Sensor-based interactive systems – e.g., "smart" speakers, webcams, and RFID tags – allow us to embed computational functionality into physical environments. They also expose users to real and perceived privacy risks: users know that device manufacturers, app developers, and malicious third parties want to collect and monetize their personal data, which fuels their mistrust of these systems even in the presence of privacy and security controls. We propose a new design paradigm, physically-intuitive privacy and security (PIPS), which aims to improve user trust by designing privacy and security controls that provide users with simple, physics-based conceptual models of their operation. PIPS consists of three principles: (1) direct physical manipulation of sensor state; (2) perceptible assurance of sensor state; and, (3) intent-aligned sensor (de)activation. We illustrate these principles through three case studies – Smart Webcam Cover, Powering for Privacy, and On-demand RFID – each of which has been shown to improve trust relative to existing sensor-based systems.

Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference due to latency, communication, or privacy constraints. Optimizing models for heterogeneous microcontrollers (MCUs) requires simultaneously satisfying hard physical constraints on memory, power, and temperature while preserving accuracy, a multidimensional optimization that is today performed manually by experts. We ask whether an LLM agent can autonomously navigate this complex, multi-turn pipeline guided by real hardware feedback, and introduce a hardware-in-the-loop agent arena in which the agent iteratively refines both model and firmware -- compiling, flashing, and measuring on real hardware -- to enable closed-loop optimization. Frontier models, including Claude Opus 4.7 and Gemini 3.1 Pro, fail entirely without hardware feedback (0% deployment success), whereas our hardware-in-the-loop formulation achieves the first successful deployment within three iterations and can surpass human expert results within seven. This agentic co-optimization achieves 250x compression for vision models with <3.3% accuracy loss and 400x for audio with <6% Feature Error Rate loss, enabling battery-free operation on a commercial MCU via solar harvesting. We demonstrate practical impact in two real-world systems: an elk-detection camera trap (96.7% accuracy) and a phonetic-transcription wearable (8.44% FER) for child development research.

Application developers frequently augment their code to produce event logs of specific operations performed by their users. Subsequent analysis of these event logs can help provide insight about the users’ behavior relative to its intended use. The analysis process typically includes both event organization and pattern discovery activities. However, most existing visual analytics systems for interaction log analysis excel at supporting pattern discovery and overlook the importance of flexible event organization. This omission limits the practical application of these systems. Therefore, we developed a novel visual analytics system called IntiVisor that implements the entire end-to-end interaction analysis approach. An evaluation of the system with interaction data from four visualization applications showed the value and importance of supporting event organization in interaction log analysis.

Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale. However, a traditional life cycle assessment (LCA) of an electronic device, which maps materials and processes to environmental impacts, often requires proprietary or unavailable data. Here, we reimagine conventional sustainability assessment by introducing a multimodal multi-agent AI system that emulates the collaborative process between LCA professionals and stakeholders (such as product managers and engineers) to automatically estimate the carbon footprint of electronic devices. The agents iteratively construct a complete life-cycle inventory by leveraging a structured data abstraction and software tools that mine information from the public internet, including repair communities and government regulatory databases. This reduces data gaps and data collection from weeks or months of expert time to under one minute. The system can calculate carbon footprint within 19

Electronic waste (e-waste) is a growing global challenge, with millions of functional components discarded due to the difficulty of repair and reuse. Traditional circuit assembly relies on soldering, which creates semi-permanent bonds that limit component recovery and contribute to unnecessary waste. We introduce ProForm, a thermoforming approach for solder-free circuit prototyping. By encapsulating electronic components with pressure-formed thermoplastics, ProForm enables secure, reversible mounting without the need for solder or custom mechanical housings. This approach supports a wide range of substrates, including flexible, paper-based, and non-planar circuits, facilitating easy reuse, replacement, and rapid prototyping. We demonstrate ProForm's versatility to support prototyping practices. We show that ProFormed circuits exhibit good electrical performance and mechanical stability. While motivated by a need for sustainable electronics practices, ProForm has other significant advantages over traditional soldering.

For today's electronics manufacturing process, the emphasis on stable functionality, durability, and fixed physical forms is designed to ensure long-term usability. However, this focus on robustness and permanence complicates the disassembly and recycling processes, leading to significant environmental repercussions. In this paper, we present three approaches that leverage easily recyclable materials-specifically, polyvinyl alcohol (PVA) and liquid metal (LM)-alongside accessible manufacturing techniques to produce electronic components and systems with versatile form factors. Our work centers on the development of recyclable electronics through three methods: 1) creating sheet electronics by screen printing LM traces on PVA substrates; 2) developing foam-based electronics by immersing mechanically stirred PVA foam into an LM solution; and 3) fabricating recyclable electronic tubes by injecting LM into mold cast PVA tubes, which can then be woven into various structures. To further assess the sustainability of our proposed methods, we conducted a life cycle assessment (LCA) to evaluate the environmental impact of our recyclable electronics in comparison to their conventional counterparts.

Algorithmic estimations of worker behavior are gaining popularity. Passive Sensing–enabled AI (PSAI) systems leverage behavioral traces from workers’ digital tools to infer their experience. Despite their conceptual promise, the practical designs of these systems elicit tensions that lead to workers resisting adoption. This paper teases apart the monolithic representation of PSAI by investigating system components that maximize value and mitigate concerns. We conducted an interactive online survey using the Experimental Vignette Method. Using Linear Mixed-effects Models we found that PSAI systems were more acceptable when sensing digital time use or physical activity, instead of visual modes. Inferences using language were only acceptable in work-restricted contexts. Compared to insights into performance, workers preferred insights into mental wellbeing. However, they resisted systems that automatically forwarded these insights to others. Our findings provide a template to reflect on existing systems and plan future implementations of PSAI to be more worker-centered.

Background Large-scale crisis events such as COVID-19 often have secondary impacts on individuals’ mental well-being. University students are particularly vulnerable to such impacts. Traditional survey-based methods to identify those in need of support do not scale over large populations and they do not provide timely insights. We pursue an alternative approach through social media data and machine learning. Our models aim to complement surveys and provide early, precise, and objective predictions of students disrupted by COVID-19. Objective This study aims to demonstrate the feasibility of language on private social media as an indicator of crisis-induced disruption to mental well-being. Methods We modeled 4124 Facebook posts provided by 43 undergraduate students, spanning over 2 years. We extracted temporal trends in the psycholinguistic attributes of their posts and comments. These trends were used as features to predict how COVID-19 disrupted their mental well-being. Results The social media–enabled model had an F1-score of 0.79, which was a 39% improvement over a model trained on the self-reported mental state of the participant. The features we used showed promise in predicting other mental states such as anxiety, depression, social, isolation, and suicidal behavior (F1-scores varied between 0.85 and 0.93). We also found that selecting the windows of time 7 months after the COVID-19–induced lockdown presented better results, therefore, paving the way for data minimization. Conclusions We predicted COVID-19–induced disruptions to mental well-being by developing a machine learning model that leveraged language on private social media. The language in these posts described psycholinguistic trends in students’ online behavior. These longitudinal trends helped predict mental well-being disruption better than models trained on correlated mental health questionnaires. Our work inspires further research into the potential applications of early, precise, and automatic warnings for individuals concerned about their mental health in times of crisis.

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

Early detection and intervention for relapse is important in the treatment of schizophrenia spectrum disorders. Researchers have developed AI models to predict relapse from patient-contributed data like social media. However, these models face challenges, including misalignment with practice and ethical issues related to transparency, accountability, and potential harm. Furthermore, how patients who have recovered from schizophrenia view these AI models has been underexplored. To address this gap, we first conducted semi-structured interviews with 28 patients and reflexive thematic analysis, which revealed a disconnect between AI predictions and patient experience, and the importance of the social aspect of relapse detection. In response, we developed a prototype that used patients' Facebook data to predict relapse. Feedback from seven patients highlighted the potential for AI to foster collaboration between patients and their support systems, and to encourage self-reflection. Our work provides insights into human-AI interaction and suggests ways to empower people with schizophrenia.

In this paper, we propose SwellSense, a fabrication technique to screen print stretchable circuits onto a special micro-capsule paper, creating localized swelling patterns with sensing capabilities. This simple technique will allow users to create a wide range of paper-based tactile interactive devices, which are mostly maintaining 2D planar form factor but can also be curved or folded into 3D interactive artifacts. We frst present the design guidelines to support various tactile interaction design including basic tactile graphic geometries, patterns with directional density, or fner interactive textures with embedded sensing such as touch sensor, pressure sensor, and mechanical switch. We then provide a design editor to enable users to design more creatively using the SwellSense technique. We provide a technical evaluation and user evaluation to validate the basic performance of SwellSense. Lastly, we demonstrate several application examples and conclude with a discussion on current limitations and future work.

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption. Environmental impacts span all stages of the lifecycle - manufacturing, operation, and disposal. A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices. This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud. The panel will also discuss potential cross-disciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

Social media platforms are being utilized by individuals with mental illness for engaging in self-disclosure, finding support, or navigating treatment journeys. Individuals also increasingly bring their social media data to psychotherapy consultations. This emerging practice during psychotherapy can help us to better understand how patients appropriate social media technologies to develop and iterate patient narratives -- the stories of patients' own experiences that are vital in mental health treatment. In this paper, we seek to understand patients' perspectives regarding why and how they bring up their social media activities during psychotherapy consultations as well as related concerns. Through interviews with 18 mood disorder patients, we found that social media helps augment narratives around interpersonal conflicts, digital detox, and self-expression. We also found that discussion of social media activities shines a light on the power imbalance and privacy concerns regarding use of patient-generated health information. Based on the findings, we discuss that social media data are different from other types of patient-generated health data in terms of supporting patient narratives because of the social interactions and curation social media inherently engenders. We also discuss privacy concerns and trust between a patient and a therapist when patient narratives are supported by patients' social media data. Finally, we suggest design implications for social computing technologies that can foster patient narratives rooted in social media activities.

We are witnessing an emergence in Passive Sensing enabled AI (PSAI) to provide dynamic insights for performance and wellbeing of information workers. Hybrid work paradigms have simultaneously created new opportunities for PSAI, but have also fostered anxieties of misuse and privacy intrusions within a power asymmetry. At this juncture, it is unclear if those who are sensed can find these systems acceptable. We conducted scenario-based interviews of 28 information workers to highlight their perspectives as data subjects in PSAI. We unpack their expectations using the Contextual Integrity framework of privacy and information gathering. Participants described appropriateness of PSAI based on its impact on job consequences, work-life boundaries, and preservation of flexibility. They perceived that PSAI inferences could be shared with selected stakeholders if they could negotiate the algorithmic inferences. Our findings help envision worker-centric approaches to implementing PSAI as an empowering tool in the future of work.

Energy harvesting reduces the burden of power source maintenance and promises to make computing systems genuinely ubiquitous. Researchers have made inroads in this area, but their novel energy harvesting materials and fabrication techniques remain inaccessible to the general maker communities. Therefore, this paper aims to provide a toolkit that makes energy harvesting accessible to novices. In Study 1, we investigate the challenges and opportunities associated with devising energy harvesting technology with experienced researchers and makers (N=9). Using the lessons learned from this investigation, we design a wind energy harvesting toolkit, Exergy, in Study 2. It consists of a simulator, hardware tools, a software example, and ideation cards. We apply it to vehicle environments, which have yet to be explored despite their potential. In Study 3, we conduct a two-phase workshop: hands-on experience and ideation sessions. The results show that novices (N=23) could use Exergy confidently and invent self-sustainable energy harvesting applications creatively.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students’ learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility —- a methodology we refer to as WiFi mobility models (W i M ob ). This approach enables policymakers to explore more granular policies like localized closures (LC). W i M ob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, W i M ob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from W i M ob , we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. W i M ob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

A comprehensive understanding of collocation can help understand performance outcomes. For university cohorts, this needs data that describes large groups over a long period. Harnessing user devices to infer this, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors in the environment is limited by the expense of covering the entire campus. We investigate the feasibility of leveraging WiFi association logs for this purpose. While these provide coarse approximations of location, these are easily obtainable and depict multiple users on campus over a semester. We explore how these coarse collocations are related to individual performance. Specifically, we inspect the association between individual performance and the collocation behaviors of project group members. We study 163 students (in 54 project groups) over 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student's final score. We find collocation behaviors show a significant correlation (Pearson's r = 0.24) with performance -- better than both peer feedback or individual behaviors like attendance. Finally, we discuss how repurposing WiFi logs can facilitate applications for domains like mental wellbeing and physical health.

Energy harvesting reduces the burden of power source maintenance and promises to make computing systems genuinely ubiquitous. Researchers have made inroads in this area, but their novel energy harvesting materials and fabrication techniques remain inaccessible to the general maker communities. Therefore, this paper aims to provide a toolkit that makes energy harvesting accessible to novices. In Study 1, we investigate the challenges and opportunities associated with devising energy harvesting technology with experienced researchers and makers (N=9). Using the lessons learned from this investigation, we design a wind energy harvesting toolkit, Exergy, in Study 2. It consists of a simulator, hardware tools, a software example, and ideation cards. We apply it to vehicle environments, which have yet to be explored despite their potential. In Study 3, we conduct a two-phase workshop: hands-on experience and ideation sessions. The results show that novices (N=23) could use Exergy confidently and invent self-sustainable energy harvesting applications creatively.

Mark Weiser predicted in 1991 that computing would lead to individuals interacting with countless computing devices, seamlessly integrating them into their daily lives until they disappear into the background [42]. However, achieving this seamless integration while addressing the associated environmental concerns is challenging. Trillions of smart devices with varied capabilities and form-factor are needed to build a networked environment of this magnitude. Yet, conventional computing paradigms require plastic housings, PCB boards, and rare-earth minerals, coupled with hazardous waste, and challenging reclamation and recycling, leading to significant e-waste. The current linear lifecycle design of electronic devices does not allow circulation among different life stages, neglecting features like recyclability and repairability during the design process. In this position paper, we present the concept of computational materials designed for transiency as a substitute for current devices. We envision that not all devices must be designed with performance, robustness, or even longevity as the sole goal. We detail computer systems challenges to the circular economy of computational materials and provide strategies and sketches of tools to assess a device's entire lifetime environmental impact.

Smart speakers come with always-on microphones to facilitate voice-based interaction. To address user privacy concerns, existing devices come with a number of privacy features: e.g., mute buttons and local trigger-word detection modules. But it is difficult for users to trust that these manufacturer-provided privacy features actually work given that there is a misalignment of incentives: Google, Meta, and Amazon benefit from collecting personal data and users know it. What's needed is perceptible assurance- privacy features that users can, through physical perception, verify actually work. To that end, we introduce, implement, and evaluate the idea of "intentionally-powered" microphones to provide users with perceptible assurance of privacy with smart speakers. We employed an iterative-design process to develop Candid Mic, a battery-free, wireless microphone that can only be powered by harvesting energy from intentional user interactions. Moreover, users can visually inspect the (dis)connection between the energy harvesting module and the microphone. Through a within-subjects experiment, we found that Candid Mic provides users with perceptible assurance about whether the microphone is capturing audio or not, and improves user trust in using smart speakers relative to mute button interfaces.