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発表担当

終了した勉強会

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Meaningful Technology at Work – A Reflective Design Case of Improving Radiologists' Wellbeing Through Medical Technology
説明

In radiology, medical technology providers (MTP) focus mainly on technology-related issues, such as image quality or efficiency of reporting. Broader notions of radiology as "meaningful work" are largely seen as out of scope for an MTP. The present paper challenges this. In a real-world case with a large MTP, we showed that medical technology could be designed more holistically to explicitly improve radiologists' wellbeing. We first gathered work practices experienced as especially conducive to wellbeing. From there, we distilled ideal practices to increase wellbeing and turned them into two software applications. The MTP's initial skepticism dissolved, while radiologists unanimously emphasized wellbeing and demonstrated how they work towards improving it. Based on our insights, the applications resonated well among the radiologists involved, the healthcare provider, and other customers of the MTP. We close with a critical reflection of the challenges and opportunities of designing wellbeing-driven technology in the work domain.

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BodyLights: Open-Ended Augmented Feedback to Support Training Towards a Correct Exercise Execution
説明

Technologies targeting a correct execution of physical training exercises typically use pre-determined models for what they consider correct, automatizing instruction and feedback. This falls short on catering to diverse trainees and exercises. We explore an alternative design approach, in which technology provides open-ended feedback for trainers and trainees to use during training. With a personal trainer we designed the augmentation of 18 strength training exercises with BodyLights: 3D printed wearable projecting lights that augment body movement and orientation. To study them, 15 trainees at different skill levels trained three times with our personal trainer and BodyLights. Our findings show that BodyLights catered to a wide range of trainees and exercises, and supported understanding, executing and correcting diverse technique parameters. We discuss design features and methodological aspects that allowed this; and what open-ended feedback offered in comparison to current technology approaches to support training towards a correct exercise execution.

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PuffPacket: A Platform for Unobtrusively Tracking the Fine-grained Consumption Patterns of E-cigarette Users
説明

The proliferation of e-cigarettes and portable vaporizers presents new opportunities for accurately and unobtrusively tracking e-cigarette use. PuffPacket is a hardware and soft-ware research platform that leverages the technology built into vaporizers, e-cigarettes and other electronic drug delivery devices to ubiquitously track their usage. The system piggybacks on the signals these devices use to directly measure and track the nicotine consumed by users. PuffPacket augments e-cigarettes with Bluetooth to calculate the frequency, intensity, and duration of each inhalation. This information is augmented with smartphone-based location and activity information to help identify potential contextual triggers. Puff-Packet is generalizable to a wide variety of electronic nicotine,THC, and other drug delivery devices currently on the mar-ket. The hardware and software for PuffPacket is open-source so it can be expanded upon and leveraged for mobile health tracking research.

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CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
説明

The recent development of data-driven AI promises to automate medical diagnosis; however, most AI functions as 'black boxes' to physicians with limited computational knowledge. Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain — a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: (i) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (ii) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (iii) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI. We summarize by discussing recommendations for future work to design and implement explainable medical AI systems that encompass four recurring themes: motivation, constraint, explanation, and justification.

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The Struggle for Recognition in Advanced Dementia: Implications for Experience-Centered Design
説明

Focusing on the person with advanced dementia as a social being presents a new opportunity for Experience-Centered Design (ECD), opening design to appreciate the agency and intentional actions of the person with advanced dementia. If Human-Computer Interaction is to shift from the predominantly assistive approach to a focus on experience, a theoretical framing that emphasizes the relational nature of selfhood is needed. In this article, we present Recognition Theory—a social theory based on an inter-subjectivist account of the struggle for recognition—to extend ECD approaches for advanced dementia. Focusing on people with advanced dementia, we examine recognition as a social and ethical perspective for establishing and maintaining self. We present a framework for design based on research with people with advanced dementia, experience-centered engagement and social identity, that will support designers to craft opportunities for mutual recognition in the design process and the practice of making.

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