Abstract
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.
Highlights
An automatic and smooth locomotion transition is important for many kinds of robots such as bipedal robots, lower extremity exoskeleton robots and prostheses [1,2,3]
We propose to use the support vector machine (SVM) algorithm optimized by particle swarm optimization (PSO) to identify five kinds of locomotion modes, i.e., level-ground walking, stair descent, stair ascent, ramp ascent, ramp descent, and transitions between them
The predictive locomotion mode labels are recorded by three different ways, i.e., SVM, PSO optimized SVM (PSO-SVM)
Summary
An automatic and smooth locomotion transition is important for many kinds of robots such as bipedal robots, lower extremity exoskeleton robots and prostheses [1,2,3]. It is necessary to know about the locomotion mode condition and preceding corresponding movements to complete tasks. An adaptive smooth locomotion transition can enhance safety and balance to bring about a more natural gait pattern. To achieve a smooth transition between different locomotion modes such as stair descent, stair ascent, ramp descent, ramp ascent, and level-ground walking, increasing efforts have been made during the last few decades. Approaches for locomotion mode identification can be classified according to the type of identification signals: classifiers using biological signals, e.g., EMG and other classifiers using kinesiological signals, e.g., interaction forces.
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