Dual-sided Peltier Elements for Rapid Thermal Feedback in Wearables

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.

Counterfactual Explanation-Based Badminton Motion Guidance Generation Using Wearable Sensors

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.

Team Badminseok at IJCAI CoachAI Badminton Challenge 2023: Multi-Layer Multi-Input Transformer Network (MuLMINet) with Weighted Loss

The increasing use of artificial intelligence (AI) technology in turn-based sports, such as badminton, has sparked significant interest in evaluating strategies through the analysis of match video data. Predicting future shots based on past ones plays a vital role in coaching and strategic planning. In this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet) that leverages professional badminton player match data to accurately predict future shot types and area coordinates. Our approach resulted in achieving the runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2. To facilitate further research, we have made our code publicly accessible online, contributing to the broader research community's knowledge and advancements in the field of AI-assisted sports analysis.