Mohammad Raihanul Bashar

Dwell-based text entry seems to peak at 20 words per minute (WPM). Yet, little is known about the factors contributing to this limit, except that it requires extensive training. Thus, we conducted a longitudinal study, broke the overall dwell-based selection time into six different components, and identified several design challenges and opportunities. Subsequently, we designed two novel dwell keyboards that use multiple yet much shorter dwell thresholds: Dual-Threshold Dwell (DTD) and Multi-Threshold Dwell (MTD). The performance analysis showed that MTD (18.3 WPM) outperformed both DTD (15.3 WPM) and the conventional Constant-Threshold Dwell (12.9 WPM). Notably, absolute novices achieved these speeds within just 30 phrases. Moreover, MTD’s performance is also the fastest-ever reported average text entry speed for gaze-based keyboards. Finally, we discuss how our chosen parameters can be further optimized to pave the way toward more efficient dwell-based text entry.

Due to the increasing availability and popularity of virtual reality (VR) systems, 3D sketching applications have also boomed. Most of these applications focus on peripersonal sketching, e.g., within arm’s reach. Yet, sketching in larger scenes requires users to walk around the virtual environment while sketching or to change the sketch scale repeatedly. This paper presents Depth3DSketch, a 3D sketching technique that allows users to sketch objects up to 2.5 m away with a freehand sketching technique. Users can select the sketching depth with three interaction methods: using the joystick on a single controller, the intersection from two controllers, or the intersection from the controller ray and the user’s gaze. We compared these interaction methods in a user study. Results show that users preferred the joystick to select visual depth, but there was no difference in user accuracy or sketching time between the three methods.

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.

Electroencephalography (EEG) captures brain signals from Scalp. If analyzed and patterns are recognized properly this has a high potential application in medicine, psychology, rehabilitation, and many other areas. However, EEG signals are inherently noise-prone, and it is not possible for human to see patterns in raw signals most of the time. Application of appropriate computational intelligence is must to make sense of the raw EEG signals. Moreover, if the signals are collected by a consumer grade wireless EEG acquisition device, the amount of interference is ever more complex to avoid, and it becomes impossible to see any sorts of pattern without proper use of computational intelligence to discover patterns. The objective of EEG based Brain-Computer Interface (BCI) systems is to extract specific signature of the brain activity and to translate them into command signals to control external devices or understand human brains action mechanism to stimuli. A typical BCI system is comprised of a Signal Processing module which can be further broken down into four submodules namely, Pre-processing, Feature Extraction, Feature Selection and Classification. Computational intelligence is the key to identify and extract features also to classify or discover discriminating characteristics in signals. In this chapter we present an overview how computational intelligence is used to discover patterns in brain signals. From our research we conclude that, since EEG signals are the outcome of a highly complex non-linear and non-stationary stochastic biological process which contain a wide variety of noises both from internal and external sources; thus, the use of computational intelligence is required at every step of an EEG-based BCI system starting from removing noises (using advanced signal processing techniques such as SWTSD, ICA, EMD, other than traditional filtering by identifying/exploiting different artifact/noise characteristics/patterns) through feature extraction and selection (by using unsupervised learning like PCA, SVD, etc.) and finally to classification (either supervised learning based classifier like SVM, probabilistic classifier like NB or unsupervised learning based classifiers like neural networks namely RBF, MLP, DBN, k-NN, etc.). And the usage of appropriate computational intelligence significantly improves the end results.