ArticleSensors (Basel, Switzerland)2021
Using Artificial Intelligence to Achieve Auxiliary Training of Table Tennis Based on Inertial Perception Data.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- TTSwing: a Dataset for Table Tennis Swing and Racket Kinematics Analysis.Scientific data · 2025Article
- Article
- Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.Sensors (Basel, Switzerland) · 2024Article
- Identifying the Inertial Properties of a Padel Racket: An Experimental Maneuverability Proposal.Sensors (Basel, Switzerland) · 2022Article
- Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments.Sensors (Basel, Switzerland) · 2022Article
- Application of Multiple Regression Analysis Model in Table Tennis Competition.Journal of environmental and public health · 2022Article
- Artificial Intelligence Technologies and Their Application for Reform and Development of Table Tennis Training in Complex Environments.Computational intelligence and neuroscience · 2022Article
- Application of Tactics in Technical and Tactical Analysis of Table Tennis Mixed Doubles Based on Artificial Intelligence Graph Theory Model.Journal of environmental and public health · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compared with optical sensors, wearable inertial sensors have many advantages such as low cost, small size, more comprehensive application range, no space restrictions and occlusion, better protection of user privacy, and more suitable for sports applications. This article aims to solve irregular actions that table tennis enthusiasts do not know in actual situations. We use wearable inertial sensors to obtain human table tennis action data of professional table tennis players and non-professional table tennis players, and extract the features from them. Finally, we propose a new method based on multi-dimensional feature fusion convolutional neural network and fine-grained evaluation of human table tennis actions. Realize ping-pong action recognition and evaluation, and then achieve the purpose of auxiliary training. The experimental results prove that our proposed multi-dimensional feature fusion convolutional neural network has an average recognition rate that is 0.17 and 0.16 higher than that of CNN and Inception-CNN on the nine-axis non-professional test set, which proves that we can better distinguish different human table tennis actions and have a more robust generalization performance. Therefore, on this basis, we have better realized the enthusiast of table tennis the purpose of the action for auxiliary training.
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Registered trials
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