The maker movement embodies a resurgence in DIY creation, merging physical craftsmanship and arts with digital technology support. However, mere technological skills and creativity are insufficient for economically and psychologically sustainable practice. By illuminating and smoothing the path from "maker" to "maker entrepreneur," we can help broaden the viability of making as a livelihood. Our research centers on makers who design, produce, and sell physical goods. In this work, we explore the transition to entrepreneurship for these makers and how technology can facilitate this transition online and offline. We present results from interviews with 20 USA-based maker entrepreneurs (i.e., lamps, stickers), six creative service entrepreneurs (i.e., photographers, fabrication), and seven support personnel (i.e., art curator, incubator director). Our findings reveal that many maker entrepreneurs 1) are makers first and entrepreneurs second; 2) struggle with business logistics and learn business skills as they go; and 3) are motivated by non-monetary values. We discuss training and technology-based design implications and opportunities for addressing challenges in developing economically sustainable businesses around making.

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

For a robot to repair its own error, it must first know it has made a mistake. One way that people detect errors is from the implicit reactions from bystanders - their confusion, smirks, or giggles clue us in that something unexpected occurred. To enable robots to detect and act on bystander responses to task failures, we developed a novel method to elicit bystander responses to human and robot errors. Using 46 different stimulus videos featuring a variety of human and machine task failures, we collected a total of 2,452 webcam videos of human reactions from 54 participants. To test the viability of the collected data, we used the bystander reaction dataset as input to a deep-learning model, BADNet, to predict failure occurrence. We tested different data labeling methods and learned how they affect model performance, achieving precisions above 90%. We discuss strategies (manual labelling, failure-vs-control, and failure-time) used to model bystander reactions and predict failure, and how this approach can be used in real-world robotic deployments to detect errors and improve robot performance. As part of this work, we also contribute with the “Bystander Affect Detection” (BAD) dataset of bystander reactions, supporting the development of better prediction models.

Most robots are unclothed. However, we believe that robot clothes present an underutilized opportunity for the field of designing interactive systems. Clothes can help robots become better robots––by helping them be useful in a new, wider array of contexts, or better adapt and function in the contexts they are already in. In this paper, we provide a foundation for a research area of robot clothing by speculating on its potential. We systematically present functional requirements of robot clothing, considerations, and parameters for robot clothing designers, as well as key reference cases of robots in clothes. We then discuss what robot clothes can do specifically for the field of designing interactive systems.

To help both designers and people with tetraplegia fully realize the benefts of voice assistant technology, we conducted interviews with fve people with tetraplegia in the home to understand how this population currently uses voice-based interfaces as well as other technologies in their everyday tasks. We found that people with tetraplegia use voice assistants in specifc places, such as in their beds, or when traveling in their wheelchair. In addition, we note the inefciencies for people with tetraplegia when using voice assistance.

People with Developmental Disabilities (DD) often rely on other people to perform basic activities such as leaving the house and accessing public spaces. This problem, exaggerated by a decrease in community engagement, has been documented to decrease their sense of self-efficacy. Telepresence robots provide a unique opportunity for people with DD to access public spaces, particularly for those who are homebound or dependent on others for using transportation or buying exhibit tickets. This research evaluates the use of telepresence robots operated by people with DD in exploring a public exhibit. This study was in partnership with Hope Services, an organization that provides skill-improving activities for people with DD. Our analysis consisted of quantitative and qualitative methods using data from semi-structured pre- and post-interviews focusing on participants' sense of physical and social self- efficacy, and well-being. Our study revealed positive trends toward showing that using telepresence can contribute to wellbeing and physical and social self-efficacy. Therefore, we believe that there is some promise for using telepresence robots to tour an exploratory space for people with DD and that it can be a viable option for those who face accessibility limitations.

SpokeIt is a mobile serious game for health designed to support speech articulation therapy. Here, we present SpokeIt as well as 2 preceding speech therapy prototypes we built, all of which use a novel offline critical speech recognition system capable of providing feedback in real-time. We detail key design motivations behind each of them and report on their potential to help adults with speech impairment co-occurring with developmental disabilities. We conducted a qualitative within-subject comparative study on 5 adults within this target group, who played all 3 prototypes. This study yielded refined functional requirements based on user feedback, relevant reward systems to implement based on user interest, and insights on the preferred hybrid game structure, which can be useful to others designing mobile games for speech articulation therapy for a similar target group.

This paper describes a long term user study in which 13 blind participants were asked to use a blind friendly iPhone app, VizSnap -- an app designed to assist blind people in organizing and browsing a photo library without sight -- for a total of two months. VizSnap records audio while the user is aiming the camera, and allows an optional voice memo to be recorded, to allow the user to give custom information to accompany the photo, as well as capturing time, date, and location the photo was taken. All this information is available to the user when browsing through VizSnap's photo library. The participants met with us every two weeks, in which we discuss general VizSnap usage, conduct a short user study with their photos, as well as upload all data that was gathered using VizSnap. The user study aims to determine whether accompanying audio, time, date, and location metadata assists in memory retrieval of photos by blind people. We found that in general, both ambient audio and voice memo are considered most helpful for memory retrieval.