Robot-supported interventions for joint attention (JA) in autistic children have shown encouraging outcomes, yet most remain confined to stationary robots in indoor settings, limiting opportunities for skill generalization and broader developmental benefits. We introduce an intervention that employs a quadruped robot dog as a peer-like partner for JA training across both indoor and outdoor environments. In this intervention, the robot dog directs children’s attention to distributed targets in the environment and initiates JA trials. A four-week pre-post exploratory study with six autistic children demonstrated improvements in JA performance and indications of transfer to daily social communication. Spontaneous behaviors such as motor imitation (crawling) and novel social interactions with the robot also emerged, suggesting potential for broader developmental gains. These findings provide initial evidence for the efficacy of mobile robot-supported JA interventions in naturalistic contexts and offer implications for future design.

Airflow is recognized as an effective method for inducing the illusion of self-motion (vection) and reducing motion sickness in virtual reality. However, the quantitative relationship between virtual motion and the airflow perceived as consistent with it has not been fully explored. To address this gap, this study conducted three experiments. In Experiment 1, we carried out a series of cross-modal matching tasks to establish the relationship between the speed of virtual motion and the airflow speed perceived as consistent with it, revealing a strong linear correlation. In Experiment 2, we introduced the concept of an "Airflow Gradient" to simulate the bodily sensation of curvilinear motion and examined the relationship between the radius and angular velocity of the motion and the difference in airflow speed between the left and right sides. The results indicated a linear relationship between the radius and the left-right airflow speed difference, while the angular velocity showed a near-quadratic pattern, similar to the centripetal acceleration formula. Based on these findings, Experiment 3 developed a dynamic airflow scheme and compared it with constant airflow and no-airflow conditions during locomotion tasks in a complex urban environment. The results demonstrated that dynamic airflow, which ensures consistency between visual and bodily vection, further reduces motion sickness, enhances presence, and provides a more natural and consistent virtual motion experience.

Children with high-functioning autism (HFA) often face challenges in emotional recognition and expression, leading to emotional distress and social difficulties. Conversational agents developed for HFA children in previous studies show limitations in children's learning effectiveness due to the conversational agents’ inability to dynamically generate personalized and contextual content. Recent advanced generative Artificial Intelligence techniques, with the capability to generate substantial diverse and high-quality texts and visual content, offer an opportunity for personalized assistance in emotional learning for HFA children. Based on the findings of our formative study, we integrated large language models and text-to-image models to develop a tool named EmoEden supporting children with HFA. Over a 22-day study involving six HFA children, it is observed that EmoEden effectively engaged children and improved their emotional recognition and expression abilities. Additionally, we identified the advantages and potential risks of applying generative AI to assist HFA children in emotional learning.

Text entry in virtual reality (VR) often faces challenges in terms of efficiency and task loads. Prior research has explored various solutions, including specialized keyboard layouts, tracked physical devices, and hands-free interaction. Yet, these efforts often fall short of replicating the efficiency of real-world text entry, or introduce additional spatial and device constraints. This study leverages the extensive capabilities of large language models (LLMs) in context perception and text prediction to enhance text entry efficiency by reducing users’ manual keystrokes. Three LLM-assisted text entry methods - Simplified Spelling, Content Prediction, and Keyword-to-Sentence Generation - are introduced, aligning with user cognition and the contextual predictability of English text at word, grammatical structure, and sentence levels. Through user experiments encompassing various text entry tasks on an Oculus-based VR prototype, these methods demonstrate a 16.4%, 49.9%, 43.7% reduction in manual keystrokes, translating to efficiency gains of 21.4%,74.0%, 76.3%, respectively. Importantly, these methods do not increase manual corrections compared to manual typing, while significantly reducing physical, mental, and temporal loads and enhancing overall usability. Long-term observations further reveal users’ strategies for using these LLM-assisted methods, showing that users’ proficiency with the methods can reinforce their positive effects on text entry efficiency.

Ground surfaces are often carefully designed and engineered with various textures to fit the functionalities of human environments and thus could contain rich context information for smart wearables. Ground surface detection could power a wide array of applications including activity recognition, mobile health, and context-aware computing, and potentially provide an additional channel of information for many existing kinesiology approaches such as gait analysis. To facilitate the detection of ground surfaces, we present LaserShoes, a texture-sensing-enabled system using laser speckle imaging that can be retrofitted to shoes. Our system captures videos of speckle patterns induced on ground surfaces and uses pre-processing to identify ideal images with clear speckle patterns collected when users’ feet are in contact with ground surfaces. We demonstrated our technique with a ResNet-18 model and achieved real-time inference. We conducted an evaluation in different conditions and demonstrated results that verified the feasibility.