Collective stress is the stress within a group or an organization. It affects individuals’ well-being and group productivity. HCI research has started exploring collective stress visualization to facilitate group awareness and collective coping via testing prototypes in controlled settings. However, an in-depth understanding of users’ needs and envisaged scenarios based on their authentic experiences are still lacking. In this study, we utilized a participatory approach called co-constructing stories to investigate how a collective stress visualization would be used in office workers’ authentic workday routines. We constructed use case stories with a group of office workers separately based on their personal lived experiences, using a design probe called AffectiveGarden. Our results categorized six clusters of benefits for collective coping through visualization and their implications for future design practice.

This paper presents TexonMask, a facial expression recognition system using lightweight electrode-augmented commodity facemasks. With a matrix of textile electrodes carefully deployed on a commodity mask, our edge computing system recognizes the wearer’s facial expressions with machine learning based on the capacitive sensor readings, provides a wearable affective display and communicates with external devices using low bandwidth. Results from user studies show that the system is effective and efficient at recognizing five or ten facial expressions with an accuracy of around , using a personalized classifier trained with only six data points per expression. The system’s performance is stable across the use sessions and further improves when more data points are collected. We further developed two LiveEmoji applications for facilitating online and face-to-face communication of facemask wearers, demonstrated them in user interviews, and obtained positive participant feedback. Based on the results and findings of the study, we discuss implications and future research directions for facilitating emotional communication between facemask wearers and others.

This paper presents NFCSense, a data-defined rich-ID motion sensing technique for fluent tangible interaction design by using commodity near-field communication (NFC) tags and a single NFC tag reader. An NFC reader can reliably recognize the presence of an NFC tag at a high read rate (∼ 300 reads/s) with low latency, but such high-speed reading has rarely been exploited because the reader may not effectively resolve collisions of multiple tags. Therefore, its human–computer interface applications have been typically limited to a discrete, hands-on interaction style using one tag at a time. In this work, we realized fluent, hands-off, and multi-tag tangible interactions by leveraging gravity and anti-collision physical constraints, which support effortless user input and maximize throughput. Furthermore, our system provides hot-swappable interactivity that enables smooth transitions throughout extended use. Based on the design parameters explored through a series of studies, we present a design space with proof-of-concept implementations in various applications.

Co-dining benefits in closeness and connectedness between all family members. However, it is hard for those in different locations to enjoy a meal together due to the geographical barriers and inconsistent life schedule. The widely applied video technology caused excessive visual interference when solving problems, while the effect of sound in a meal on intimacy has not been studied. In this paper, we explored how can recorded audio create an asynchronous co-dining experience and support intimacy. We conducted a field study with 24 participants, and the result shows the system potentially enhance the intimacy in remote coding experience. In the follow-up workshop with 6 participants, we further discussed the future landscape of designing a sound interface for remote meals.