ProForm: Solder-Free Circuit Assembly Using ThermoformingElectronic 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.2025NPNarjes Pourjafarian et al.Circuit Making & Hardware PrototypingEcological Design & Green ComputingUIST
Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI ResearchIn the mental health domain, where AI technologies will impact their treatment and daily lives, patient engagement can be a 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 produce the data and who will be 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.2024DYDong Whi Yoo et al.Session 3b: Bridging Technology and TherapyCSCW
Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and TreatmentEarly 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.2024DYDong Whi Yoo et al.Kent State UniversityAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityMental Health Apps & Online Support CommunitiesCHI
Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information WorkersAlgorithmic 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.2024VSVedant Das Swain et al.Northeastern UniversityAI-Assisted Decision-Making & AutomationWorkplace Wellbeing & Work StressCHI
Exergy: A Toolkit to Simplify Creative Applications of Wind Energy HarvestingEnergy 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. https://dl.acm.org/doi/10.1145/35808142023JPHaesun Park et al.Aging-Friendly Technology DesignSustainable HCIEcological Design & Green ComputingUbiComp
Building Causal Agency in Autistic Students through Iterative Reflection in Collaborative Transition PlanningTransition planning is a collaborative process to promote agency in students with disabilities by encouraging them to participate in setting their own goals with team members and learn ways to assess their progress towards the goals. For autistic young adults who experience a lower employment rate, less stability in employment, and lower community connections than those with other disabilities, successful transition planning is an important opportunity to develop agency towards preparing and attaining success in employment and other areas meaningful to them. However, a failure of consistent information sharing among team members and opportunities for agency in students has prevented successful transition planning for autistic students. Therefore, this work brings causal agency theory and the collaborative reflection framework together to uncover ways transition teams can develop students' agency by collaboratively reflecting on students' inputs related to transition goals and progress. By interviewing autistic students, parents of autistic students, and professionals who were involved in transition planning, we uncovered that teams can better support student agency by accommodating their needs and encouraging their input in annual meetings, building relationships through transparent and frequent communication about day-to-day activities, centering goals on student's interests, and supporting student's skill-building in areas related to their transition goals. However, we found that many teams were not enacting these practices, leading to frustration and negative outcomes for young adults. Based on our findings, we propose a role for autistic students in the collaborative reflection framework that encouraged participation and builds causal agency. We also make design recommendations to encourage autistic students' participation in collaborative reflection around long-term and short-term needs in ways that promote their causal agency.2023RLRachel Lowy et al.Teaching and LearningCSCW
SwellSense: Creating 2.5D interactions with micro-capsule paperIn 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 first present the design guidelines to support various tactile interaction design including basic tactile graphic geometries, patterns with directional density, or finer 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.2023TCTingyu Cheng et al.Interactive ComputingShape-Changing Interfaces & Soft Robotic MaterialsData PhysicalizationMuseum & Cultural Heritage DigitizationCHI
Functional Destruction: Utilizing sustainable materials' physical transiency for electronics applicationsToday's electronics are manufactured to provide stable functionality and fixed physical forms optimized for reliable operation over long periods and repeated use. However, even when applications don't call for such robustness, the permanency of these electronics comes with environmental consequences. In this paper, we describe an alternative approach that utilizes sustainable transient electronics whose method of destruction is also key to their functionality. We create these electronics through three different methods: 1) by inkjet printing conductive silver traces on poly(vinyl alcohol) (PVA) substrates to create water-soluble sensors; 2) by mixing a conductive beeswax material configured as a meltable sensor; and 3) by fabricating edible electronics with 3D printed chocolate and culinary gold leaf. To enable practical applications of these devices, we implement a fully transient and sustainable chipless RF detection system.2023TCTingyu Cheng et al.Interactive ComputingShape-Changing Interfaces & Soft Robotic MaterialsSustainable HCIEcological Design & Green ComputingCHI
Algorithmic Power or Punishment: Information Worker Perspectives on Passive Sensing Enabled AI Phenotyping of Performance and WellbeingWe 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.2023VSVedant Das Swain et al.Georgia Institute of TechnologyPrivacy by Design & User ControlPrivacy Perception & Decision-MakingWorkplace Wellbeing & Work StressCHI
Supporting the Contact Tracing Process with WiFi Location Data: Opportunities and ChallengesContact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member's recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.2022KHKaely Hall et al.Georgia Institute of TechnologyInteractive Data VisualizationGeospatial & Map VisualizationCHI
PITAS: Sensing and Actuating Embedded Robotic Sheet for Physical Information CommunicationThis work presents PITAS, a thin-sheet robotic material composed of a reversible phase transition actuating layer and a heating/sensing layer. The synthetic sheet material enables non-expert makers to create shape-changing devices that can locally or remotely convey physical information such as shape, color, texture and temperature changes. PITAS sheets can be manipulated into various 2D shapes or 3D geometries using subtractive fabrication methods such as laser, vinyl, or manual cutting or an optional additive 3D printing method for creating 3D objects. After describing the design of PITAS, this paper also describes a study conducted with thirteen makers to gauge the accessibility, design space, and limitations encountered when PITAS is used as a soft robotic material while designing physical information communication devices. Lastly, this work reports on the results of a mechanical and electrical evaluation of PITAS and presents application examples to demonstrate its utility.2022TCTingyu Cheng et al.Interactive Computing, Interactive ComputingShape-Changing Interfaces & Soft Robotic MaterialsShape-Changing Materials & 4D PrintingCHI
Semantic Gap in Predicting Mental Wellbeing through Passive SensingWhen modeling passive data to infer individual mental wellbeing, a common source of ground truth is self-reports. But these tend to represent the psychological facet of mental states, which might not align with the physiological facet of that state. Our paper demonstrates that when what people ``feel'' differs from what people ``say they feel'', we witness a semantic gap that limits predictions. We show that predicting mental wellbeing with passive data (offline sensors or online social media) is related to how the ground-truth is measured (objective arousal or self-report). Features with psycho-social signals (e.g., language) were better at predicting self-reported anxiety and stress. Conversely, features with behavioral signals (e.g., sleep), were better at predicting stressful arousal. Regardless of the source of ground truth, integrating both signals boosted prediction. To reduce the semantic gap, we provide recommendations to evaluate ground truth measures and adopt parsimonious sensing.2022VSVedant Das Swain et al.Georgia Institute of TechnologyMental Health Apps & Online Support CommunitiesCHI