用脚踝传感器+遗传算法，精准预测截肢者过障碍时的关节角度。
Feature Matching-Based Gait Phase Prediction for Obstacle Crossing Control of Powered Transfemoral Prosthesis
- 通过遗传算法优化神经网络结构，提升关节角度预测精度。
- 噪声标准差低于1时，步态阶段识别准确率达100%，采样率150Hz。
- 适合需要复杂地形适应的智能假肢研发与临床应用。
对于配备动力式股骨假肢的截肢者而言，跨越障碍物或在复杂地形中行走仍具挑战性。本研究利用健侧脚踝处的惯性传感器，指导过障动作。通过遗传算法优化神经网络结构，以预测大腿和膝关节所需角度。基于步态进展预测算法，确定假肢膝关节电机的驱动角度索引，最终确定所需的股四头肌和膝关节角度及步态进程。结果显示，当大腿角度数据中高斯噪声的标准差小于1时，该方法能有效消除噪声干扰，在150 Hz采样率下实现步态阶段估计100%准确率，大腿角度预测误差为8.71%，膝关节角度预测误差为6.78%。这些结果表明该方法能精确预测步态进程与关节角度，对动力式股骨假肢的障碍穿越具有显著实用价值。
原文摘要 · Abstract (English)
For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100\% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71\% and knee angle prediction error being 6.78\%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.
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