Long-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n = 20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n = 6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances the understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.

Publisher Copyright: © 2026 Copyright held by the owner/author(s)

With the rapid development of smart wearable devices and digital health monitoring platforms, technology-assisted health management has become vital for young people. However, these devices often present single-dimensional data, limiting system-wide health insights and management. Chinese medicine pulse-taking addresses this gap. This study introduces Gourdian, a character-based home health robot that uses traditional Chinese medicine (TCM) principles, optimizing human-computer interaction through TCM diagnostics. The findings demonstrate a novel application of TCM in home health monitoring and provide a practical approach to improving young people's health awareness and engagement.

This paper presents an effective transfer learning (TL) strategy for the realization of surface electromyography (sEMG)-based gesture recognition with high generalization and low training burden. To realize the idea of taking a well-trained model as the feature extractor of the target networks, 30 hand gestures involving various states of finger joints, elbow joint and wrist joint are selected to compose the source task, and a convolutional neural network (CNN)-based source network is designed and trained as the general gesture EMG feature extraction network. Then, two types of target networks, in the forms of CNN-only and CNN+LSTM (long short-term memory) respectively, are designed with the same CNN architecture as the feature extraction network. Finally, gesture recognition experiments on three different target gesture datasets are carried out under TL and Non-TL strategies respectively. The experimental results verify the validity of the proposed TL strategy in improving hand gesture recognition accuracy and reducing training burden. For both the CNN-only and the CNN+LSTM target networks, on the three target datasets from new users, new gestures and different collection scheme, the proposed TL strategy improves the recognition accuracy by 10%∼38%, reduces the training time to tens of times, and guarantees the recognition accuracy of more than 90% when only 2 repetitions of each gesture are used to fine-tune the parameters of target networks. The proposed TL strategy has important application value for promoting the development of myoelectric control systems.