PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.
PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.
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DOI：
10.3390/s16091408
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发表时间：
2016-09-02
期刊：
影响因子：
--
通讯作者：
Dong W
中科院分区：
文献类型：
--
作者：
Long Y;Du ZJ;Wang WD;Zhao GY;Xu GQ;He L;Mao XW;Dong W
运动模式识别对于康复机器人外骨骼的控制至关重要。提出了一种基于粒子群优化算法的在线支持向量机方法，用于识别不同的运动模式，实现平稳、自动的运动过渡。采用粒子群优化算法对支持向量机的参数进行优化，以获得更好的整体性能。将集成在可穿戴鞋鞋垫中的足底压力传感器和安装在鞋和腿段小腿上的MEMS姿态航向参考系统（AHRS）测量的信号融合在一起作为支持向量机的输入信息。在选取200 ms（采样频率40 Hz）窗口的基础上，采用三层小波包分析（WPA）进行特征提取，然后利用核主成分分析（kPCA）对特征集进行降维，以降低SVM的计算量。由于信号来自两种类型的不同传感器，因此进行归一化以将输入缩放到[0，1]的区间内。采用五重交叉验证方法训练分类器，避免了分类器的过拟合。基于MATLAB离线得到的SVM模型，构造了一种在线SVM运动模式识别算法。实验结果表明，该算法在不同运动模式下的定位准确率为96.00% ± 2.45%。为提高识别精度，采用多数表决算法（MVA）进行后处理，识别精度优于98.35% ± 1.65%。该算法可以推广应用于机器人康复和辅助领域。
Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.
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