Group mood and engagement are invisible currents that shape how people collaborate at work, emerging through everyday interactions rather than isolated individual states. While positive socio-emotional dynamics support collaboration and productivity, they remain difficult to sense and interpret unobtrusively. This work investigates whether posture-based behavioral cues sensed through everyday objects can provide insight into group mood and engagement. We present a pressure-sensing chair prototype that captures changes in weight distribution during meetings. In a study with 14 groups (N = 46), we combine postural cues with participants’ retrospective video annotation to triangulate engagement and mood. Our results show that posture activity is associated with engagement and mood arousal, while moments of shared group mood co-occur with increased postural synchrony. We further identify synchronized behavioral patterns reflecting affective convergence and contagion. These findings demonstrate how situated sensing through everyday objects can reveal socio-emotional dynamics and inform the design of collaborative systems.

Publicly displaying personal tracking data can promote healthier lifestyles, with most existing research focusing on digital visualizations. However, public physicalizations have been shown to attract more attention and foster greater engagement. Despite this, few studies in this area have utilized personal physical activity data as input or explored its impact on motivating data generators’ physical activity. To address this gap, we designed PlanetWalker, a system that visualizes users’ walking steps on their phone and shows the visualization publicly through a digital display, as well as medium- and large-scale public physicalizations as probes. The probes were deployed following the Wizard of Oz method in sequence during a six-week in-the-wild study. Through evaluations with users generating data and passersby, our findings show how public physicalizations can improve motivation, facilitate social interaction, and increase engagement. We conclude by discussing the design directions for supporting the public presentation of personal data through public physicalization to motivate physical activity and provide design trade-offs and suggestions on the physicality and scale of the medium.

Access to computer-aided fabrication tools, such as 3D printing, empowers various craft techniques to democratize the creation of artifacts. To afford new blow molding techniques in the field of Human-Computer Interaction, we make efforts to simplify this challenging handy fabrication and enrich the design space of blow molding by taking advantage of the thermoformability and heat deformability of 3D printed thermoplastics. We propose a novel and democratized blow molding technique, PneuFab, enabled by FDM 3D-printed custom structures and temporal triggering methods. Then we implement and evaluate a design tool that allows users to play with parameters and preview the resulting forms until achieving their desired shapes. Showcasing design spaces including artifacts with complex geometries and tunable stiffness, we hope to expand access and dive into what more the digital blow molding fabrication can be.

Internet of Medical Things (IoMT) is regarded as an imperative technology for intelligent healthcare in the foreseeable 6G era. Due to the limited computing power of edge devices and task-related coupling, IoMT faces significant challenges. Considering the associative relationship among tasks, this paper proposes a computing offloading policy for multiple-user devices (UDs) under a multi-access edge computing (MEC) system. Specifically, we formulate the offloading scheme as a mixed-integer nonconvex optimization problem to minimize the total delay and energy consumption. To obtain the optimal solution, a double deep Q-network (Double DQN) based associative tasks computing offloading (DDATO) algorithm is then proposed, which can make the best offloading decision under the condition that tasks of UDs are associative. In addition, we use a dynamic ε−greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods, the proposed algorithm can lower the total cost more efficiently within the maximum delay and energy consumption tolerance.

In most cases, especially in remote sensing targets, the contour of the object is used to describe the features of the object, so the shape descriptor plays an indispensable role in the target detection and recognition. In recent years, more and more shape descriptors have come to the fore, but many descriptors ignore the details of the shape, such as the traditional shape descriptor of centroid and contour descriptor (CCD) and shape context descriptor (SC). The inner distance of a shape has strong robustness for describing shape features, and the inner distance shape context9(IDSC) is a good example. Therefore, this paper proposes a method of using the inner distance to find shape bones and segment the shape contour by these bones, finally performing Fourier transform to form shape feature (FD-IDBS). In matching time, we using the distance difference to perform shape matching. What is commendable is that its discriminability and robustness is strong, process is simple and matching speed is fast. More importantly, the experiment results show that the shape descriptor has higher retrieval accuracy.