography (EEG) equipment to monitor and visualize brain activity. Besides traditional EEG applications, such as medical aspects and neuroscience purposes, it can be used to distinguish individuals based on unique and characteristic signals. The ability to recognize a person without their physical attendance is the first step for future remote access to Brain-Computer Interface (BCI) applications. Therefore, in this work, a novel Deep Neural Network (DNN), based on inception modules with a kernel-adapted modification, is developed. Inception-based DNNs recently gained much interest for Time Series Classification (TSC), since they provide comparable performance to very deep Convolutional Neural Networks (CNN) with much less computational complexity. To the best of knowledge, this is the first inception-based DNN developed for authentication purposes using EEG. To validate the proposed network's performance it is compared to three other inception-based DNNs, namely: InceptionTime, EEG-Inception, and EEG-ITNet. Using an anonymized dataset of 22 participants with a length of 1 s per epoch, the proposed network achieves an average recall and precision of 99.1 % and 99.4 %, respectively, outperforming the other DNNs, with EEG-Inception achieving the best results with an average recall and precision of 96.8 % and 97.1 %, respectively.

Virtual reality (VR) users interact with virtual objects using motion-tracked controllers. While many devices utilise abstract button pushes for interactions, some allow for limited finger tracking by estimating finger positions based on sensors. In this study, the Vive Wands and the Valve Index controllers were compared in three tasks: direct interaction with objects (throwing), tool usage (bow) and indirect control of a character (remote-control). Forty-four participants completed each task with both devices and rated the usability of the device after each task. Results showed only differences in preference for the remote-control task. Some participants noted that using the thumbstick of the Index instead of the touchpad of the Wands controller felt more natural in this task. However, performance did not differ between devices in any task. Therefore, future research should not only compare designs of controllers but also consider assets and interactions, as there may be preference and performance differences for certain combinations.

The feasible solutions in the robust recoverable selection problem are subsets of size p that are to be selected from a ground set of size n. The objective is to construct a feasible solution in two sequential stages with two separate (but interleaved) cost structures. The fastest algorithm for this problem in the literature up to now has quadratic running time. We improve on this by developing an algorithm with linear running time.