Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification
Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It makes a significant contribution to sport analysis, b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2422 |
| _version_ | 1797614412173934592 |
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| author | Maria Skublewska-Paszkowska Pawel Powroznik |
| author_facet | Maria Skublewska-Paszkowska Pawel Powroznik |
| author_sort | Maria Skublewska-Paszkowska |
| collection | DOAJ |
| description | Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It makes a significant contribution to sport analysis, by indicating players’ performance level and training evaluation. The main purpose of this study is to investigate how the content of three-dimensional data influences on classification accuracy of four basic tennis strokes: forehand, backhand, volley forehand, and volley backhand. An entire player’s silhouette and its combination with a tennis racket were taken into consideration as input to the classifier. Three-dimensional data were recorded using the motion capture system (Vicon Oxford, UK). The Plug-in Gait model consisting of 39 retro-reflective markers was used for the player’s body acquisition. A seven-marker model was created for tennis racket capturing. The racket is represented in the form of a rigid body; therefore, all points associated with it changed their coordinates simultaneously. The Attention Temporal Graph Convolutional Network was applied for these sophisticated data. The highest accuracy, up to 93%, was achieved for the data of the whole player’s silhouette together with a tennis racket. The obtained results indicated that for dynamic movements, such as tennis strokes, it is necessary to analyze the position of the whole body of the player as well as the racket position. |
| first_indexed | 2024-03-11T07:12:04Z |
| format | Article |
| id | doaj.art-fb5e913acfba4b16a91231bbd6e13fd2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:12:04Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fb5e913acfba4b16a91231bbd6e13fd22023-11-17T08:34:32ZengMDPI AGSensors1424-82202023-02-01235242210.3390/s23052422Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes ClassificationMaria Skublewska-Paszkowska0Pawel Powroznik1Department of Computer Science, Lublin University of Technology, 20-618 Lublin, PolandDepartment of Computer Science, Lublin University of Technology, 20-618 Lublin, PolandHuman Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It makes a significant contribution to sport analysis, by indicating players’ performance level and training evaluation. The main purpose of this study is to investigate how the content of three-dimensional data influences on classification accuracy of four basic tennis strokes: forehand, backhand, volley forehand, and volley backhand. An entire player’s silhouette and its combination with a tennis racket were taken into consideration as input to the classifier. Three-dimensional data were recorded using the motion capture system (Vicon Oxford, UK). The Plug-in Gait model consisting of 39 retro-reflective markers was used for the player’s body acquisition. A seven-marker model was created for tennis racket capturing. The racket is represented in the form of a rigid body; therefore, all points associated with it changed their coordinates simultaneously. The Attention Temporal Graph Convolutional Network was applied for these sophisticated data. The highest accuracy, up to 93%, was achieved for the data of the whole player’s silhouette together with a tennis racket. The obtained results indicated that for dynamic movements, such as tennis strokes, it is necessary to analyze the position of the whole body of the player as well as the racket position.https://www.mdpi.com/1424-8220/23/5/2422sporttennis strokeshuman action recognitionA3T-GCNmotion capture |
| spellingShingle | Maria Skublewska-Paszkowska Pawel Powroznik Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification Sensors sport tennis strokes human action recognition A3T-GCN motion capture |
| title | Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification |
| title_full | Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification |
| title_fullStr | Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification |
| title_full_unstemmed | Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification |
| title_short | Temporal Pattern Attention for Multivariate Time Series of Tennis Strokes Classification |
| title_sort | temporal pattern attention for multivariate time series of tennis strokes classification |
| topic | sport tennis strokes human action recognition A3T-GCN motion capture |
| url | https://www.mdpi.com/1424-8220/23/5/2422 |
| work_keys_str_mv | AT mariaskublewskapaszkowska temporalpatternattentionformultivariatetimeseriesoftennisstrokesclassification AT pawelpowroznik temporalpatternattentionformultivariatetimeseriesoftennisstrokesclassification |