nature scientific data

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

Minwoo Seong, Gwangbin Kim, Dohyeon Yeo, Yumin Kang, Heesan Yang, Joseph DelPreto, Wojciech Matusik, Daniela Rus & SeungJun Kim

Gwangju Institute of Science and Technology, School of Integrated Technology, Gwangju, South Korea


badminton, wearable, real-time-feedback

badminton, wearable, real-time-feedback


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.

Moticon's Schlussfolgerungen

This study addresses the significant potential of multimodal wearable sensors and AI in improving badminton training, focusing on the forehand clear and backhand drive strokes. Data collection involved inertial measurement units (IMUs), eye trackers, electromyography (EMG) sensors, and Moticon sensor insoles to monitor foot pressure. Based on the collected sensor data, the research evaluates player movements and physiological responses. By recording over 150 stroke data points per type from players of varying skill levels, the study produced the MultiSenseBadminton dataset including detailed annotations such as stroke type, skill level, hitting point, and sound quality. Preliminary machine learning models used for validation showed promising results for classifying strokes and skill levels, demonstrating the dataset’s utility for developing training programs and performance analysis tools. This work underscores the potential of sensor-based analysis in sports, offering comprehensive feedback for coaches and players alike.

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