Musical emotion modulation is central to mental well-being, yet existing affective haptic systems often prioritize technical feasibility over investigation of where and what optimal stimuli should be applied. This paper systematically examines the emotional effects of vibrotactile, thermotactile, and combined stimuli based on the Valence–Arousal model across the wrist, neck, and ear under two music conditions, indexed by EEG measures and subjective ratings. We found that both body site and stimulus type significantly influenced emotional responses. The ear strongly enhanced arousal, the neck produced context-dependent effects, and the wrist primarily modulated pleasantness. Vibration primarily boosted arousal, thermal cues enhanced valence, and their combination enabled a more balanced, immersive experience. Our findings provide scientific guidance for future affective wearable design in various contexts.

With the development of children’s education, parents and teachers pay more and more attention to the learning and training of children with developmental dyslexia (DD). There are many reasons for DD and various intervention tools with a large number of learning costs. In this paper, we aim to analyze the relationship between music, pinyin, and reading, and confirm the theoretical link of "music - Chinese pinyin - reading ability". Then, we sort out a method to design the music intervention tool in this article, which is a new tangible interactive tool for children with DD. This tool is composed of hardware and software and has three modules: music intervention, pinyin intervention, and reading intervention, covering a total of 11 levels and is suitable for classroom teaching, home self-study, and other scenarios, to improve the Chinese pinyin and reading abilities. Subsequently, we conducted a pre-experiment and formal experiment on 20 children with DD to demonstrate the effectiveness of this tool, while using the SUS scale to analyze the evaluation of these children. Finally, we figured that the intervention tool designed in this study has a certain helpful effect on improving the phonetic and reading abilities of children with DD, and is more effective than the three intervention methods used alone. This study has certain reference significance for the designers of intervention tools and also provides new intervention methods for teachers in training institutions. It also provides another possibility for the self-treatment and training of children with developmental dyslexia.

Dementia caregivers widely turn to social media for much needed information and support. Prior research on caregivers’ online information exchange has focused on the original questions or posts, without including peer comments in response to those original questions, which does not provide a complete picture of online information exchanges between caregivers and their peers. This paper provides a preliminary analysis of a subset of data from a larger project and suggests how peer comments might match with evidence-based care strategies for memory loss and confusion. A total of 954 peer comments on 114 Reddit posts for dementia memory loss and confusion were collected and mapped with 5 evidence-based strategies generated from 17 care strategies from 3 credible websites. Our results report how well peer comments on Reddit can map to existing evidence-based strategies, and provide preliminary evidence supporting the necessity of providing tailored information based on the patient’s characteristics, stages of disease, and the progression level of memory loss and confusion.

User independent emotion recognition with large scale physiological signals is a tough problem. There exist many advanced methods but they are conducted under relatively small datasets with dozens of subjects. Here, we propose Res-SIN, a novel end-to-end framework using Electrodermal Activity(EDA) signal images to classify human emotion. We first apply convex optimization-based EDA (cvxEDA) to decompose signals and mine the static and dynamic emotion changes. Then, we transform decomposed signals to images so that they can be effectively processed by CNN frameworks. The Res-SIN combines individual emotion features and external emotion benchmarks to accelerate convergence. We evaluate our approach on the PMEmo dataset, the currently largest emotional dataset containing music and EDA signals. To the best of author's knowledge, our method is the first attempt to classify large scale subject-independent emotion with 7962 pieces of EDA signals from 457 subjects. Experimental results demonstrate the reliability of our model and the binary classification accuracy of 73.65% and 73.43% on arousal and valence dimension can be used as a baseline.

Microsimulation models are normally used to evaluate traffic-adaptive signal control systems. This paper develops an analytical approach for this evaluation based on queuing models. In particular, a queuing model is developed for a simplified adaptive control strategy that is based on rolling horizon scheme, in which a signal serves two movements alternatively. In this strategy, the first movement is served until the queue dissipates, then the second movement is served until the queue dissipates, then the signal goes back to serving the first movement, and this cyclic process repeats. A numerical algorithm is developed in a stochastic context to compute steady-state performance measures such as average delays and expected queue lengths. These results are compared with simulation-based results, and indeed, the analytically derived numerical method predicts well the simulation results