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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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to propose a new smooth online near time-optimal trajectory planning approach to reduce the consuming time compared to the conventional dynamics trajectory planning methods. Design/methodology/approach -In the proposed method, the robot path is expressed by a scalar path coordinate. The joints torque boundary and speed boundary are transformed into the plane, which can generate the limitation curves of pseudo-velocity. The maximum pseudo-velocity curve that meets the limits of torque and speed is built up through the feature points and control points in the plane by using cubic polynomial fitting method. Control points used for cubic polynomial construction are optimized by the Golden-Section method. Findings -The proposed method can avoid Range's phenomenon and also guarantee the continuity of torque. Practical implications -The algorithm designed in this paper is used for the controller of a new industrial robot which will be equipped for the welding automatic lines of Chery Automobile Co. Ltd. Originality/value -Compared to the five-order polynomial trajectory optimization method proposed by Macfarlane and Croft, the approach described in this paper can effectively take advantage of joints maximum speed, and the calculation time of this method is shorter than conventional dynamics methods.

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