ArticleScientific data2024
MultiSenseBadminton: Wearable Sensor-Based Biomechanical Dataset for Evaluation of Badminton Performance.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Integrating Eye Tracking in Acoustic Research: Methods for Sound Localization, Event Detection, Multimodal Sensing, and Perceptual Analysis.Sensors (Basel, Switzerland) · 2026Review
- EdgeFuser: A tightly coupled adaptive framework for real-time athlete group analytics.iScience · 2026Article
- Gaze behaviors during forehand clear and backhand driving in badminton: a comparison between beginner, intermediate and expert players.BMC psychology · 2026Article
- A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors.Scientific data · 2026Article
- Fine reconstruction of badminton swing dynamic trajectory assisted by event camera.Scientific reports · 2026Article
- YOLC with dynamic sparse attention for high-speed small target detection in wearable sports images.Scientific reports · 2026Article
- Evaluating the sports performance of badminton players based on grip strength of the real hitting scenario.Scientific reports · 2026Article
- Wearable sensing for badminton stroke recognition with one-dimensional convolutional neural network.Scientific reports · 2025Article
- Wearable Ultrasound Devices for Therapeutic Applications.Nano-micro letters · 2025Review
- Hand Motion Catalog of Human Center-Out Transport Trajectories Measured Redundantly in 3D Task-Space.Scientific data · 2025Article
- DIODEM - A Diverse Inertial and Optical Dataset of kinEmatic chain Motion.Scientific data · 2025Article
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
- Materials, Structure, and Interface of Stretchable Interconnects for Wearable Bioelectronics.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- The analysis of motion recognition model for badminton player movements using machine learning.Scientific reports · 2025Article
- 3D-Printed Insole for Measuring Ground Reaction Force and Center of Pressure During Walking.Sensors (Basel, Switzerland) · 2025Article
- TTSwing: a Dataset for Table Tennis Swing and Racket Kinematics Analysis.Scientific data · 2025Article
- Multi-Planar Cervical Motion Dataset: IMU Measurements and Goniometer.Scientific data · 2025Article
- Exploring the relationship between motor visual proficiency and performance metrics in elite skeet shooters: An in-depth analysis.PloS one · 2025Article
- Article
- Expert-Novice Level Classification Using Graph Convolutional Network Introducing Confidence-Aware Node-Level Attention Mechanism.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.