Department of Materials Science and Engineering, Soongsil University, Seoul 156-743, Republic of Korea.
Department of Smart Wearable Engineering, Soongsil University, Seoul 156-743, Republic of Korea.
Sensors (Basel). 2023 Jul 7;23(13):6223. doi: 10.3390/s23136223.
In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness exercises, utilizing a decision tree as the first stage and a one-dimension convolutional neural network as the second stage. The data acquisition was carried out by five participants performing exercises while wearing an inertial measurement unit sensor attached to a wristband on their wrists. However, only data acquired along the -axis of the IMU accelerator was used as input to train and test the proposed model, to simplify the model and optimize the training time while still achieving good performance. To examine the efficiency of the proposed method, the authors compared the performance of the cascaded model and the conventional 1D-CNN model. The obtained results showed an overall improvement in the accuracy of exercise classification by the proposed model, which was approximately 92%, compared to 82.4% for the 1D-CNN model. In addition, the authors suggested and evaluated two methods to optimize the clustering outcome of the first stage in the cascaded model. This research demonstrates that the proposed model, with advantages in terms of training time and computational cost, is able to classify fitness workouts with high performance. Therefore, with further development, it can be applied in various real-time HAR applications.
近年来，人类活动识别 (HAR) 引起了体育和健身行业研究人员的极大兴趣。在这项研究中，作者提出了一种级联方法，包括两个分类阶段，用于对健身运动进行分类，使用决策树作为第一阶段，一维卷积神经网络作为第二阶段。数据采集是由五名参与者在佩戴手腕上的惯性测量单元传感器的情况下进行运动时完成的。然而，仅使用 IMU 加速器的 - 轴采集的数据作为输入来训练和测试所提出的模型，以简化模型并优化训练时间，同时仍能获得良好的性能。为了检验所提出方法的效率，作者比较了级联模型和传统的 1D-CNN 模型的性能。所得结果表明，所提出的模型在运动分类的准确性方面有了整体提高，约为 92%，而 1D-CNN 模型的准确性为 82.4%。此外，作者提出并评估了两种方法来优化级联模型中第一阶段的聚类结果。这项研究表明，所提出的模型具有训练时间和计算成本方面的优势，能够以高性能对健身运动进行分类。因此，随着进一步的发展，它可以应用于各种实时 HAR 应用中。
