Artificial Intelligence (AI) is increasingly automating expert judgments across diverse domains. However, the practical dynamics of adaptation among diverse stakeholders remain underexplored. We investigated the Korea Baseball Organization’s adoption of the Automated Ball-Strike System (ABS), the first league-wide deployment of an AI adjudicator. Interviews with 38 stakeholders—umpires, players, coaches, and fans—revealed that adoption was driven by demands for fairness and frustration with human limitations, and was viewed as an inevitable trajectory. Acceptance depended less on accuracy than on verifiable consistency, which reduced interpersonal conflict by shifting judgment to technology. However, adaptive burdens were redistributed: players faced pressure to recalibrate strategies for survival, while umpires grappled with diminished authority. Systemic legitimacy hinged on procedural transparency and visible feedback mechanisms. Based on these findings, we propose governance principles emphasizing transparency and adaptive role reconfiguration for sustainable human-AI coexistence.
Modern media consumption habits challenge `lean-back' viewers—who prefer passive viewing—to stay engaged during the frequent periods of Non-Event Time in soccer matches. Existing commentary options often fail this audience, being either too dry or too interactive. To investigate their needs, we developed \textbf{ARUA}, a prototype that positions users as `directors' of their own AI commentary. This approach serves as a probe to understand their preferences for a more engaging viewing experience. In a qualitative study with 32 fans, we found users craft the commentary into a relational tool, tailoring its social presence and emotional tone to maintain engagement. They created proxy voices for their own emotions and curated balanced conversational panels. Our work contributes an understanding of lean-back viewers, introduces a user-directed paradigm for personalized media, and provides design principles for creating engaging, low-effort experiences that support control over social presence, emotional tone, and cognitive load.
Collegiate student-athletes train and compete in a dense data ecology where information about their bodies and performances circulates among coaches, staff, and fans. To understand how student-athletes themselves engage with this data, we conducted interviews with 20 student-athletes, identifying four modes of engagement: 1) performance-directive, executing training and targeting improvement; 2) reflective-monitoring, assessing the body’s reaction to training and daily load; 3) coach-mediated, receiving insights through staff expertise; and 4) selective-disengagement, intentionally stepping back to protect confidence or avoid overload. These findings fill a gap left open by three related areas of research: SportsHCI, collegiate athletics, and personal data engagement. Each mode entails reasons, practices, and trade-offs. Student-athletes draw on different combinations of these modes as they respond to training demands, coaching oversight, and their own well-being. Our findings highlight how an evolving data ecology creates opportunities and pressures, requiring student-athletes to balance performance with protecting their state of mind.
Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that BadminSense achieves a stroke classification accuracy of 91.43\%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9\%. A real-world usability study further demonstrates BadminSense’s potential to provide reliable and meaningful support for daily badminton practice.
RageSense introduces a novel system for detecting and regulating player frustration during mobile gaming. Instead of relying on coarse emotion labels, RageSense estimates users’ valence and arousal levels in real time using near-ultrasonic acoustic sensing. By analyzing facial muscle movements via built-in smartphone speakers and microphones, our approach enables emotion sensing without requiring cameras or wearables, constituting a more unobtrusive, environment-resilient, and privacy-friendly approach than traditional emotion recognition. To transform detection into action, we integrate a large language model (LLM) that generates empathetic, context-aware interventions based on gameplay screenshots, behavioral signals, and emotional trajectories. These interventions are delivered in real time, tailored to the user’s emotional state, and designed to mitigate rage while enhancing player well-being. In a 53-participant field study, our system improved emotional state immediately after triggers and was preferred over random or template-based messages. To our knowledge, this is the first demonstration of near-ultrasonic, on-phone valence-arousal regression during mobile gameplay that directly drives real-time, context-aware interventions.
Encouraging home-based physical activity (PA) is increasingly important for public health. While smartphones and smartwatches are widely adopted, passive monitoring fails to effectively promote home exercise, providing basic metrics without meaningful feedback or user-preference alignment. Recent technological advances enable interactive, tangible devices designed for home PA interventions. We systematically reviewed 21 studies published since 2015 describing functional devices with physical components designed for stand-alone home use to analyse device categories, modalities, feedback mechanisms, and behaviour change techniques (BCTs). Our findings show a dominance of embedded systems that support strength and balance activities, visual-heavy feedback, limited layering of triggers, and a limited use of BCTs directly linked to habit formation. We offer design directions for tangible devices that support PA in home environments and promote durable behaviour change through emphasising stationary aerobic micro routines, layering prompts embedded in objects, and providing a mix of real-time and delayed feedback.
In sports training, individualized skill assessment and feedback are essential for athletes to master complex movements and enhance performance. Existing approaches for generating coaching comments primarily rely on externally captured pose information, which limits their applicability in outdoor sports such as skiing that involve large-scale movement. To address this challenge, we propose a method for presenting athletes' postures and generating coaching feedback solely based on foot pressure and IMU data collected from insole sensors. In our approach, a large language model directly interprets foot pressure signals to provide actionable coaching, thereby supporting independent practice. Through model evaluation and user studies, we demonstrate that the proposed method generates expert-level feedback and outperforms pose-based approaches. Furthermore, the user study shows that the feedback helps athletes identify body parts requiring correction and enhances their motivation for training.