Existing virtual reality (VR) street view and 360-degree road view applications often rely on complex controllers or touch interfaces, which can hinder user immersion and accessibility. These challenges are particularly pronounced for under-represented populations, such as older adults and individuals with limited familiarity with digital devices. Such groups frequently face physical or environmental constraints that restrict their ability to engage in outdoor activities, highlighting the need for alternative methods of experiencing the world through virtual environments. To address this issue, we propose a VR street view application featuring an intuitive, gesture-based interface designed to simplify user interaction and enhance accessibility for socially disadvantaged individuals. Our approach seeks to optimize digital accessibility by reducing barriers to entry, increasing user immersion, and facilitating a more inclusive virtual exploration experience. Through usability testing and iterative design, this study evaluates the effectiveness of gesture-based interactions in improving accessibility and engagement. The findings emphasize the importance of user-centered design in fostering an inclusive VR environment that accommodates diverse needs and abilities.

This paper presents ITap, a novel interaction method utilizing hand tracking to create a virtual touchpad on a tabletop. ITap facilitates touch interactions such as tapping, dragging, and swiping using the index finger. The technique combines gaze-based object selection with touch gestures, while a pinch gesture performed with the opposite hand activates a manual mode, enabling precise cursor control independently of gaze direction. The primary purpose of this research is to enhance interaction efficiency, reduce user fatigue, and improve accuracy in gaze-based object selection tasks, particularly in complex and cluttered XR environments. Specifically, we addressed two research questions: (1) How does ITap’s manual mode compare with the traditional gaze + pinch method regarding speed and accuracy in object selection tasks across varying distances and densities? (2) Does ITap provide improved user comfort, naturalness, and reduced fatigue compared to the traditional method during prolonged scrolling and swiping tasks? To evaluate these questions, two studies were conducted. The first study compared ITap’s manual mode with the traditional gaze + pinch method for object selection tasks across various distances and in cluttered environments. The second study examined both methods for scrolling and swiping tasks, focusing on user comfort, naturalness, and fatigue. The findings revealed that ITap outperformed gaze + pinch in terms of object selection speed and error reduction, particularly in scenarios involving distant or densely arranged objects. Additionally, ITap demonstrated superior performance in scrolling and swiping tasks, with participants reporting greater comfort and reduced fatigue. The integration of gaze-based input and touch gestures provided by ITap offers a more efficient and user-friendly interaction method compared to the traditional gaze + pinch technique. Its ability to reduce fatigue and improve accuracy makes it especially suitable for tasks involving complex environments or extended usage in XR settings.

This paper proposes a method of predicting future medical examination measurements given the past values. The medical examinations considered in this paper are blood sugar level, low and high blood pressures, and cholesterol level. This paper uses a specific type of artificial neural networks, radial-basis function network (RBFN), to approximate mapping from the past medical measurements to that of the upcoming year, in order to help the subjects be aware of the signs of unusual health states without consulting with doctors. Experimental results show that the RBFN-based estimation is superior to the conventional linear regression in terms of prediction accuracy of the future examination measurements. The proposed method is expected to be implemented in a handy consumer electronic devices such as Smartphones without adding extra hardware parts provided that the history of the medical examination measurements are available.

This study proposes a novel algorithm for effectively estimating partial upper and lower body movements in a Virtual Reality (VR) environment using only head movements and synchronized head rotation axes, without the need for additional hardware. The proposed algorithm calculates the angle between the avatar’s pelvis and the head rotation axis to naturally reproduce the user’s upper body inclination and lower body bending. Notably, it offers the advantage of efficiently utilizing limited computational resources in multiplayer environments. The experiment was conducted in two stages. In the first stage, the objective performance of the algorithm was evaluated by comparing it with ground truth inclination data. In the second stage, participants performed two types of games (e.g., a dodgeball game and a limbo game) to assess their sense of immersion and embodiment. The objective results demonstrated that the proposed algorithm accurately and naturally expressed upper and lower body movements. Additionally, post-experiment surveys indicated that participants reported a high level of immersion and a natural interaction experience. This study presents a cost-effective solution for tracking upper and lower body movements in VR environments without requiring additional hardware, significantly enhancing the immersion of the VR experience. Future research will explore the expansion of the method to include upper body rotation estimation and full-body motion tracking, incorporating user locomotion.