Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor
Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/6/1638 |
| _version_ | 1811301042051088384 |
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| author | Kun Xia Hanyu Wang Menghan Xu Zheng Li Sheng He Yusong Tang |
| author_facet | Kun Xia Hanyu Wang Menghan Xu Zheng Li Sheng He Yusong Tang |
| author_sort | Kun Xia |
| collection | DOAJ |
| description | Racquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments. |
| first_indexed | 2024-04-13T07:01:50Z |
| format | Article |
| id | doaj.art-1d8d2234923b45e980dc18919bbf0dbd |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:01:50Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1d8d2234923b45e980dc18919bbf0dbd2022-12-22T02:57:05ZengMDPI AGSensors1424-82202020-03-01206163810.3390/s20061638s20061638Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable SensorKun Xia0Hanyu Wang1Menghan Xu2Zheng Li3Sheng He4Yusong Tang5Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaRacquet sports can provide positive benefits for human healthcare. A reliable detection device that can effectively distinguish movement with similar sub-features is therefore needed. In this paper, a racquet sports recognition wristband system and a multilayer hybrid clustering model are proposed to achieve reliable activity recognition and perform number counting. Additionally, a Bluetooth mesh network enables communication between a phone and wristband, and sets-up the connection between multiple devices. This allows users to track their exercise through the phone and share information with other players and referees. Considering the complexity of the classification algorithm and the user-friendliness of the measurement system, the improved multi-layer hybrid clustering model applies three-level K-means clustering to optimize feature extraction and segmentation and then uses the density-based spatial clustering of applications with noise (DBSCAN) algorithm to determine the feature center of different movements. The model can identify unlabeled and noisy data without data calibration and is suitable for smartwatches to recognize multiple racquet sports. The proposed system shows better recognition results and is verified in practical experiments.https://www.mdpi.com/1424-8220/20/6/1638internet of things (iot)physical activity recognition (par)machine learning (ml)wearable sensors |
| spellingShingle | Kun Xia Hanyu Wang Menghan Xu Zheng Li Sheng He Yusong Tang Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor Sensors internet of things (iot) physical activity recognition (par) machine learning (ml) wearable sensors |
| title | Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor |
| title_full | Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor |
| title_fullStr | Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor |
| title_full_unstemmed | Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor |
| title_short | Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor |
| title_sort | racquet sports recognition using a hybrid clustering model learned from integrated wearable sensor |
| topic | internet of things (iot) physical activity recognition (par) machine learning (ml) wearable sensors |
| url | https://www.mdpi.com/1424-8220/20/6/1638 |
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