School of Physical Education, Sanya University, Sanya 572000, China.
School of Management, Sanya University, Sanya 572000, China.
Comput Intell Neurosci. 2022 Jul 14;2022:3410153. doi: 10.1155/2022/3410153. eCollection 2022.
In recent years, with the rapid development of a new generation of artificial intelligence technology, how to deeply apply artificial intelligence technology to physical education and break through the limitations of time-space scenarios and knowledge transfer methods in traditional models has become a key issue in intelligent physical education in the era of artificial intelligence. In order to realize the online monitoring of wearable devices with artificial intelligence in sports and overcome the problem of low recognition accuracy of electrocardiogram, blood oxygen, and respiratory signals in many cases, this paper proposes a combination of variational modal decomposition based on the maximum envelope kurtosis method. Long-short-term neural network (VMD-LSTM) monitoring method for wearable sports equipment. Through experimental analysis and verification, the current signal of the VMD model shows a trend of fluctuating from large to stable and then to large with motion, while the training accuracy of LSTM after the 150th iteration is 94.09%, which shows that the coupling model VMD LSTM can better predict the direction of sports artificial intelligence. In addition, although the training time of the BP neural network is shorter than that of the LSTM model, there is a large gap between the recognition effect and the LSTM, and there are also large differences between different neural network structures. This shows that the VMD-LSTM model has broad application prospects in such models.
近年来，随着新一代人工智能技术的快速发展，如何将人工智能技术深度应用于体育教学中，突破传统模式在时间-空间场景和知识传递方式上的局限，成为人工智能时代智能体育教学的关键问题。为了实现人工智能对可穿戴设备的在线监测，克服心电、血氧、呼吸信号在许多情况下识别精度低的问题，本文提出了一种基于最大包络峭度法的变分模态分解与长短时记忆网络(VMD-LSTM)相结合的可穿戴运动设备监测方法。通过实验分析和验证，VMD 模型的当前信号随着运动呈现出由大到小再到大的波动趋势，而 LSTM 在 150 次迭代后的训练精度为 94.09%，这表明耦合模型 VMD-LSTM 可以更好地预测运动人工智能的方向。此外，虽然 BP 神经网络的训练时间短于 LSTM 模型，但识别效果与 LSTM 相差较大，不同神经网络结构之间也存在较大差异。这表明 VMD-LSTM 模型在这类模型中具有广阔的应用前景。
