基于神经网络的可穿戴动作识别研究

Research on Wearable Action Recognition Based on Neural Networks

随着可穿戴设备技术的快速发展,提高人体动作识别准确性已成为关键科技挑战之一。本研究通过构建和优化神经网络模型,采用多层感知机和卷积神经网络,结合创新的数据预处理和增强技术,高效识别复杂动作。实验结果表明,相较于传统方法,改进后的模型显著提升识别精度,具有更好的实用性和可扩展性。

With the rapid development of wearable device technology,improving the accuracy of human motion recognition has become one of the key technological challenges.This study constructs and optimizes a neural network model,employing multilayer perceptrons and convolutional neural networks combined with innovative data preprocessing and enhancement techniques to efficiently recognize complex motions.Experimental results show that the improved model significantly enhances recognition accuracy compared to traditional methods and offers better practicality and scalability.