In mixed reality environments, virtual objects can obscure real-world obstacles, creating a risk of collision when users walk through them. Although users may choose to detour around virtual objects, this behavior also carries risks, such as colliding with obstacles or pedestrians along the detour route. To reduce collision risks, it is essential to understand the factors that determine whether users walk through or detour, as well as the walking paths associated with each behavior. In this research, we investigated users’ walking behavior toward both a static virtual obstacle and a virtual obstacle that disappeared as the user approached. Our findings suggest that individual characteristics and the width of the virtual obstacle influence the decision to walk through or detour. Furthermore, while most users initially chose paths that detoured around the virtual obstacle, once the obstacle began to disappear, they switched their walking paths toward the space where it had been.

Expanding an object’s region in motor space (selection area) using Voronoi tessellation can facilitate the selection of small targets in gaze-based interfaces. While visual feedforward, such as visualizing the selection area, has been shown to improve selection performance in hand-based interfaces, its influence on gaze-based interfaces remains unclear. To address this, we conducted two user studies to examine how visual feedforward affects fixation behavior and selection performance when the centers of a target and its selection area differ. The results reveal that visualizing either the selection area or its center effectively guides users’ gaze toward the center of the selection area, thereby improving selection accuracy. Furthermore, displaying a gaze cursor reduces errors by enabling users to adjust their fixation, even though it does not directly guide gaze toward the center of the selection area.

Electrical muscle stimulation (EMS) can directly induce finger flexion, providing a promising supplementary modality for information presentation beyond vision and audition. However, the perceptual resolution of EMS remains poorly understood, particularly when applied to different fingers. We conducted a systematic experiment to examine how accurately humans can discriminate multiple levels of EMS intensity applied to different fingers. Results showed that participants were able to discriminate EMS intensities both with and without visual access to their fingers, indicating that reliable recognition was possible even in the absence of visual feedback. Across fingers and visibility conditions, mean accuracies reached 87.5%, 72.9%, and 60.6% for the 3-, 4-, and 5-step conditions. The little and middle fingers yielded higher performance than the index and ring fingers. These findings provide the first systematic characterization of finger-specific perceptual limits and error patterns in EMS-based interaction, and derive design guidelines for allocating signals across fingers and intensity steps.

This paper presents a system that authenticates users solely based on the action of grasping a door handle.The system authenticates users by utilizing the differences in frequency characteristics between a hand and a door handle when grasped.The frequency characteristics of a hand vary from person to person depending on anatomical differences such as skeletal structure and muscle composition.This system is beneficial because it is not vulnerable to shoulder surfing and allows authentication solely by grasping the door handle.Data were collected from 25 users, and the system achieved an F1 score of 93.0% in the 25-class classification.In the One-vs-Rest classification using unknown users as test data, the average equal error rate was 4.76%.

Thumb-to-finger gestures, such as 12 phalange taps, provide diverse input.This gesture enables eyes-free, rich, and one-handed smartwatch controls.Previous research on recognizing thumb-to-finger gestures has typically relied on additional sensors, making it difficult to achieve recognition using only a commercial off-the-shelf (COTS) smartwatch.In this paper, we developed a thumb-to-finger gesture recognition system that only uses the accelerometers built into a COTS smartwatch.Our recognition system achieved 80.1% accuracy for 17 gestures.Additionally, we developed optimal gesture sets for different numbers of gestures based on the recognition performance of the accelerometer.Consequently, we achieved 94.7% accuracy for 4 gestures in leave-one-participant-out crossvalidation and 90.2% accuracy for 11 gestures in participant-specific leave-one-session-out cross-validation.

The maximum physical range of horizontal human eye movement is approximately 45°. However, in a natural gaze shift, the difference in the direction of the gaze relative to the frontal direction of the head rarely exceeds 25°. We name this region of 25° − 45° the “Kuiper Belt” in the eye-gaze interaction. We try to utilize this region to solve the Midas touch problem to enable a search task while reducing false input in the Virtual Reality environment. In this work, we conduct two studies to figure out the design principle of how we place menu items in the Kuiper Belt as an “out-of-natural angle” region of the eye-gaze movement, and determine the effectiveness and workload of the Kuiper Belt-based method. The results indicate that the Kuiper Belt-based method facilitated the visual search task while reducing false input. Finally, we present example applications utilizing the findings of these studies.

We conducted two studies exploring how an area cursor technique can improve the eye-gaze interface. We first examined the bubble cursor technique. We developed an eye-gaze-based cursor called the bubble gaze cursor and compared it to a standard eye-gaze interface and a bubble cursor with a mouse. The results revealed that the bubble gaze cursor interface was faster than the standard point cursor-based eye-gaze interface. In addition, the usability and mental workload were significantly better than those of the standard interface. Next, we extended the bubble gaze cursor technique and developed a bubble gaze lens. The results indicated that the bubble gaze lens technique was faster than the bubble gaze cursor method and the error rate was reduced by 54.0%. The usability and mental workload were also considerably better than those of the bubble gaze cursor.