Exploring effective approaches to enhance face-to-face interactions and interpersonal relationships is an important topic in the applications of affective computing. According to the co-actualization model, we propose a face-to-face co-participation serious game for relationship enhancement, with a focus on battling COVID-19. Moreover, a prototype system is developed using an immersive virtual environment and a low-cost brain-computer interface. Through this system, a dynamic flow experience enhancement tool is utilized to involve partners in the cooperative task. To evaluate the system performance, two studies are conducted with schoolmates as participants. Study 1 compares the cooperative and competitive modes, and demonstrates that the former elicited higher level of decision-making challenge and affections, which are beneficial for forming relationships. Study 2 further examines the effect of the dynamic flow enhancement tool in the cooperative task and the results show its effectiveness in promoting flow experience, perceived closeness, and intimacy in relationships. Given this short-term participation, participants felt a greater sense of closeness and intimacy than they had before the test. In conclusion, our proposed system is effective in enhancing schoolmate relationships.

To evaluate the correlation of serum PIVKA-II levels and development of portal vein tumor thrombus (PVTT) in hepatocellular carcinoma (HCC) patients. One hundred and twenty-three patients with newly diagnosed HCC were included in this study between March 2016 and October 2018. Thirty-five of these patients were detected with PVTT and all subjects were randomly divided to analysis group (N = 73) and validation (N = 50) group. Serum levels of PIVKA-II, laboratory tests including serum aspartate aminotransferase, total bilirubin, platelet count, albumin levels were demonstrated in all the patients. T-test, chi-squared test and logistic regression was used for analyzing data. Diagnostic efficiency and cut-off value of PIVKA-II in PVTT development of HCC patients were calculated using receiver operator curve (ROC) analysis. Serum level of PIVKA-II in HCC patients with PVTT was significantly higher than that in HCC patients without PVTT (995.8 mAU/ml vs 94.87 mAU/ml; P = 0.003), as well as D-dimer levels (2.12 mg/L vs 0.56 mg/L P = 0.001). Univariate analysis showed that high serum D-dimer level was an independent risk factor for development of PVTT (OR = 1.22, 95%CI 1.02–1.45). ROC curve showed that among analysis group, the area under ROC curve (AUROC) of PIVKA-II was 0.73 (95%CI 0.59–0.86). For the detection of PVTT in HCC, PIVKA-II had a sensitivity of 83.7% and a specificity of 69.2% at a cutoff of 221.26 mAU/ml, which had a sensitivity of 85.71% and a specificity of 55.56% in validation group, respectively. Serum PIVKA-II level is a potential marker for diagnosis of PVTT in HCC patients, which may guide therapeutic strategy and assessment of tumor prognosis of HCC.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

The COVID-19 has led to home quarantine in global scale, which increased people’s need for indoor fitness activities in order to relieve stress/anxiety and learn knowledge well during the prevention. In this article, an augmented reality (AR) indoor exergame is developed, mainly a kind of moving target hitting task with Lomachenkos headwear speedball as the prototype. Then, a comparative experiment is performed to test the effect of the exergame in relieving anxiety, as well as the differences between AR and virtual reality modes (using HTC Vive Pro) on user experience (flow experience and motion sickness) and interaction efficiency. Results from 28 participants show that the anxiety level of participants significantly reduced after using the exergame. Although the difference on flow experience is not significant, there is a marginally significant difference on nausea level, a dimension of motion sickness. Moreover, interaction efficiency between the two modes is significantly different. In the task of moving target hitting, AR mode performed better in avoiding motion sickness and improving interaction performance.

When executing interdependent personal tasks for the team's purpose, simultaneous individual flow (simultaneous flow) is the antecedent condition of achieving shared team flow. Detecting simultaneous flow helps better understanding the status of team members, which is thus important for optimizing multi-user interaction systems. However, there is currently a lack exploration on objective features and methods for detecting simultaneous flow. Based on brain mechanism of flow in teamwork and previous studies on electroencephalogram (EEG)-based individual flow detection, this study aims to explore the significant EEG features related to simultaneous flow, as well as effective detection methods based on EEG signals. First, a two-player simultaneous flow task is designed, based on which we construct the first multi-EEG signal dataset of simultaneous flow. Then, we explore the potential EEG signal features that may be related to individual and simultaneous flow and validate their effectiveness in simultaneous flow detection with various machine learning models. The results show that 1) the inter-brain synchrony features are relevant to simultaneous flow due to enhancing the models' performance in detecting different types of simultaneous flow; 2) the features from the frontal lobe area seem to be given priority attention when detecting simultaneous flows; 3) Random Forests performed best in binary classification while Neural Network and Deep Neural Network3 performed best in ternary classification.

This paper describes a multisensory interface in which continuous contact sound effects of userpsilas actions on virtual objects are synthesized and rendered with haptic and visual stimuli. The auditory output is generated by convolving the force profile with the impulse response of an object, thus tightly coupled with the haptic output. Since each action, such as striking, scratching, and inhibiting, affects vibration modes distinctively, the nondeterministic nature of userpsilas real-time interactions requires more responsive approach than that for animations. We developed a technique to adjust parameters and drive corresponding modal sound models with low latency (lms). The implemented system produces realistic sound effects and smooth transitions between various actions.