Interacting with multiple objects simultaneously makes us fast. A pre-step to this interaction is to select the objects, i.e., multi-object selection, which is enabled through two steps: (1) toggling multi-selection mode — mode-switching — and then (2) selecting all the intended objects — subselection. In extended reality (XR), each step can be performed with the eyes, hands, and voice. To examine how design choices affect user performance, we evaluated four mode-switching (SemiPinch, FullPinch, DoublePinch, and Voice) and three subselection techniques (Gaze+Dwell, Gaze+Pinch, and Gaze+Voice) in a user study. Results revealed that while DoublePinch paired with Gaze+Pinch yielded the highest overall performance, SemiPinch achieved the lowest performance. Although Voice-based mode-switching showed benefits, Gaze+Voice subselection was less favored, as the required repetitive vocal commands were perceived as tedious. Overall, these findings provide empirical insights and inform design recommendations for multi-selection techniques in XR.

Constant-and Dual-Threshold Dwell keyboards

State-of-the-art Virtual Reality (VR) Head-Mounted Displays (HMDs) enable users to interact with virtual objects using their hands via built-in camera systems. However, the accuracy of the hand movement detection algorithm is often affected by limitations in both camera hardware and software, including image processing & machine learning algorithms used for hand skeleton detection. In this work, we investigated a visual feedback mechanism to create an early warning system that detects hand skeleton recognition failures in VR HMDs and warns users in advance. We conducted two user studies to evaluate the system's effectiveness. The first study involved a cup stacking task, where participants stacked virtual cups. In the second study, participants performed a ball sorting task, picking and placing colored balls into corresponding baskets. During both of the studies, we monitored the built-in hand tracking confidence of the VR HMD system and provided visual feedback to the user to warn them when the tracking confidence is 'low'. The results showed that warning users before the hand tracking algorithm fails improved the system's usability while reducing frustration. The impact of our results extends beyond VR HMDs, any system that uses hand tracking, such as robotics, can benefit from this approach.

In recent years, there has been a race among researchers, developers, engineers, and designers to come up with new interaction techniques for enhancing the performance and experience of users while interacting with virtual environments, and a key component of a 3D interaction technique is the selection technique. In this paper, we explore the environmental factors used in the assessment of 3D selection methods and classify each factor based on the task environment. Our approach consists of a thorough literature collection process, including four major Human-Computer Interaction repositories—Scopus, Science Direct, IEEE Xplore, and ACM Digital Library and created a dataset of a total of 277 papers. Drawing inspiration from the parameters outlined by LaViola et al. we manually classified each of those papers based on the task environment described in the papers. In addition, we explore the methodologies used in recent user studies to assess interaction techniques within various task environments, providing valuable insights into the developing landscape of virtual interaction research. We hope that the outcomes of our paper serve as a valuable resource for researchers, developers, and designers, providing a deeper understanding of task environments and offering fresh perspectives to evaluate their proposed 3D selection techniques in virtual environments.

Pre-processing, Feature Extraction, Feature Selection and Classification are the four sub modules of the Signal Processing module of a typical BCI system. Pattern recognition is mainly involved in this Signal Processing module and in this paper, we experimented with different state-of-the-art algorithms for each of these submodules on two separate datasets we acquired using Emotiv EPOC and the Muse headband from 38 college-aged young adults. For our experiment, we used two artefact removal techniques, namely Stationary Wavelet Transform (SWT) based denoising technique and an extended SWT technique (SWTSD). We found SWTSD improves average classification accuracy up to 7.2 % and performs better than SWT. However, that does not state that SWTSD will outperform SWT when implemented on other BCI paradigms or on other EEG-based applications. In our study, the highest average accuracy achieved by the data of the Muse headband and Emotiv EPOC were 77.7% and 66.7% respectively and from our results we conclude that, the performance of different BCI systems depends on several different factors including artefact removal techniques, filters, feature extraction and selection algorithms, classifiers, etc. and appropriate choice and usage of such methods can have a significant positive impact on the end results.