While ubiquitous computing research has explored diverse devices for personal health tracking, we know less about multi-device designs for family informatics, where health management is inherently collaborative. To understand how families adopt and perceive ubiquitous access to shared health data across contexts, we ev...
Lucas M. Silva, Evropi Stefanidi, Aehong Min et al.· 0 citations
A user study examining the difference in human behavior between an HRT scenario conducted in a virtual vs real environment and found differences in participants's strategy, their mental model of robots, and the type of trust they had for robots between modalities.
Sean Dallas, Absalat Getachew, Motaz Abuhijleh et al.· 0 citations
Teaching robotics relies on screen-based simulation, showing robot motion in an abstract coordinate frame rather than at real scale in the learner's own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays...
Santiago Berrezueta-Guzman, Habiba-Loai Khalil, A. Koshelev et al.· 0 citations
Service robots must respond to unexpected instructions in real-world environments. However, robots cannot detect all failures and exceptions during a task. To address these issues, we propose a real-time feedback function that enables robots to modify their behavior based on human feedback. In this system, users can in...
Ryo Terashima, Yuga Yano, Koshun Arimura et al.· 0 citations
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EarStreAM is presented, a closed-loop earable system for stress-adaptive meditation that integrates in-ear physiological sensing with personalized, real-time intervention and highlights how in-ear sensing, closed-loop adaptation, and personalized generative meditation can be integrated into an earable system for real-t...
Jonas Hummel, Luisa Faust, Elias Müller et al.· 0 citations
It is shown how infrared earables can make physiological feedback tangible through subtle, biosignal-adaptive thermal cues through subtle, ear-localized warming cues.
Valeria Zitz, Michael Küttner, Jonas Hummel et al.· 0 citations
This work examines how people reason about GenAI's appropriateness in DSM and conceptualizes this as boundary drawing and shows how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.
In practice-based design courses such as knit yarn design, students must turn visual ideas into feasible material outcomes. This is difficult because creative decisions are tied to yarn properties, stitch structures, machine operation, and limited opportunities for physical sampling. This study presents an integrated p...
People often want garments that reflect their aesthetic preferences, fit their bodies, and meet their sizing needs, yet turning these requirements into physical garments remains difficult. Ready-to-wear options provide limited personalization, while custom tailoring is costly and time-consuming. Recent generative artif...
Hong Qu, Zhao-Xiang Xu, Jin-Bo Luo et al.· 0 citations
Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical...
P. Pakianathan, R. Islambouli, Diogo Branco et al.· 0 citations
This work introduces interactional cultural markers, measurable patterns of doctor-patient interaction grounded in cross-cultural clinical communication, and uses them to compare real, simulated, and synthetic consultations from Indian and US clinical contexts to find distinct patterns of participation and control.
Krithi Shailya, Siddharth D. Jaiswal, A. Makani et al.· 0 citations
Low back pain (LBP) is a leading cause of disability worldwide and affects populations ranging from working adults to students with prolonged sitting habits. Conventional lumbar support belts are generally static and non-adaptive, which limits their ability to accommodate dynamic postural changes and individualized com...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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