Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel). 2020 Mar 15;20(6):1638. doi: 10.3390/s20061638.
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.
球拍类运动对人类健康有益。因此，需要一种可靠的检测设备，能够有效地区分具有相似子特征的运动。在本文中，提出了一种球拍类运动识别腕带系统和一种多层混合聚类模型，以实现可靠的活动识别和计数。此外，蓝牙 mesh 网络允许手机和腕带之间进行通信，并建立多个设备之间的连接。这使用户可以通过手机跟踪他们的运动，并与其他运动员和裁判员共享信息。考虑到分类算法的复杂性和测量系统的易用性，改进后的多层混合聚类模型应用三级 K-means 聚类来优化特征提取和分割，然后使用基于密度的带有噪声的空间聚类应用 (DBSCAN) 算法来确定不同运动的特征中心。该模型可以识别未标记和嘈杂的数据，而无需数据校准，适用于智能手表识别多种球拍运动。所提出的系统在实际实验中表现出更好的识别结果，并得到验证。
