Embodied metaphors, grounded in sensorimotor experience, can enrich tangible interaction design by linking abstract functions to familiar bodily actions and perceptions, making them more intuitive and meaningful. However, our formative study (N=10) revealed that designers—especially novices—struggle to identify appropriate embodied metaphors, move beyond superficial analogies, and translate them into tangible designs. To address these challenges, we developed MetaEmbody, an AI-assisted creative support system that helps designers explore contextual analogies, derive embodied metaphors, and shape them into tangible interaction concepts. A user study (N=20) demonstrated that MetaEmbody effectively stimulated embodied thinking and enhanced the metaphorical embodiment of design outcomes. It also yielded higher ratings in novelty, feasibility, and overall human–AI collaboration experience, with notable benefits for novice designers. We explore the potential of how human–AI collaboration can foster embodied design thinking, advancing generative AI beyond visual metaphor blending to support deeper exploration of interaction and experiential meaning.

Multimodal Sentiment Analysis (MSA), a key area in affective computing, involves developing models to process multimodal inputs and predict sentiment values. However, existing MSA methods often struggle with performance degradation due to random modality feature loss, which is a common occurrence in real-world scenarios. This paper introduces ATD-AMSMamba, a novel Mamba-based architecture for MSA. It integrates two key components—Adversarial Token Dropout (ATD) and Adaptive Multimodal Scanning Mamba (AMSM)—to enhance robustness through token transformation and state-space transition. Specifically, ATD mitigates over-dependence on specific uni-modal tokens during training, thereby improving robustness to missing modality scenarios. Furthermore, we introduce AMSM, which employs adaptive state weighting to ensure robust intra-propagation of multimodal state transitions, effectively mitigating corrupted transitions caused by missing modalities. This paper explores the potential of Mamba-based models for enhancing robustness in multimodal sentiment analysis. Experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS validate that ATD-AMSMamba significantly outperforms previous state-of-the-art methods.

This study aimed to detect stressful hazard perception scenarios subjectively and objectively when using intelligent driving systems. We used electrooculography (EOG), electroencephalography (EEG), subjective ratings, and interviews to identify potential stressful hazard perceptions and record improvements in an intelligent navigation-guided pilot (NGP) system. Moreover, we analyzed electrophysiological data. Our study contributes to the use of engagement, concentration, and phase locking value connectivity based on EEG to support previous research methodologies using beta power, pupil size, fixation ratio, fixation duration, and subjective evaluations for investigating hazard perception. Our analyses showed that stressful hazard perception scenarios occurred mainly when encountering broken and solid lines, frequent lane changes, cars approaching suddenly, several cars driving in parallel, and the decision to change lanes but immediately pulling back upon using the NPG system. Our findings shed light on obtaining accurate results based on subjective and objective evaluations for developing intelligent driving systems.

Abstract With the expansion of extended reality (XR), virtual agents engage more with humans across fields. While the influence of anthropomorphic traits on user perceptions is studied, the combined effects of voice traits and gestures need further investigation. We explored the impact of a virtual agent's voice personality and gestures on subjective perception and visual behaviors in a hospital guidance setting. Using a 2 × 2 within-subjects design, we assessed the influence of voice traits (calm vs. lively) and gestures (with vs. without) through subjective reports and eye tracking. Results show that the impact of gestures on content comprehension ease varied depending on the voice personality traits. Specifically, a lively voice with gestures significantly increased comprehension while a calm voice with gestures did not. Additionally, a calm voice led to longer average fixation durations on body-related areas of interest (AOIs), shorter time to first fixation, and fewer fixation counts on dialog content-related AOIs. Gestures decreased fixation counts on face-related AOIs. For some eye metrics, the impact of gestures varied depending on voice personality traits, whereas for others, the influence of voice traits depended on the presence of gestures. Thus, voice traits and gestures influence user perceptions and visual attention differently.

Virtual reality (VR), as an immersive interactive technology, holds the potential to promote feelings of well-being by evoking positive emotions. However, the underlying causes and extent of emotional responses elicited by VR remain underexplored. Accordingly, we aimed to investigate the types of interaction behaviors in VR that effectively enhance positive emotions, using electroencephalogram (EEG) signals as measurements of emotional expressions. In an exploratory study conducted on a virtual museum $(N =22)$, we designed four interactive tasks with varying user autonomy and interaction functions. An individual emotion model based on EEG was employed to predict the promotion of positive emotions and its extent. The results indicated that simply roaming the virtual museum had no obvious impact on positive emotions. However, incorporating specific interaction functions such as doodles, emojis, and comments increased positive emotions, with the extent of the increase closely linked to the degree of user autonomy.

Brain-computer interfaces (BCI) are widely used in the field of disability assistance and rehabilitation, and virtual reality (VR) is increasingly used for visual guidance of BCI-MI (motor imagery). Therefore, how to improve the quality of electroencephalogram (EEG) signals for MI in VR has emerged as a critical issue. People can perform MI more easily when they visualize the hand used for visual guidance as their own, and the Rubber Hand Illusion (RHI) can increase people's ownership of the prosthetic hand. We proposed to induce RHI in VR to enhance participants' MI ability and designed five methods of inducing RHI, namely active movement, haptic stimulation, passive movement, active movement mixed with haptic stimulation, and passive movement mixed with haptic stimulation, respectively. We constructed a first-person training scenario to train participants' MI ability through the five induction methods. The experimental results showed that through the training, the participants' feeling of ownership of the virtual hand in VR was enhanced, and the MI ability was improved. Among them, the method of mixing active movement and tactile stimulation proved to have a good effect on enhancing MI. Finally, we developed a BCI system in VR utilizing the above training method, and the performance of the participants improved after the training. This also suggests that our proposed method is promising for future application in BCI rehabilitation systems.

Nowadays, social robots have become human's important companions. The anthropomorphic features of robots, which are important in building natural user experience and trustable human-robot partnership, have attracted increasing attention. Among these features, eyes attract most audience's attention and are particularly important. This study aims to investigate the influence of robot eye design on users’ trustworthiness perception. Specifically, a simulation robot model was developed. Three sets of experiments involving sixty-six participants were conducted to investigate the effects of (i) visual complexity of eye design, (ii) blink rate, and (iii) gaze aversion of social robots on users’ perceived trustworthiness. Results indicate that high visual complexity and gaze aversion lead to higher perceived trustworthiness and reveal a positive correlation between the perceived anthropomorphic effect of eye design and users’ perceived trust, while a non-significant effect of blink rate has been found. Preliminary suggestions are provided for the design of social robots in future works.