This work investigates generative facial expression interfaces for intelligent agents from a meta-design perspective. We propose the Generative Personalized Facial Expression Interface (GPFEI) framework, which organizes rule-bounded spaces, character identity, and context–expression mapping to address challenges of control, coherence, and alignment in run-time facial expression generation. To operationalize this framework, we developed GenFaceUI, a proof-of-concept tool that enables designers to create templates, apply semantic tags, define rules, and iteratively test outcomes. We evaluated the tool through a qualitative study with twelve designers. The results show perceived gains in controllability and consistency, while revealing needs for structured visual mechanisms and lightweight explanations. These findings provide a conceptual framework, a proof-of-concept tool, and empirical insights that highlight both opportunities and challenges for advancing generative facial expression interfaces within a broader meta-design paradigm.

This work investigates the integration of generative visual aids in human-robot task communication. We developed GenComUI, a system powered by large language models that dynamically generates contextual visual aids (such as map annotations, path indicators, and animations) to support verbal task communication and facilitate the generation of customized task programs for the robot. This system was informed by a formative study that examined how humans use external visual tools to assist verbal communication in spatial tasks. To evaluate its effectiveness, we conducted a user experiment (n = 20) comparing GenComUI with a voice-only baseline. The results demonstrate that generative visual aids, through both qualitative and quantitative analysis, enhance verbal task communication by providing continuous visual feedback, thus promoting natural and effective human-robot communication. Additionally, the study offers a set of design implications, emphasizing how dynamically generated visual aids can serve as an effective communication medium in human-robot interaction. These findings underscore the potential of generative visual aids to inform the design of more intuitive and effective human-robot communication, particularly for complex communication scenarios in human-robot interaction and LLM-based end-user development.

End-user development allows everyday users to tailor service robots or applications to their needs. One user-friendly approach is natural language programming. However, it encounters challenges such as an expansive user expression space and limited support for debugging and editing, which restrict its application in end-user programming. The emergence of large language models (LLMs) offers promising avenues for the translation and interpretation between human language instructions and the code executed by robots, but their application in end-user programming systems requires further study. We introduce Cocobo, a natural language programming system with interactive diagrams powered by LLMs. Cocobo employs LLMs to understand users’ authoring intentions, generate and explain robot programs, and facilitate the conversion between executable code and flowchart representations. Our user study shows that Cocobo has a low learning curve, enabling even users with zero coding experience to customize robot programs successfully.

Running is a highly popular form of exercise, while incorrect running posture over an extended period can lead to severe knee injuries. Smart textiles have recently demonstrated significant potential for continuous motion monitoring. This study involved the design and development of a smart legging with a resistive textile sensor network to monitor lower body motion. The study consists of three main parts. Firstly, we tested textile sensors in terms of linearity and robustness to determine the basic sensor unit that can monitor the characteristics of running postures. Next, optimal sensor placement was determined through comparison experiments, and a sensor network was proposed. Finally, based on the LSTM model with data gathered from 6 participants, we developed the smart legging system that is capable of identifying three types of improper running postures and normal postures with 99.1% accuracy. The evaluation revealed that the smart legging system had the potential to help users adjust their running postures to prevent knee injury through continuous monitoring and multi-modal feedback.

Textile sensors have demonstrated significant potential in next-generation wearable systems due to their excellent performance and unobtrusive nature. By building specialized sensing networks and algorithms, textile-based wearable systems can estimate the continuous motion angles of human joints with desirable accuracies. This article offers a systematic review aimed at identifying key challenges in this field and encouraging further applications of textile strain sensor networks within the human–computer interaction (HCI) community. To achieve this, we conducted an exhaustive literature search across four major databases: IEEE Xplore, PubMed, Scopus, and Web of Science, spanning from January 2016 to August 2023. Applying inclusion and exclusion criteria, we narrowed down 2684 results to a total of 24 relevant papers. To analyze these studies, we proposed a framework that incorporates both technical aspects – such as textile strain sensors, sensor placement, algorithms, and technical evaluations – and contextual factors like target users, wearability, and application scenarios. Our analysis uncovered two critical research gaps: First, it exists an incongruity between the development of textile-based wearables and the advancements in textile sensors. Second, there is a noticeable absence of contextual design considerations in this specific domain. To address these issues, we offer discussions and recommendations from three perspectives: 1) enhancing the robustness of textile-sensing networks, 2) improving wearability, and 3) expanding application scenarios.

As electronic textiles have become more advanced in sensing, actuating, and manufacturing, incorporating smartness into fabrics has become of special interest to ubiquitous computing and interaction researchers and designers. However, innovating smart textile interfaces for numerous input and output modalities usually requires expert-level knowledge of specific materials, fabrication, and protocols. This paper presents EmTex, a construction kit based on embroidered textiles, patterned with dedicated sensing, actuating, and connecting components to facilitate the design and prototyping of smart textile interfaces. With machine embroidery, EmTex is compatible with a wide range of threads and underlay fabrics, proficient in various stitches to control the electric parameters, and capable of integrating versatile and reliable interaction functionalities with aesthetic patterns and precise designs. EmTex consists of 28 textile-based sensors, actuators, connectors, and displays, presented with standardized visual and tactile effects. Along with a visual programming tool, EmTex enables the prototyping of everyday textile interfaces for diverse life-living scenarios, that embody their touch input, and visual and haptic output properties. With EmTex, we conducted a workshop and invited 25 designers and makers to create freeform textile interfaces. Our findings revealed that EmTex helped the participants explore novel interaction opportunities with various smart textile prototypes. We also identified challenges EmTex shall face for practical use in promoting the design innovation of smart textiles.

Stroke is a cardiovascular and cerebrovascular disease that affects the aged population at a high rate. Patients’ functional disabilities can be reduced with effective rehabilitation training. However, due to a lack of hospital resources and a social yearning for family contact, patients frequently discontinue rehabilitation training sessions and return home to their local community. Such a shift emphasizes the value of home and community-based rehabilitation, where patients can perform daily training with remote support from therapists. In this survey, the technologies that assist stroke rehabilitation will be discussed in following aspects: (1) technologies for home-based stroke rehabilitation; (2) technologies for community-based stroke rehabilitation; (3) technologies for therapist’s engagement in remote rehabilitation. A comprehensive overview of technologies that support home and community-based stroke rehabilitation was presented, as well as insights into future research themes.

Explanations have become increasingly vital in communicating with human drivers about the reasons for the decision-making of autonomous vehicles (AVs), particularly in tactical-level driving tasks. Focusing on lane-changing scenarios, we examine whether providing tactical-level explanations and in addition, whether providing a confirmation option, influences drivers’ decision-making, trust, and emotional experience. Thirty participants were equally assigned into three groups: indicator (I), explanation (E), and explanation + confirmation (EC), experiencing four lane-changing scenarios in a driving simulator. Real-time question probes and interviews were adopted to understand drivers’ decision-making process, and post-drive questionnaires on trust and emotional experience were given. Results indicated that merely providing tactical-level explanations had little effect on driver’s trust and experience, but caused worse decision-making performance. The option to confirm lane changes after an explanation promoted driver’s trust, but brought two-sided effects on decision-making performance. Situational trust and decision-making performance varied significantly across lane-changing scenarios.

Service robots are envisioned to be adaptive to their working environment based on situational knowledge. Recent research focused on designing visual representation of knowledge graphs for expert users. However, how to generate an understandable interface for non-expert users remains to be explored. In this paper, we use knowledge graphs (KGs) as a common ground for knowledge exchange and develop a pattern library for designing KG interfaces for non-expert users. After identifying the types of robotic situational knowledge from the literature, we present a formative study in which participants used cards to communicate the knowledge for given scenarios. We iteratively coded the results and identified patterns for representing various types of situational knowledge. To derive design recommendations for applying the patterns, we prototyped a lab service robot and conducted Wizard-of-Oz testing. The patterns and recommendations could provide useful guidance in designing knowledge-exchange interfaces for robots.

The emergence of social media creates a unique opportunity for developing a new class of crowd-powered information collection systems. Such systems actively identify potential users based on their public social media posts and solicit them directly for information. While studies have shown that users will respond to solicitations in a few domains, there is little analysis of the quality of information received. Here we explore the quality of information solicited from Twitter users in the domain of product reviews, specifically reviews for a popular tablet computer and L.A.-based food trucks. Our results show that the majority of responses to our questions (>70%) contained relevant information and often provided additional details (>37%) beyond the topic of the question. We compare the solicited Twitter reviews to other user-generated reviews from Amazon and Yelp, and found that the Twitter answers provided similar information when controlling for the questions asked. Our results also reveal limitations of this new information collection method, including its suitability in certain domains and potential technical barriers to its implementation. Our work provides strong evidence for the potential of this new class of information collection systems and design implications for their future use.

Dandelion is a tool that extends wikis to support coordinated, collaborative authoring using a tag-based approach. Specifically, users can insert tags in a wiki page to specify various co-authoring tasks. These tags can then be executed to help drive and manage the collaboration workflow, and provide content-centric collaboration awareness for all the co-authors. Four successful pilot deployments and positive user feedback show the practical value of Dandelion, especially its value in supporting a structured, collaborative authoring process often seen in business settings.