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首页> 外文期刊>Nature reviews Cancer >Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion
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Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

机译：基于多源特征参数融合的人体混合运动模式识别方法

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摘要

Aiming at the requirement of rapid recognition of the wearer's gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.
机译：旨在要求在智能骨骼控制过程中快速识别佩戴者的步态阶段，本文研究了基于多源特征参数的人体混合运动模式识别技术。我们获取有关人的下肢加速和Purantar的信息，分析了基于特征评估和神经网络的运动状态识别方法的这些参数与步态周期之间的关系。基于每次使用外骨骼的实际要求，确定了15种常见的步态模式。使用此，在多源下肢运动信息的时域，频域和能量特征提取上进行研究。基于距离的特征筛选方法用于提取最佳特征。最后，基于多层BP（反向传播）神经网络，建立了特征量和运动状态之间的非线性映射模型。实验结果表明，单次运动模式的识别精度可达98.28％，而混合运动模式中两组实验的识别准确度分别为92.7％和97.4％。验证了模型的可行性和有效性。

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