Establishing an objective and quantifiable analysis of the behaviors of children with autism remains a major challenge in current research. Behavioral analysis is essential for both early autism screening and designing effective intervention strategies. Drawing on theories from psychology and behavioral science, we propose an explainable interactive digital behavioral analysis method grounded in spatial correlation. Using a series of context-based behavioral stimulation paradigms, we collected multi-modal reactive behaviors from 112 children. We then compared the response patterns of children with autism to their clinically observed symptoms to evaluate the developmental status of the core competencies underlying their behaviors. By quantifying children's multi-modal responses through a spatial correlation behavioral paradigm analysis method, we generated digital behavioral representations and applied them to autism screening, achieving an accuracy of 94.4 %. These results validate the effectiveness and reliability of our digital behavioral analysis approach, demonstrating its potential value in supporting objective autism screening and informing subsequent intervention planning.

Recently, the electroencephalogram (EEG) has been widely adopted as a quantitative indicator for monitoring emotional modulation during virtual reality (VR) experiences. Although VR emotion-induction materials are continuously being developed, few methods have been proposed for constructing VR scenarios through psychophysiological calibration. To achieve precise emotional regulation, we propose an integrated psychophysiological oriented EEG-based VR scenario modeling approach for emotion induction. This methodology constructs 3D VR scenarios by extending 2D elements through core processes: (1) deconstruction of emotion-annotated 2D images to extract visual-audio emotional patterns, (2) development of immersive environments with dynamic camera trajectories, and (3) integration of audio stimuli and real-world physical configurations to form multi-sensory emotional stimuli. During emotioninduction experiments, EEG signals reflecting emotional states were captured and analyzed for each scenario. We implemented multidimensional calibration of emotion-induction materials by: (a) calculating emotion indicators from EEG signals, (b) combining these with traditional assessments using the Self-Assessment Manikin (SAM) scale, and (c) calibrating scenarios for distinct emotion categories. This yields psychophysiologically calibrated VR scenarios for emotion induction, delivering standardised affective stimuli with synchronised EEG datasets to advance affective computing research.

Using mobile phones for photography and videography has become increasingly prevalent. However, novice users often struggle with mastering professional photography techniques and lack the necessary skills for precise camera movement. To address this issue, we propose ARVideoCam, a video shooting guidance method based on augmented reality (AR) designed to assist novice users in accurately controlling camera movements and learning photography skills through imitating high-quality video shooting. ARVideoCam consists of three layers: the extraction layer, the analysis layer, and the AR layer. The extraction layer of the model captures the subject's motion in the sample video, and the analysis layer converts the subject's motion into camera motion, which is then transformed into AR-based guidance in the AR layer. The user study results indicate that AR guidance significantly enhances novice users' ability to imitate sample videos across different types of camera motion, resulting in more efficient and precise camera movements during shooting. This study offers valuable inspiration for the research on AR-assisted video shooting and enhancing video shooting skills among novice users.

Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism for VR mindfulness meditation aimed at optimizing the effects of emotion regulation. This mechanism consists of three modules: 1) EEG-based emotional state computation; 2) process-based relaxation assessment; and 3) adaptive positive decision feedback. Collectively, these components form a computation-assessment-feedback closed-loop system that objectively quantifies emotions while enabling real-time decision adjustments based on emotional trends, thereby enhancing user engagement and emotion regulation efficacy through personalized feedback. The contribution of the proposed feedback mechanism was evaluated through a randomized controlled trial (N= 36). The results indicated that both physiological measures and self-reported relaxation significantly increased when compared to interventions without feedback. These findings validate that the EEG-based positive feedback mechanism effectively enhances emotion regulation while providing additional insights into improving both the engagement and effectiveness within digital mental health interventions.

Computerized Cognitive Behavioral Therapy (CCBT) has been widely proven effective in alleviating negative emotions such as depression and anxiety. To address the lack of real-time feedback, we explored how to add appropriate interactions from the user experience perspective. By analyzing user context, we propose a transformation framework from CBT to interactive CCBT (iCCBT), which makes online interventions more effective. According to the framework, we designed and implemented an iCCBT platform for young adults. Through a 9-day controlled experiment on 28 college students, we conclude that interaction can effectively improve users’ knowledge retention, learning efficiency, satisfaction, and willingness to use. This study demonstrates methods and results for enhancing the interactivity of CCBT. The design thinking of the transformation framework can be extended to other online psychotherapy.

Autistic children have developmental disorders including social and communication deficits, attention problems, and so on. Due to these deficits, autistic children are difficult to establish social connection with humans and learn skills from social interactions, especially in group learning environment. To support autistic children in group learning, a motivating interaction environment is important. On the other hand, robot-mediated interventions have shown to be effective and more engaging. In this sense, social robots with multiple interaction modalities, might have the potential to support autistic children’s group learning. In this paper, we present the design of a robot-mediated group picture book reading activity, with the goal to contribute to autistic children’s better engagement, picture book learning, and group interaction behaviors. We evaluate and discuss the initial experimental results.

Human-Computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.

Abstract Virtual pet applications have been widely developed and applied in various fields. Mobile augmented reality (MAR) provides a new medium for virtual pets, allowing users to have a more immersive interactive experience through MAR pets. However, the issue of the user experience in MAR pets remains uninvestigated and relatively unexploited. Therefore, this article proposes a context‐aware MAR pet interaction model (CAPet model) to enhance the user experience in MAR pet systems, which allows the MAR pet makes feedback adaptively corresponding to the dynamic context. In addition, this article presents a user experience pyramid to measure the user experience for MAR pet. According to the proposed CAPet model, a MAR pet‐dog application is designed and implemented, based on which user test are conducted. The results of user test indicate the effectiveness of the proposed method on enhancing user experience, which provides a basis for the design of MAR pet applications in the future.

Abstract In recent years, mindfulness meditation has become increasingly popular as a way to relieve negative emotions. Although many studies have shown the advantages of the most immersive virtual reality (VR) technologies to support mindfulness meditation, few have summarized a standardized process for developing a VR tool to aid mindfulness meditation. We propose an interaction design model for VR mindfulness meditation using imagery‐based transformation and positive feedback to help users quickly soothe their negative emotions. Based on the traditional mindfulness meditation model, we propose the three detailed transformational steps to build (1) an interaction guidance process, (2) an interaction experience context, and (3) an interaction feedback mechanism. We build two scenarios based on them. Our experimental results suggest that our design can effectively alleviate a user's anxiety and provide emotional relief. Our insights on the transformation model of VR mindfulness meditation can be applied in related research, providing an effective reference model for VR mindfulness meditation systems, while providing new ideas for non‐pharmacological interventions in psychotherapy.

Dimensional emotion recognition research is an important branch of affective computing, which uses continuous values to represent complex human emotions. In this study, we propose a visual–audio emotion recognition system that integrated emotional dimensions. For the visual part, the corresponding relationship between emotion category and emotion dimension interval is established based on rules, and the respective classifiers are trained and fused using the machine learning methods. For the audio part, some emotion-related features are extracted, and a 128-D global feature is extracted through a deep convolutional neural network (DCNN). We use a combination of Bayesian and machine learning to integrate the information of visual–audio modalities. We have tested the proposed system and its single modalities on the standard databases CK+ and eNTERFACE ’05, and the experimental results and comparison showed the efficiency of the proposed system. Furthermore, our proposed system uses emotion category label and dimension values simultaneously to represent emotion, providing strong interpretability and expansibility for emotion recognition, which goes beyond the methods only come with either classification or dimension.

Immersion is a powerful and important interactive experience. However, little is known about how we can facilitate immersion in Mobile Augmented Reality (MAR) applications. Establishing the relationship between the virtual and the real is considered a promising way to promote immersion. To enhance immersion in MAR, we present BRIDGE, an interaction design model which builds a bridge between virtual and reality through the following three kinds of relationships: The virtual object has a close relationship with the real environment where the user is (contextual relationship) ; the virtual object has the same physical properties as the real world (physical relationship) ; the user imitates real-world interactions by directly interacting with the virtual world with their hands (interactive relationship). To evaluate the effect of the BRIDGE model, we implement it into the application design and conduct a comparative study of 32 users, and explore the immersive user experience of contextual and non-contextual, physical and non-physical, natural-interaction and screen-touch. The quantitative and qualitative results show that virtual objects have a stronger presence and users are more immersed in the environment when there is a contextual and physical relationship and users can interact naturally. This study is the first step to having a better understanding of the characteristics that contribute to an immersive experience and how they affect human perception and the presence of virtual objects. We hope to provide design insights for MAR applications based on these results.

Abstract With a core purpose of helping users to understand the context, a water interface provides possibility for enhancing user experience in interaction process. Starting from analyzing existing water‐mediated interaction approaches, we proposed a water‐mediated interaction design model and a corresponding user experience model, aiming to eliminate the boundary between users and the context with water as the medium. According to the proposed model, we implemented a water‐mediated interaction system Landscape Rippling, with the painting “A Panorama of Rivers and Mountains” as its context. Ultimately, user experience tests of the interaction system demonstrate the effectiveness of this water‐mediated interaction design model.

Abstract Social robots have great potential for the therapy of children with autism spectrum disorder, but the practical use of them is challenging. In this article, we presented a social robot YANG, which can interact with children with autism in their daily training. We propose a therapist‐robot interactive (TRI) model, which integrates with the practice of discrete trial training (DTT), a basic method utilized in autism training. To evaluate the TRI model, we implemented the model in YANG and conducted a single‐subject experiment in a rehabilitation training center. Data were collected on three children (ages 3–4) and their three therapists as they interacted with YANG in training sessions. Results showed that the children's learning ability significantly improved. YANG formed natural and friendly relationships with the children and delivered substantial support to therapists. Our research brings insight into using social robots for children with autism.

Using mobile phones to shoot video is considerably common in our daily life. However, novice users have difficulty in controlling the camera properly due to lack of professional knowledge and skill. In this paper, in order to assist novice users in learning and imitating professional camera movement from watching high quality sample videos, we propose ARCAM, an Augmented Reality (AR) based video shooting guidance method for novice users. Using AR, we visualized the concept of camera movement and embedded it into natural scene to provide real-time guidance. User can follow the guidance while shooting video by matching a calibration frame to the guidance, to achieve the desired camera movement. We conducted a user study comparing the effectiveness of ARCAM to a traditional static arrow guidance. Results showed that ARCAM was more effective in helping users understand the camera work in the sample videos and move the camera with more accuracy. Our work provides insights on designing mobile video shooting application and suggests that AR has great potential in assisting novice video shooters.

The BabeBay is a children companion robot which has the ability of real-time multimodal affective computing. Accurate and effective affective fusion computing makes BabeBay own adaptability and capability during interaction according to different children in different emotion. Furthermore, the corresponding cognitive computing and robots behavior can be enhanced to personalized companionship.

The human eye's state of motion and content of interest can express people's cognitive status and emotional status based on their situation. When observing the surrounding things, the human eyes make different eye movements according to the observed objects which reflects human's attention and interest. In this paper, we capture and analyze patterns of human eye-gaze behavior and head motion and classify them into different categories. Besides, we compute and train the eye-object movement attention model and eye-object feature preference model based on different peoples' eye-gaze behaviors by using machine learning algorithms. These models are used to predict humans' object of interest and the interaction intention according to people's real-time situation. Furthermore, the eye-gaze behavior and head motion patterns can be used as a modality of non-verbal information in the computing of human emotional states based on the PAD affective computing model. Our methodology analyzes human emotion and cognition status from the aspect of eye-gaze behavior and head motion, understands the cognitive information that human eyes can express, and effectively improves the efficiency of human-computer interaction in different circumstances.

Abstract Researchers have proposed various techniques for cloth simulation in the last decades. The crucial problem in interactive animation for cloth is how to speed up simulation with a stable system. In this paper, we describe a realistic and stable scheme for cloth simulation based on mass‐spring system. This scheme modifies semi‐implicit integration used in cloth simulation system with an efficient damping method. Semi‐implicit integration methods have been widely used in dynamic simulation because of acceptable speed and stability. However, internal damping forces are generated with respect to rotational rigid motions, which are only depending on relative velocities. Undesirable change in global movement of dynamic cloth could result in damping artifact. We replace the internal damping forces with an optimal damping method which is based on iterative spring damping but the limit can be computed directly. The method provides simple damping parameter estimation and guarantees conservation of global movement. As a result, complex clothes can be realistically and stably simulated in real time. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, the weighted average prediction (WAP) is introduced into the existing consensus protocol for simultaneously improving the robustness to communication delay and the convergence speed of achieving the consensus. The frequency-domain analysis and algebra graph theory are employed to derive the necessary and sufficient condition guaranteeing the second-order delayed multi-agent systems applying the WAP-based consensus protocol to achieve the stationary consensus. It is proved that introducing the WAP with the proper length into the existing consensus protocol can improve the robustness against communication delay. Also, we prove that for two kinds of second-order delayed multi-agent systems: 1) the IR-ones with communication delay approaching zero and 2) the ones with communication delay approaching the maximum delay, introducing the WAP with the proper length into the existing consensus protocol can accelerate the convergence speed of achieving the stationary consensus.

The design of NPC (non-player character) is an analytic process. It is relying on assumptions of human game players' behavior. In practice, however, different PCs (player characters) often exhibit variable behavior, making them difficult to predicate and complicating the design process. In this paper, we describe an approach for team AI planning and learning. This approach is based on procedural knowledge and a layered multi-agent architecture. We implement real-time transfer learning and adaptive mechanism for the team of NPCs. The team can react to the human player with the tactical awareness of seasoned team behavior. Results indicate that the approach of using the hybrid of transfer learning and adaptive mechanism can improve NPCs' overall performance in real-time.