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The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver’s cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs.

A logogram is a type of writing system in which each character represents a word. Compared to segmental scripts where the alphabets reflect sounds, learning logograms are disengaging, since each character is not linked to its pronunciation. This paper presents Logogram VR, a virtual reality edutainment game that uses a treadmill and controllers to teach Hanja, which uses logograms. Hanja is a traditional Korean language writing system comprising over 8000 Chinese characters. The system leverages the logogram’s feature that each letter stands for each vocabulary item, as an embodied learning strategy. Specifically, it incorporates each character’s meaning into the VR learning environment, accompanied by gamified actions using a treadmill and VR controllers. We evaluated the system with 33 participants to test its overall usability, while determining the desirable playtime and number of characters for the further enhancement of it. We demonstrated and assessed the system with 125 visitors at an exhibition to disseminate it and verify the results with a wider population sample. The user studies revealed that the system provides a playful experience for learning Hanja without severe motion sickness. The differences in age groups showed that the embodiment approach utilizing meanings and actions in VR may be an effective logogram edutainment strategy, particularly among adolescents.

Signs, landmarks, and other urban elements should attract attention to or harmonize with the environment for successful landscape design. These elements also provide information during navigation—particularly for people with cognitive difficulties or those unfamiliar with the geographical area. Nevertheless, some urban components are less eye-catching than intended because they are created and positioned irrespective of their surroundings. While quantitative measures such as eye tracking have been introduced, they help the initial or final stage of the urban design process and they involve expensive experiments. We introduce machine-learning-predicted visual saliency as iterative feedback for pedestrian attention during urban element design. Our user study focused on wayfinding signs as part of urban design and revealed that providing saliency prediction promoted a more efficient and helpful design experience without compromising usability. The saliency-guided design practice also contributed to producing more eye-catching and aesthetically pleasing urban elements. The study demonstrated that visual saliency can lead to an improved urban design experience and outcome, resulting in more accessible cities for citizens, visitors, and people with cognitive impairments.

This study presents Portal Display, a screen-based telepresence system that mediates the interaction between two distinct spaces, each using a single display system. The system synchronizes the users’ viewpoint with their head position and orientation to provide stereoscopic vision through this single monitor. This research evaluates the impact of graphically rendered and video-streamed backgrounds and remote user representations on social telepresence, usability, and concentration during conversations and collaborative tasks. Our results indicate that the type of background has a negligible impact on these metrics. However, point cloud streaming of remote users significantly improves social telepresence, usability, and concentration compared with graphical avatars. This study implies that Portal Display can operate more efficiently by substituting the background with graphical rendering and focusing on higher-resolution 3D point cloud streaming for narrower regions for remote user representations. This configuration may be especially advantageous for applications where the remote user’s background is not essential to the task, potentially enhancing social telepresence.

This paper introduces a motor-driven Peltier device designed to deliver immediate thermal sensations within extended reality (XR) environments. The system incorporates eight motor-driven Peltier elements, facilitating swift transitions between warm and cool sensations by rotating preheated or cooled elements to opposite sides. A multi-layer structure, comprising aluminum and silicone layers, ensures user comfort and safety while maintaining optimal temperatures for thermal stimuli. Time-temperature characteristic analysis demonstrates the system's ability to provide warm and cool sensations efficiently, with a dual-sided lifetime of up to 206 seconds at a 2V input. Our system design is adaptable to various body parts and can be synchronized with corresponding visual stimuli to enhance the immersive sensation of virtual object interaction and information delivery.

This study proposes a framework for enhancing the stroke quality of badminton players by generating personalized motion guides, utilizing a multimodal wearable dataset. These guides are based on counterfactual algorithms and aim to reduce the performance gap between novice and expert players. Our approach provides joint-level guidance through visualizable data to assist players in improving their movements without requiring expert knowledge. The method was evaluated against a traditional algorithm using metrics to assess validity, proximity, and plausibility, including arithmetic measures and motion-specific evaluation metrics. Our evaluation demonstrates that the proposed framework can generate motions that maintain the essence of original movements while enhancing stroke quality, providing closer guidance than direct expert motion replication. The results highlight the potential of our approach for creating personalized sports motion guides by generating counterfactual motion guidance for arbitrary input motion samples of badminton strokes.