Gao Jingwei, Ma Chao, Su Hong, Wang Shaohong, Xu Xiaoli, Yao Jie
Key Laboratory of Modern Measurement and Control Technology, Ministry of Education Beijing Information Science and Technology University, Beijing 100192, P. R. China.
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):103-111. doi: 10.7507/1001-5515.202106072.
Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 , and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 . The value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.
针对人体下肢异步过程中的个体差异以及行走过程中步幅的随机变化问题，本文提出了一种利用运动姿态信号进行步态识别与预测的方法。该研究采用基于免疫粒子群优化（IPSO）的优化门控循环单元（GRU）网络算法，建立了一个以人体姿态变化数据为输入，下一阶段的姿态变化数据和准确率为输出的网络模型，以实现人体姿态变化的预测。本文首先清晰地概述了IPSO对GRU算法的优化过程。它收集了多个受试者进行平地行走、蹲坐以及坐立位腿部屈伸运动时的人体姿态变化数据。然后，通过对IPSO优化的递归神经网络（RNN）、长短期记忆（LSTM）网络、GRU网络分类与预测的对比分析，验证了所构建模型的有效性。测试结果表明，优化后的算法能够更好地预测人体姿态的变化。其中，平地行走和蹲坐的均方根误差（RMSE）可达10 的精度，坐立位腿部屈伸的RMSE可达10 的精度。各种动作的 值可达0.966以上。上述研究结果表明，优化后的算法可应用于康复治疗中实现人体步态运动评估和步态趋势预测，以及在假肢和下肢康复设备的设计中，为未来提高患者肢体功能、活动水平和生活独立能力的研究提供参考。
