Acquiring motor skills, such as dance, is considered a difficult task, as novice learners frequently encounter challenges in promptly comprehending and adjusting their movements in learning contexts. In this study, we present a virtual environment training system that incorporates haptic feedback and generative AI to support novices’ learning of body movements in dance. The system guides the user through haptic feedback in VR, generated from dance sequence data captured from professional choreographers. User studies with novice participants were conducted to 1) establish effective feedback for guidance and 2) evaluate the user’s training experiences using the system. We also conducted comprehensive interviews with participants and experts to confirm the design strategies and seek insights regarding the perception and utilization of virtual environments, along with haptic and visual feedback in dance learning processes. Based on the outcomes, we established the design guidelines for virtual environment training systems that facilitate learning bodily movement regulation, including in dance education.

Cybersickness is a major obstacle to immersive VR experiences, and various strategies have been proposed to overcome it. In particular, providing artificial sensory stimuli is considered a prominent approach, which improves presence and real-virtual congruence, thereby helping the situation. In this study, we evaluated the effectiveness of artificial sensory stimuli in inducing virtual walking sensations in mitigating cybersickness. We conducted two experiments involving a total of 80 participants: (1) a static user experiment ($\mathrm{N}=40$), in which participants used controller-based steering without physical movement, and (2) a dynamic user experiment ($\mathrm{N}=40$), in which participants walked on an omnidirectional treadmill, enabling virtual movement similar to real walking. In both experiments, participants were divided into two groups based on the presence or absence of vibrotactile feedback. We collected heart rate and subjective discomfort data during exposure to a VR environment and then evaluated cybersickness and presence. Across both experiments, the results indicated that virtual walking sensations induced through vibrotactile feedback significantly reduced cybersickness without negatively affecting the perceived task load or presence. Additionally, in the dynamic user locomotion experiment, a greater reduction in cybersickness was observed when vibrotactile feedback was present. These findings suggest that even if the real-virtual sensations are synchronized, vibrotactile feedback may induce cognitive distraction that further mitigates cybersickness.

This study investigated the effect of vibrotactile stimulus location on the balancing task in virtual reality (VR). Using a virtual snowboarding system with wearable haptic devices, we conducted a between-subject user study comparing the effectiveness of two different body locations–upper body (UB; torso vibrations) and lower body (LB; ankle vibrations). The real-time vibrotactile balance-correction feedback was generated by the Center of Pressure (CoP) calculated from the sensor array on insoles. The initial results showed that UB feedback is better than LB to improve users’ balance ability.

This paper compares force-feedback methods for sensorimotor skill transfer in a VR engraving art task. Using a 3-DoF force-feedback device, we implemented a VR engraving art system that provides two different haptic feedback methods of haptic guidance and disturbance. As haptic guidance, the force and profile we gathered from an art expert were presented as is, while the inverse direction of the profile was given as haptic disturbance. We evaluated the user’s task performance with two methods with a baseline of no haptic feedback and the results showed haptic disturbance led to slightly better performance than guidance.

Vibrotactile feedback has been actively utilized in many virtual reality applications to provide the sense of touch. In this preliminary work, we investigated the effects of vibrotactile feedback in the dart throwing task in a virtual environment. The user study compared the task performance, as well as observed the participants’ behavior in throwing tasks with vibrotactile feedback or not. The results showed that the participants made larger body movements during the task when vibrotactile feedback was on, while the feedback did not affect the task performance.

Training on cues to deception is one of the promising ways of addressing humans’ poor performance in deception detection. However, the effect of training may be subject to the context of deception and the design of training. This study aims to investigate the effect of verbal cue training on the performance of phishing email detection by comparing different designs of training and examining the effect of topic familiarity. Based on the results of a lab experiment, we not only confirm the effect of training but also provide suggestions on how to design training to better facilitate the detection of phishing emails. In addition, our results also discover the effect of topic familiarity on phishing detection. The findings of this study have significant implications for the mitigation and intervention of online deception.

Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation.