Wearable sensing systems increasingly depend on textiles that are both materially wearable and electronically functional. Their design requires collaboration between textile designers, who reason through stitches, yarn behavior, and machine constraints, and interaction designers, who reason through electrodes, signal paths, and insulation. However, these forms of expertise do not easily translate across disciplinary boundaries. This poster presents CapSenseBand, a knitted capacitive-sensing wristband developed through a research-through-design process organized around Analysis, Synthesis, and Detailing. We document an artifact chain spanning material swatches, a rapid wearable prototype, Paper Models as shared negotiation surfaces, a double-layer knitted structure, and an insulated Swept Frequency Capacitive Sensing breakout board. We show how Paper Models functioned as boundary objects, helping collaborators externalize intent, negotiate spatial and technical constraints, and preserve disciplinary expertise while converging on a shared design. We contribute a reusable swatch-to-sleeve pattern for material-centered HCI: keep discipline-specific probes open early, then converge through artifacts that make material, spatial, and electronic decisions legible before fabrication locks them in.

Senior architects are pivotal in shaping architectural projects, yet integrating Generative AI (GenAI) into their workflows presents notable challenges. A formative study (N=11) identified key pain points in their early concept design process. To address these, we developed EarlyArchi, a GenAI-driven system supporting automated concept generation and evaluation. In a within-subject study (N=13), participants used EarlyArchi for early-stage design tasks. Results showed enhanced perceived creativity, improved design competency, and more efficient ideation. However, concerns emerged regarding controllability and domain-specific accuracy, highlighting the need for features that preserve professional autonomy and trust. Further analysis revealed three GenAI involvement modes—fully AI-driven, GenAI-led, and human-led—emphasizing the importance of adaptive role allocation in balancing creative exploration with expert leadership. These findings offer insights into supporting senior architects through GenAI while identifying key considerations for designing future human–AI co-creation systems.

In architecture design, while Generative AI can effortlessly create initial prototypes, architects struggle to update designs to stakeholders’ evolving requirements. Through a formative study (N = 12), we identified specific obstacles that architecture designers face when meeting the dynamic design demands of various project stakeholders. We therefore developed ArchiConnect, a proof-of-concept interactive system that helps architects communicate with multiple stakeholders and update final design deliverables. ArchiConnect supports creativity and engagement by visualizing evolving demands, conflicts, and concept extractions from diverse stakeholders. We evaluated our system in a week-long user study (N = 8) with a simulated project. Participants found ArchiConnect effective for improving multi-stakeholder communication and management, describing it as intuitive and useful. Our findings offer design considerations for future AI tools to better handle dynamic stakeholder needs, including how to address sustainability requirements in line with development goals.

The rapid increase in the number of individuals with Autism Spectrum Disorder (ASD) has drawn extensive attention from both the general public and researchers. Artificial Intelligence (AI) has been applied in the assessment, early diagnosis, and intervention of ASD to enhance the efficiency of clinicians and reduce tension in medical resources. However, the adoption of AI systems in clinical practice is relatively limited due to the challenge of complexity and diversity of ASD. Thus, involving insights into clinicians' perceptions and barriers toward the role of AI is crucial for enhancing clinicians-AI cooperation for autism. Through conducting the semi-structured interview with 18 physicians across tertiary and secondary hospitals in various regions, this study indicates the positive attitude toward collaborating with AI among physicians. Additionally, some concerns are also reported, such as the complexity of ASD, uncertainty of AI capabilities, and understandability of AI. The findings of this study highlight the significance of human-centered AI in satisfying different stakeholders' needs and discuss the potential implications of AI capabilities for adopting AI in future autism research.

Human-computer interaction has seen growing interest in emotion detection. To gain deeper insights into the physiological indicators of emotions, researchers have delved into utilizing electroencephalography (EEG) and micro-gestures (MGs). This study assesses the efficacy of EEG and MG features in emotion detection by recruiting 15 participants to gather EEG and MG data in response to diverse figure-based emotional stimuli. To incorporate these features, this article introduces Emo-MG, a multimodal interface that integrates EEG and MG features and employs a long short-term memory (LSTM) model to predict emotional states within the valence-arousal-dominance (VAD) space. This study presents an in-depth analysis of feature importance and correlation results based on EEG and MG features for feature selection in emotion detection tasks. Through accuracy and F1-score metrics, Emo-MG achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models, validating the efficacy of integrating EEG and MG features

In light of the success of graph neural networks (GNNs), recent years have seen significant developments in modeling graphstructured data. Heterogeneous graphs have been widely adopted to model complex systems for various ML tasks. However, although some researchers have proposed methods for heterogeneous graphs, they merely focus on node features while neglecting the effectiveness of edge features. Besides, some research projects attempted incorporating edges into GNNs, but most regarded edge features as shared weights between node pairs. In this paper, we propose a two-stage method HES to learn the correlations among edge neighbors: (1) Graph Trans-formation: We convert the original heterogeneous graph into an undirected graph while preserving the orientation information, (2) Group Edge Sampling: To reduce the computation cost for the edge sampling in a heterogeneous graph, we propose to sample the most important edges over a group of edge neighbors instead of the whole graph, which leverages the edge features based on the one-hot encodings to describe the mutual influences between any adjacent edges. Finally, the experimental results on multiple public datasets show that HES outperforms existing state-of-the-art (SOTA) graph sampling methods. We further apply our approach to some existing GNN models as a pre-training process, demonstrating that HES can augment GNN-based models effectively.

Home anomaly monitoring is crucial for the elderly who live alone. A number of IoT-based home monitoring systems have been available, but most rely on privacy-intrusive cameras. With more and more concerns on privacy and security of human data, anomaly detection based on nonintrusive IoT devices becomes more desirable. Considering the elderly consumers, a low-cost system with good detection accuracy is further critical for the system's acceptability by elderly users. We propose a smart home monitoring system for living-alone senior citizens, relying on carefully designed, low-cost infrared sensor devices, as well as a cloud-based data processing and anomaly detection platform. Our PIR sensor device is effective in continuous monitoring of motion data in a user's apartment, and an open-hardware software platform is devised to support sensors manufactured by various vendors in the IoT system, all for cost reduction purpose. For privacy preservation, we encrypt collected data and store data indices in a blockchain system, to achieve efficient data access control and auditing. For motion anomaly detection, we propose a simple but effective environment adaptation method to work with the one-class support vector machine (OCSVM) method. Experiments driven by real-world traces show good reliability, accuracy, and efficiency of our system.