Tangible narrative allow children to interact with physical objects and provides an immersive storytelling approach to influence their cognition and behavior. However, research on using tangible narratives to encourage behaviors in children remains limited. To improve children’s eating behaviors, we combined narrative transportation theory with behavior change principles to conduct a design study, called TangibleTale. Specifically, we conducted a formative user study (N = 12 pairs) to identify the characteristics of children’s engagement with tangible narrative and their interactions with parents, which were then incorporated into a design workshop (N = 12) to develop an interactive product comprising tangible elements and an accompanying app. With the produced outcomes, we conducted a comparative experiment (N = 24 pairs) in a home setting to verify and explore the role of tangible narrative in child–parent mealtime interaction. Finally, we formulated design guidelines for a tangible narrative that can serve as a reference to assist in creating more impactful products that foster positive behavioral growth in children.

Motor imagery (MI) is a critical component of Brain-Computer Interface (BCI) technology, but the effective use of MI-BCI requires significant user training. In order to improve the training effect, it is necessary to choose a stable and easy to use paradigm. To explore the effects of different paradigms on training effectiveness, 18 untrained participants were recruited to perform kinesthetic motor imagery (KMI) and visual motor imagery (VMI) experiments. The participants’ subjective vividness and mental fatigue levels were measured using a subjective scale and (θ + α)/β value in the experiments, respectively. Additionally, the Common Spatial Pattern (CSP) algorithm and the Support Vector Machine (SVM) algorithm were used to extract EEG signal features and classify them. The results showed that the majority of participants demonstrated a greater aptitude for excelling in the VMI paradigm, where the average accuracy rate was 84.7%, compared to the KMI paradigm, where the average accuracy rate was 79.6%. In addition to this, participants with higher levels of sports proficiency showed better adaptability in the KMI paradigm. Compared to KMI, VMI can enable most inexperienced participants to achieve a better user experience with less fatigue and improve training performance.

In real-world environments, interferences such as noise, lighting, and vibrations impact users’ psychological and motor imagery (MI) EEG signals. Numerous studies focus on the performance of brain-computer interface (BCI) systems in interference-free laboratory environments, leading to significantly reduced accuracy in real-world applications. This study aims to address challenges for motor-disabled individuals using BCI systems in real-world environments, particularly the issues of insufficient system robustness to interference and differences in individual EEG features. This study designed a quantitative EEG experiment with environmental noise, analyzed its impact on EEG features, and constructed an EEG feature extraction and classification model that can adaptively adjust weight coefficients according to the external sound environment. This model was applied to a brain-controlled wheelchair system, achieving over 10% higher average classification accuracy in high-noise environments compared to two classical methods, with a classification speed within 1500 ms, significantly improving the system’s noise resistance and generalization ability.

"Like poles repel, unlike poles attract" is a fundamental principle of magnetism commonly used in instantaneous haptic interaction. Through the assembly design of basic five magnets, MagneChase creates a potential barrier between repulsion and attraction. This allows MagneChase modules to change the direction of their interacting force when brought closer. Applying this interaction principle, we provide application examples of kinetic roleplay to enhance tangible play experiences. A preliminary user study suggests that children were captivated by the magnetic phenomenon and derived pleasure from engaging with MagneChase. We also discuss the potential for MagneChase as a tangible kit to promote enlightenment learning in gameplay.

4D printing encodes transformability over time, which empowers users to create artifacts by on-demand deformation. The creative process of 4D printing shape-changing artifacts can be challenging because of its discontinuous fabrication steps, such as digital designing, specific path planning, automatic printing and manual triggering. We hypothesize that switching from typical 4D printing reliant on 3D printers to a more “handcrafted” method can allow users to understand and continuously reflect upon the artifact and its transformability. Towards this vision, we introduce 4Doodle, a hybrid craft approach that integrates unique deformation controllability and five techniques for freehand 4D printing, using a 3D pen. To tackle the shape-changing challenges of uncertain hands-on fabrication, we develop a mixed reality system to help novices master the manual skills of 4D printing. We also demonstrate a series of 4D printed artifacts with fully human intervention. Finally, our user study shows that 4Doodle lowers the skill-acquisition barrier associated with handcrafting 4D printed artifacts, and it has great potential for creative production and spatial ability.

The mobile phone interface has become important access to daily information and social entertainment. And the color design of digital interfaces has a direct impact on improving the user experience. In order to explore the user's perceptual cognition of the interface color imagery, the objective relationship between EEG (Electroencephalography) signal and interface color imagery cognition is analyzed and an inference model of interface color imagery based on EEG is established. Imagery words were selected by a card sorting experiment. EEG data is generated in the users' cognition process of matching app interface color and imagery words. Experimental results show that the N400 component appears in the experiment of imagery word as the target stimulus, and the P300 component appears in the experiment of interface picture as the target stimulus. The inference model of interface color imagery is established by Extreme Learning Machine (ELM) based on the average amplitude data of N400 or P300. Results of the verification experiment indicate that the inference model can accurately predict the imagery match tendency of interface color and imagery words.