Basic investigation of a method of assessing tennis forehand stroke quality using convolutional neural networks and inertia sensors

研究成果: ジャーナルへの寄稿会議記事査読

抄録

To improve the movement performance of athletes, it is desirable to have an environment in which the movement quality ca be accurately assessed. For that reason, in this study, we construct a model to assess quality using inertia sensors (acceleration/angular velocity signal measuring devices) and convolutional neural networks (CNN). The forehand stroke in tennis was selected as the subject movement for quality assessment. We took data from one player who had previous experience in playing tennis and two who had not, and labeled data from the experienced player (30 samples) as high quality, and labeled those from the inexperienced players (30 samples) as low quality. We divided the total of 60 samples into 40 samples of training data and 20 samples of test data, constructed the CNN based on the training data, and tested the estimation accuracy using the test data. The result displayed 95.0% of correct answer rate, demonstrating high estimation accuracy. However, due to the low number of test subjects, it shall be necessary to increase this moving forward, to get more reliable results.

本文言語英語
論文番号120077-1
ジャーナルAIP Conference Proceedings
2872
1
DOI
出版ステータス出版済み - 2023
イベント11th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2022 - Virtual, Online, セルビア
継続期間: 5 9月 20228 9月 2022

フィンガープリント

「Basic investigation of a method of assessing tennis forehand stroke quality using convolutional neural networks and inertia sensors」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル