Gesture-based locomotion enhances immersion in virtual reality (VR), with seated motion being crucial for accessibility and prolonged use. However, existing techniques often apply uniform gesture-to-walking speed mappings, ignoring the fact that different gestures involve varying levels of physical effort and subjective impressions. This mismatch can degrade the user experience. This study investigates how three seated gestures with different physical loads—Tap-in-Place (TIP), Swing-in-Place (SIP), and Grip-in-Place (GIP)—influence users’ expected walking speed. While the evaluations revealed unique experiential trade-offs for each gesture, our primary finding is a consistent perceptual pattern in the expectation of walking speed: Users expected to walk fastest with SIP, followed by GIP, then TIP (SIP > GIP > TIP). These results demonstrate that a one-size-fits-all approach is insufficient and provide empirical recommendations for designing more intuitive seated VR locomotion systems that align walking speed with user perception.

When humans generate stimuli voluntarily, they perceive the stimuli more weakly than those produced by others, which is called sensory attenuation (SA). SA has been investigated in various body parts, but it is unclear whether an extended body induces SA. This study investigated the SA of audio stimuli generated by an extended body. SA was assessed using a sound comparison task in a virtual environment. We prepared robotic arms as extended bodies, and the robotic arms were controlled by facial movements. To evaluate the SA of robotic arms, we conducted two experiments. Experiment 1 investigated the SA of the robotic arms under four conditions. The results showed that robotic arms manipulated by voluntary actions attenuated audio stimuli. Experiment 2 investigated the SA of the robotic arm and innate body under five conditions. The results indicated that the innate body and robotic arm induced SA, while there were differences in the sense of agency between the innate body and robotic arm. Analysis of the results indicated three findings regarding the SA of the extended body. First, controlling the robotic arm with voluntary actions in a virtual environment attenuates the audio stimuli. Second, there were differences in the sense of agency related to SA between extended and innate bodies. Third, the SA of the robotic arm was correlated with the sense of body ownership.

There is a method to recognize facial expressions of Head-Mounted Display (HMD) wearers by machine learning of reflection intensity information from photo-reflective sensors embedded into the interior of an HMD [1]. This study evaluates whether facial expression recognition accuracy can be improved by using a learning model that considers temporal changes in sensor values. We assessed whether facial expression recognition accuracy could be improved by adding the head posture data acquired from the Inertial Measurement Unit (IMU) in the HMD to the discriminator input and performing time-series learning. The experimental results showed that PRS-based facial expression recognition with time-series data was more accurate than without. The multimodal recognition using the reflection intensity and head posture data was slightly more accurate than the discrimination using only the reflection intensity information. It was especially effective for the learning condition without considering the time-series.

As one of the facial expression recognition techniques for Head-Mounted Display (HMD) users, embedded photo-reflective sensors have been used. In this paper, we investigate how gaze and face directions affect facial expression recognition using the embedded photo-reflective sensors. First, we collected a dataset of five facial expressions (Neutral, Happy, Angry, Sad, Surprised) while looking in diverse directions by moving 1) the eyes and 2) the head. Using the dataset, we analyzed the effect of gaze and face directions by constructing facial expression classifiers in five ways and evaluating the classification accuracy of each classifier. The results revealed that the single classifier that learned the data for all gaze points achieved the highest classification performance. Then, we investigated which facial part was affected by the gaze and face direction. The results showed that the gaze directions affected the upper facial parts, while the face directions affected the lower facial parts. In addition, by removing the bias of facial expression reproducibility, we investigated the pure effect of gaze and face directions in three conditions. The results showed that, in terms of gaze direction, building classifiers for each direction significantly improved the classification accuracy. However, in terms of face directions, there were slight differences between the classifier conditions. Our experimental results implied that multiple classifiers corresponding to multiple gaze and face directions improved facial expression recognition accuracy, but collecting the data of the vertical movement of gaze and face is a practical solution to improving facial expression recognition accuracy.

We propose a facial expression mapping technology between virtual avatars and Head-Mounted Display (HMD) users. HMD allow people to enjoy an immersive Virtual Reality (VR) experience. A virtual avatar can be a representative of the user in the virtual environment. However, the synchronization of the the virtual avatar's expressions with those of the HMD user is limited. The major problem of wearing an HMD is that a large portion of the user's face is occluded, making facial recognition difficult in an HMD-based virtual environment. To overcome this problem, we propose a facial expression mapping technology using retro-reflective photoelectric sensors. The sensors attached inside the HMD measures the distance between the sensors and the user's face. The distance values of five basic facial expressions (Neutral, Happy, Angry, Surprised, and Sad) are used for training the neural network to estimate the facial expression of a user. We achieved an overall accuracy of 88% in recognizing the facial expressions. Our system can also reproduce facial expression change in real-time through an existing avatar using regression. Consequently, our system enables estimation and reconstruction of facial expressions that correspond to the user's emotional changes.