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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...

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Detalles Bibliográficos
Autores principales: Xia, Kun, Wang, Hanyu, Xu, Menghan, Li, Zheng, He, Sheng, Tang, Yusong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146345/
https://www.ncbi.nlm.nih.gov/pubmed/32183426
http://dx.doi.org/10.3390/s20061638
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author Xia, Kun
Wang, Hanyu
Xu, Menghan
Li, Zheng
He, Sheng
Tang, Yusong
author_facet Xia, Kun
Wang, Hanyu
Xu, Menghan
Li, Zheng
He, Sheng
Tang, Yusong
author_sort Xia, Kun
collection PubMed
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.
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spelling pubmed-71463452020-04-15 Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor Xia, Kun Wang, Hanyu Xu, Menghan Li, Zheng He, Sheng Tang, Yusong Sensors (Basel) Article 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. MDPI 2020-03-15 /pmc/articles/PMC7146345/ /pubmed/32183426 http://dx.doi.org/10.3390/s20061638 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xia, Kun
Wang, Hanyu
Xu, Menghan
Li, Zheng
He, Sheng
Tang, Yusong
Racquet Sports Recognition Using a Hybrid Clustering Model Learned from Integrated Wearable Sensor
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146345/
https://www.ncbi.nlm.nih.gov/pubmed/32183426
http://dx.doi.org/10.3390/s20061638
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