Papers

Proactive AI as a Catalyst for Creativity? Balancing Human Agency and AI Contribution in Collaborative Story Writing

Large Language Models (LLMs) hold promise in supporting creative writing, yet the role of proactive AI in collaborative writing remains underexplored due to concerns around human agency and disruption. To investigate effective strategies for proactive AI support, we conducted a Wizard-of-Oz study simulating two suggestion styles: intrusive suggestions (next-sentence completions) and non-intrusive suggestions (exploratory proposals), where participants completed two story outlining tasks under each style, receiving real-time proactive suggestions from a human wizard acting as the AI. Both quantitative and qualitative results show that proactive AI can enhance creativity and accelerate writing. However, we observed a trade-off between AI involvement and perceived human agency. This trade-off was moderated by how strongly AI stimulated users—greater inspiration led to stronger perceived agency even under high AI involvement. Based on wizards' behavior, we offer guidance on suggestion style and timing to better balance creativity and agency for future proactive AI writing systems.

FeelWave: Enabling Emotion-Aware Voice Interaction through Noise-Robust mmWave Emotion Sensing

Voice has been a primary interaction mode with LLM-powered assistants. Beyond semantics, voice carries emotional cues with potential to guide empathetic system responses. Yet, robust vocal emotion sensing in noise and its use in optimizing interactions remain underexplored. In response, we present FeelWave, which achieves empathetic voice interaction through noise-robust mmWave emotion sensing and structured LLM prompts. It extracts robust vocal information from mmWave signals, applies audio-to-mmWave transfer learning for efficient emotion recognition, and employs chain-of-thought-based query optimization to enable emotion-adaptive responses. Evaluations show that FeelWave achieves 92.3% emotion recognition accuracy and remains robust in noisy environments, yielding a 62.9 percentage-point gain over audio-based models. In voice interaction studies, 74.3% of users prefer FeelWave, reporting significantly higher satisfaction than a baseline without emotion sensing (4.37 vs. 3.22). A SUS score of 88.3 confirms FeelWave's high usability in real-world deployment. We hope this work will inspire more empathetic, user-centered AI-driven assistants.

ATRU: A Stage-based Framework for Designing Ethology-Inspired Social Robots

Animal behavior (ethology) has emerged as a promising source of inspiration for social robot design. However, existing efforts have commonly resulted in isolated design instances. Our high-level understanding of the design processes for integrating ethological insights into social robot design and evaluation remains limited. To address this gap, we conducted a two-step investigation. First, we developed a stage-based framework through a systematic review, identifying six core design stages along with their descriptive dimensions. Using this framework as an analytic lens, we then analyzed design cases drawn from academic, commercial, and public contexts, deriving stage-specific considerations and actionable strategies to support designers in navigating the process. Our findings provide a conceptual scaffold for operationalizing ethology as a design resource, enabling more systematic, reflective, and transferable practices, while also surfacing new opportunities for future social robot interaction design.

FlowGait: Enabling Robust Long-Term Gait Recognition Across Real-World Covariates with mmWave Radar

Gait recognition enables proactive and personalized smart home interactions, but its long-term reliability is challenged by the non-static nature of gait. Covariates like carrying items and clothing induce a persistent domain shift that degrades traditional, static models. To solve this, we introduce FlowGait, a mmWave-based framework designed for robust, long-term adaptation. It combines self-training with continual learning, allowing the model to daily align with a user's evolving gait by learning from readily available unlabeled data. It features a specialized transformer network for radar spectrogram analysis and a novel two-stage labeling algorithm that leverages the gait's hierarchical nature to assign pseudo-labels to the unlabeled data accurately. Evaluated on three challenging datasets from 47 volunteers (covering 12 gait-covariates, 11 routes, and two weeks), FlowGait achieves high accuracies of 94.8% (cross-covariate), 98.6% (cross-route), and 95.5% (cross-day). Notably, for the long-term dataset, it reduced performance decay from 13.6% to just 1.4%, demonstrating its real-world robustness.

Application of Prompt Learning Models in Identifying the Collaborative Problem Solving Skills in an Online Task

Collaborative problem solving (CPS) competence is considered one of the essential 21st-century skills. To facilitate the assessment and learning of CPS competence, researchers have proposed a series of frameworks to conceptualize CPS and explored ways to make sense of the complex processes involved in collaborative problem solving. However, encoding explicit behaviors into subskills within the frameworks of CPS skills is still a challenging task. Traditional studies have relied on manual coding to decipher behavioral data for CPS, but such coding methods can be very time-consuming and cannot support real-time analyses. Scholars have begun to explore approaches for constructing automatic coding models. Nevertheless, the existing models built using machine learning or deep learning techniques depend on a large amount of training data and have relatively low accuracy. To address these problems, this paper proposes a prompt-based learning pre-trained model. The model can achieve high performance even with limited training data. In this study, three experiments were conducted, and the results showed that our model not only produced the highest accuracy, macro F1 score, and kappa values on large training sets, but also performed the best on small training sets of the CPS behavioral data. The application of the proposed prompt-based learning pre-trained model contributes to the CPS skills coding task and can also be used for other CSCW coding tasks to replace manual coding.

Wi-Painter: Fine-grained Material Identification and Image Delineation Using COTS WiFi Devices

Yan 等人提出 Wi-Painter 系统，利用商用 WiFi 设备实现细粒度材料识别与图像描绘，推动低成本传感应用发展。

EarSleep: In-ear Acoustic-based Physical and Physiological Activity Recognition for Sleep Stage Detection

Han 等人开发 EarSleep 系统，利用入耳式声学传感器采集睡眠期间的生理信号，实现高准确率的睡眠阶段自动识别。

LiquImager: Fine-grained Liquid Identification and Container Imaging System with COTS WiFi Devices

PackquID: In-packet Liquid Identification Using RF Signals

There are many scenarios where the liquid is occluded by other items (e.g. books in a packet), in which existing RF-based liquid identification methods are generally not suitable. Moreover, status methods are not applicable when the height of the liquid to be tested changes. This paper proposes PackquID, an RF-based in-packet liquid identification system, which can identify liquid without prior knowledge. In dealing with the obstruction of other items and the unknown container, we utilize a dual-antenna model and craft a relative frequency response factor, exploring the diversity of the permittivity in the frequency domain. In tackling the variable liquid height, we extend our model to 3D scope by analyzing the electric field distribution and solving the height effect via spatial-differential model. With 500 pages of printer paper obscured, PackquID can identify 9 common liquids, including Coca-Cola and Pepsi, with an accuracy of over 86% for 4 different packets (canvas bag, paper bag, backpack, and box) and 4 different containers. Nevertheless, PackquID can still identify liquids with an accuracy rate of over 87%, even when the liquid height changes from 4 cm to 12 cm. https://dl.acm.org/doi/10.1145/3569469

AnisoTag: 3D Printed Tag on 2D Surface via Reflection Anisotropy

In the past few years, the widespread use of 3D printing technology enables the growth of the market of 3D printed products. On Esty, a website focused on handmade items, hundreds of individual entrepreneurs are selling their 3D printed products. Inspired by the positive effects of machine-readable tags, like barcodes, on daily product marketing, we propose AnisoTag, a novel tagging method to encode data on the 2D surface of 3D printed objects based on reflection anisotropy. AnisoTag has an unobtrusive appearance and much lower extraction computational complexity, contributing to a lightweight low-cost tagging system for individual entrepreneurs. On AnisoTag, data are encoded by the proposed tool as reflective anisotropic microstructures, which would reflect distinct illumination patterns when irradiating by collimated laser. Based on it, we implement a real-time detection prototype with inexpensive hardware to determine the reflected illumination pattern and decode data according to their mapping. We evaluate AnisoTag with various 3D printer brands, filaments, and printing parameters, demonstrating its superior usability, accessibility, and reliability for practical usage.

SimpModeling: Sketching Implicit Field to Guide Mesh Modeling for 3D Animalmorphic Head Design

Head shapes play an important role in 3D character design. In this work, we propose SimpModeling, a novel sketch-based system for helping users, especially amateur users, easily model 3D animalmorphic heads - a prevalent kind of head in character design. Although sketching provides an easy way to depict desired shapes, it is challenging to infer dense geometric information from sparse line drawings. Recently, deepnet-based approaches have been taken to address this challenge and try to produce rich geometric details from very few strokes. However, while such methods reduce users' workload, they would cause less controllability of target shapes. This is mainly due to the uncertainty of the neural prediction. Our system tackles this issue and provides good controllability from three aspects: 1) we separate coarse shape design and geometric detail specification into two stages and respectively provide different sketching means; 2) in coarse shape designing, sketches are used for both shape inference and geometric constraints to determine global geometry, and in geometric detail crafting, sketches are used for carving surface details; 3) in both stages, we use the advanced implicit-based shape inference methods, which have strong ability to handle the domain gap between freehand sketches and synthetic ones used for training. Experimental results confirm the effectiveness of our method and the usability of our interactive system. We also contribute to a dataset of high-quality 3D animal heads, which are manually created by artists.

TeethTap: Recognizing Discrete Teeth Gestures using Motion and Acoustic Sensing on an Earpiece

Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices.

Tessutivo: Contextual Interactions on Interactive Fabrics with Inductive Sensing

We present Tessutivo, a contact-based inductive sensing technique for contextual interactions on interactive fabrics. Our technique recognizes conductive objects (mainly metallic) that are commonly found in households and workplaces, such as keys, coins, and electronic devices. We built a prototype containing six by six spiral-shaped coils made of conductive thread, sewn onto a four-layer fabric structure. We carefully designed the coil shape parameters to maximize the sensitivity based on a new inductance approximation formula. Through a ten- participant study, we evaluated the performance of our proposed sensing technique across 27 common objects. We yielded 93.9% real-time accuracy for object recognition. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique.