ArticleSensors (Basel, Switzerland)2020
Badminton Activity Recognition Using Accelerometer Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- From IMU Streams to Real-Time Decisions: Past-Only Next-Window Badminton Action Prediction.Sensors (Basel, Switzerland) · 2026Article
- Making Sense of Shoulder Exercise: Measuring the Accuracy of an Artificial Intelligence Model to Classify Shoulder Exercise via Wearable Sensors Among People With and Without Rotator Cuff Tendinopathy.European journal of sport science · 2026Article
- Deep learning for stress oriented human activity recognition.Frontiers in digital health · 2026Article
- Wearable sensing for badminton stroke recognition with one-dimensional convolutional neural network.Scientific reports · 2025Article
- Differences in backcourt forehand clear stroke between novice players and experienced badminton players: based on body segment acceleration data.BMC sports science, medicine & rehabilitation · 2025Article
- Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization.Heliyon · 2024Article
- Neural network and layer-wise relevance propagation reveal how ice hockey protective equipment restricts players' motion.PloS one · 2024Article
- Recognition of sports and daily activities through deep learning and convolutional block attention.PeerJ. Computer science · 2024Article
- Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition.Scientific reports · 2023Article
- Experimental and Computer Simulation Studies on Badminton Racquet Strings.Sensors (Basel, Switzerland) · 2023Article
- Sensor-Based Activity Recognition Using Frequency Band Enhancement Filters and Model Ensembles.Sensors (Basel, Switzerland) · 2023Article
- Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court.Frontiers in bioengineering and biotechnology · 2023Article
- Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics.Sensors (Basel, Switzerland) · 2021Article
- Estimating Player Positions from Padel High-Angle Videos: Accuracy Comparison of Recent Computer Vision Methods.Sensors (Basel, Switzerland) · 2021Article
- CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit.Sensors (Basel, Switzerland) · 2021Article
- Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning.Frontiers in neurorobotics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A thorough analysis of sports is becoming increasingly important during the training process of badminton players at both the recreational and professional level. Nowadays, game situations are usually filmed and reviewed afterwards in order to analyze the game situation, but these video set-ups tend to be difficult to analyze, expensive, and intrusive to set up. In contrast, we classified badminton movements using off-the-shelf accelerometer and gyroscope data. To this end, we organized a data capturing campaign and designed a novel neural network using different frame sizes as input. This paper shows that with only accelerometer data, our novel convolutional neural network is able to distinguish nine activities with 86% precision when using a sampling frequency of 50 Hz. Adding the gyroscope data causes an increase of up to 99% precision, as compared to, respectively, 79% and 88% when using a traditional convolutional neural network. In addition, our paper analyses the impact of different sensor placement options and discusses the impact of different sampling frequenciess of the sensors. As such, our approach provides a low cost solution that is easy to use and can collect useful information for the analysis of a badminton game.
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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.