AI Analysis from OwnYourAI
Wearable Sensing for Badminton Stroke Recognition with 1D-CNN
Authors: Guohan Jin & Xin Li
Published in Scientific Reports, (2025) 15:41236 | DOI: 10.1038/s41598-025-25158-2
Executive Impact Summary
This study introduces a novel wearable sensing network with two IMUs and a 1D-CNN to accurately recognize badminton stroke actions and trajectories. Designed for real-world sports training, this system offers a portable, efficient, and highly accurate solution for motion monitoring, significantly outperforming traditional machine learning methods. Its lightweight design and precise data synchronization are key to enhancing athlete performance and tactical development.
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Stroke Action Accuracy
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Trajectory Recognition Accuracy
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Multi-sensor Data Acquisition (Wrist & Racket IMUs)
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ESP32 Microcontroller Time Synchronization
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Data Normalization & Sliding Window Segmentation
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1D-CNN Feature Extraction (Conv Layers)
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Batch Normalization & ReLU Activation
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Max-Pooling & Fully Connected Layers
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Softmax Classifier
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Badminton Stroke/Trajectory Recognition
| Method | Sensor Configuration | Primary Task | Key Accuracy |
|---|---|---|---|
| This Study (1D-CNN) | Two IMUs & ESP32 | Stroke & Trajectory Recognition | 97.16% (Actions) / 86.07% (Trajectories) |
| Juyi Lin et al. (ML methods) | One IMU | Shot Recognition | 84% |
| Daniel Peralta et al. (CNN) | One IMU | Stroke Action & State Recognition | 93% |
| Tim Steels et al. (CNN/DNN) | One IMU (Wrist/Arm/Racket) | Shot & Activity Recognition | 98% (with Accel+Gyro) |
| Indrajeet Ghosh et al. (CNN/KNN) | Four IMUs | Shot Classification, Performance Predictor | 89% |
| Ben Van Herbruggen et al. (CNN/LSTM) | One IMU & UWB | Shot & Strategy Recognition | 90% (Shot) / 80% (Strategy) |
| Z. Wang et al. (HMM) | Four IMUs | Shot Classification | 96% |
Enhanced Athlete Training through AI-Powered Sensing
Challenge: Badminton coaches often struggle with precise, real-time feedback on stroke mechanics and trajectories due to limitations of traditional video analysis or single-sensor systems. Athletes face injury risks from incorrect technique and lack personalized tactical development.
Our AI Solution: We implemented a wearable sensing system featuring two IMUs (on the wrist and racket handle) synchronized by an ESP32 microcontroller, feeding data into a lightweight One-Dimensional Convolutional Neural Network (1D-CNN).
Impact & Results:
- Superior Accuracy: Achieved 97.16% accuracy for six core stroke actions and 86.07% for fifteen distinct trajectories, significantly outperforming conventional machine learning methods.
- Comprehensive Motion Capture: The dual-IMU setup provides a more complete biomechanical picture without the discomfort of excessive sensors, enhancing data quality.
- Real-time Insights for Coaches: Enables precise identification of technical errors and informs targeted training adjustments, optimizing athlete development and reducing injury risk.
- Cost-Effective & Scalable: The ESP32 and lightweight 1D-CNN offer a practical, portable, and commercially viable solution for sports academies and individual athletes.
- Foundation for Tactical AI: Accurate stroke and trajectory recognition lays the groundwork for future AI systems to develop personalized tactical strategies.
This AI-driven approach transforms badminton training, making it more data-informed, efficient, and personalized, directly contributing to improved athlete performance and skill enhancement.
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Estimated Annual Savings
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Your AI Implementation Roadmap
A strategic outline for integrating advanced wearable sensing and AI into your sports training or enterprise operations.
Phase 1: Sensor Deployment & Data Acquisition (1-2 Weeks)
Deploy IMU sensors on critical points (e.g., wrist and racket handle) and configure ESP32 for real-time, synchronized data capture. Collect baseline motion data from athletes across various stroke actions and trajectories to build a robust dataset.
Phase 2: Data Preprocessing & Model Training (3-4 Weeks)
Normalize and segment the collected sensor data to prepare it for machine learning. Train the 1D-CNN model using optimized parameters (cross-entropy loss, Adam optimizer) and validate its performance with stratified cross-validation for high accuracy.
Phase 3: Real-time Feedback & Coaching Integration (2-3 Weeks)
Integrate the trained AI model into a real-time monitoring system accessible to coaches and athletes. Develop an intuitive interface to visualize stroke actions and trajectories, providing immediate feedback for technique refinement during training sessions.
Phase 4: Performance Analytics & Tactical Development (Ongoing)
Continuously analyze athlete performance trends, identifying subtle improvements or persistent errors. Utilize AI-driven insights to inform personalized training plans and develop advanced tactical strategies, ensuring continuous skill enhancement and competitive advantage.
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