We present Pluskin, a machine-embroidered plush skin that transforms the surface of a companion robot dog into a soft tactile sensing interface. Fabricated via digital loop-pile embroidery, Pluskin provides a scalable sensing solution that integrates the softness and familiarity of plush materials with textile-based sensing. We implemented a 4 × 3 sensing array on the dorsal surface of a Petoi Bittle robotic dog to validate the system. Using a Support Vector Machine (SVM) classifier on extracted spatiotemporal voltage features, the system identifies five natural touch gestures and maps them to corresponding embodied robot behaviors. By integrating plush materiality, tactile sensing, and embodied robot feedback, this work demonstrates a feasible approach toward a more natural and affective human-robot interaction interface.

Multisensory stimulation promises in improving older adults’ affective experiences, yet its effectiveness depends on seamless affective congruency across sensory cues. This study investigated how visual, auditory, and kinetics correspondence and congruency shape affective experiences through two experiments. Experiment I examined timbre–color associations, showing that affective alignment strengthens perceived correspondence. Experiment II explored auditory–kinetics synchrony in a cross-modal art system, revealing no significant differences across conditions but indicating that older adults with lower cognitive abilities reported higher pleasure than higher-ability peers. Building on these results, an artificial intelligence (Al)-infused mode was integrated to transform strokes into real-time ink-style artworks, reducing cognitive effort, sustaining engagement. Findings demonstrate that AI enhances positive affect (pleasure, surprise, valence, and arousal) and mitigates negative affect (sadness, anger), with effects maximized by high sensory synchrony, providing compensatory support for users with lower cognitive abilities. These findings inform multisensory system design for older adults’ cognitive and affective needs.

Frequency Modulated Continuous Wave (FMCW)-based mmWave radar has attracted widespread attention because of its non-contact and high spatial resolution for vital signs monitoring. Meanwhile, current studies focus mainly on how to improve the detection performance of steady multiple objects or unsteady single objects. In this work, we propose an innovative method for identity-based multi-object vital signs monitoring under unsteady scenarios. The method automatically distinguishes between steady and motion states, and conducts a best-effort vital signs monitoring during unsteady scenarios. To this end, we design a weight vector enhancement method combined with object spatial positioning for differentiating multiple objects, and identify each object according to the gait-based EfficientNet model. We also design a steady-state detector based on the MobileNet-V2 network to find the slots of object keeping steady for vital signs monitoring and then apply the variational mode decomposition (VMD) algorithm to extract the respiratory and heart rates of a single object. The experimental results showed that the mean absolute error of respiratory rate and heart rate decreased to 1.37 bpm and 2.56 bpm respectively in the case of multiple objects. In addition, the steady-state detector achieves close to 98.1% accuracy in recognizing motion types, and the average recognition rate of identity recognition based on gait features reaches about 93.26%.

Frequency Modulated Continuous Wave (FMCW)-based mmWave radar has attracted widespread attention due to its non-contact and high spatial resolution for multi-target vital signs monitoring. However, current works mostly focus on how to improve detection performance under the steady scenarios with a single target, while unsteady scenarios and physical mutual interference from multiple targets are rarely considered. In this work, we propose an innovative method for multi-target vital signs for unsteady scenarios, such as aerobic exercise. The method automatically distinguishes between steady state and motion state, and completes a best-effort vital signs detection during unsteady state. To differentiate multiple targets, we design a weight vector enhancement method combined with target space localization, and then, apply the variational mode decomposition (VMD) algorithm to extract the respiratory and heart rates of a single target. Moreover, for evaluating the efforts of exercise, we propose Multi-target Motion Recognition (MMR) based on MobileNet-V2 network to recognize the motion states of multiple targets. The experimental results showed that the mean absolute error of respiratory rate and heart rate decreased to 1.37 bpm and 2.56 bpm, respectively. Meanwhile, the MMR algorithm achieves close to 98.1% accuracy in recognizing motion states.

Anomaly detection in attributed graph data has been widely applied in real applications. However, the intricate topology of graph data, high-dimensional attributes, and class imbalance inherent in anomaly detection tasks render attributed graph anomaly detection a challenging task. To detect anomalies using the intricate topology information of graph data, a dual-masked autoencoders is proposed for attributed graph anomaly detection, denoted as MAGAD. Specifically, in the MAGAD, the class imbalance in attributed graph data is dealt with by randomly masking the original graph data to obtain masked graph data for the anomaly detection task. And then, a latent representation of the graph data is obtained by training dual autoencoders, where one autoencoder is developed for reconstructing the original graph data, and another for reconstructing randomly masked graph data. This assists in identifying abnormal nodes in the attributed graph data. Subsequently, to capture anomalous information from relevant features, MAGAD uses a random re-masking strategy for latent representations learned from the masked graph. Finally, the anomaly scores of the nodes are calculated using the learned latent representations from the decoders of the dual autoencoders. Experimental results on five real-world datasets demonstrate that the MAGAD algorithm outperforms state-of-the-art anomaly detection algorithms.

Healthcare personnel suffer from an increased risk of stress, burnout, and depression due to the challenges of the COVID-19 pandemic. Studies show that interactive smart textiles help people alleviate their emotions. In this research, we investigate how to utilise interactive textiles to help healthcare workers mitigate their negative feelings. We have designed a smart t-shirt that encourages its wearers to perform body movements to enhance their positive emotions, stimulated by vibrotactile and audio feedback mechanisms. We demonstrate our smart t-shirt’s utility by asking healthcare workers (including physicians and nurses) to use it for five consecutive days. Our prototype design supports using it anywhere, including work, home, and other places. We evaluated our smart t-shirt prototype for emotion regulations at work for healthcare workers through an in-situ user study conducted at three hospitals. Results show that using the smart t-shirt positively impacts the healthcare workers’ immediate emotion regulation when they experienced emotion fluctuation and provided a more positive attitude towards their work. We conclude by analysing the potential factors that influence emotions and outline the design space of e-textiles for emotion regulation in real-life use.

Textile interfaces are of interest to ubiquitous computing as they are easy to carry and manipulate. However, interesting questions remain about what type of natural gestures people make when interacting with textile interfaces and their emotional response to this interaction. We introduce GesFabri, a set of five interactive textile interfaces with distinct textures, created to investigate the intuitive interaction gestures and accompanied the emotional experience. This research sought to (1) design textile interfaces with intuitive gesture affordance, (2) explore the emotional effects of the developed gesture-based interfaces under four feedback modes (touch-only, visual feedback, audio feedback, multisensory feedback). The experimental results verify our hypotheses that (1) textile texture could provide natural gesture affordances; (2) the GesFabri interfaces' feedback mode was the main factor in the differences of emotional valence, arousal, GSR; and (3) both gesture-based interaction on textiles and the feedback mode had an impact on user emotions. These results highlight the gesture affordances of the e-textile interfaces and contribute to a better understanding of the user experience when interacting with gesture-based textile interfaces.