ArticleSensors (Basel, Switzerland)2021
Classification of Tennis Shots with a Neural Network Approach.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effects of Different Marker Set Configurations on Tennis Stroke Recognition Performance: A Three-Dimensional Kinematic Study Based on MiniRocket.Sensors (Basel, Switzerland) · 2026Article
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
- External training load and rating of perceived exertion comparison between different playing styles and winning vs. losing matches in elite tennis.Frontiers in sports and active living · 2025Article
- A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach.PloS one · 2024Article
- Transforming tennis with artificial intelligence: a bibliometric review.Frontiers in sports and active living · 2024Review
- Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball?Sensors (Basel, Switzerland) · 2023Article
- Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification.Sensors (Basel, Switzerland) · 2023Article
- Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions.Sensors (Basel, Switzerland) · 2022Article
- Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13-70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F
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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.