In order to solve problems in traditional gait detection algorithms, such as simplification of information, low accuracy, being easy to fall into local optimum, a gait detection algorithm for exoskeleton robot called Support Vector Machine optimized by Improved Whale Optimization Algorithm (IWOA-SVM) was proposed. The selection, crossover and mutation of Genetic Algorithm (GA) were introduced to Whale Optimization Algorithm (WOA) to optimize the penalty factor and kernel parameters of Support Vector Machine (SVM), and then classification models were established by SVM with optimized parameters, expanding the search scope and reduce the probability of falling into local optimum. Firstly, the gait data was collected by using hybrid sensing technology. With the combination of plantar pressure sensor, knee joint and hip joint angle sensors, motion data of exoskeleton robot was acquired as the input of gait detection system. Then, the gait phases were divided and tagged according to the threshold method. Finally, the plantar pressure signal was integrated with hip and knee angle signals as input, and gait detection was realized by IWOA-SVM algorithm. Through the simulation experiments of six standard test functions, the results demonstrate that Improved Whale Optimization Algorithm (IWOA) is superior to GA, Particle Swarm Optimization (PSO) algorithm and WOA in robustness, optimization accuracy and convergence speed. By analyzing the gait detection results of different wearers, the accuracy is up to 98.8%, so the feasibility and practicability of the proposed algorithm in the new generation exoskeleton robot are verified. Compared with Support Vector Machine optimized by Genetic Algorithm (GA-SVM), Support Vector Machine optimized by Particle Swarm Optimization (PSO-SVM) and Support Vector Machine optimized by Whale Optimization Algorithm (WOA-SVM), the proposed algorithm has the gait detection accuracy improved by 5.33%, 2.70% and 1.44% respectively. The experimental results show that the proposed algorithm can effectively detect the gait of exoskeleton robot and realize the precise control and stable walking of exoskeleton robot.

Aiming at the problems of mismatching and too large matching distance because of curves scaling, shifting, rotation and non-uniform sampling in the process of online signature verification, a curve segment similarity matching method was proposed. In the progress of online signature verification, two curves were partitioned into segments and matched coarsely at first. A dynamic programming algorithm based on cumulative difference matrix of windows was introduced to get the matching relationship. Then, the similarity distance for each matching pair and weighted sum of all the matching pairs were calculated, and the calculating method is to fit each curve of matching pairs, carry out the similarity transformation within a certain range, and resample the curves to get the Euclidean distance. Finally, the average of the similarity distance between test signature and all template signatures was used as the authentication distance, which was compared with the training threshold to judge the authenticity. The method was validated on the open databases SUSIG Visual and SUSIG Blind respectively with 3.56% and 2.44% Equal Error Rate (EER) when using personalized threshold, and the EER was reduced by about 14.4% on Blind data set compared with the traditional Dynamic Time Wraping (DTW) method. The experimental results show that the proposed method has certain advantages in skilled forgery signature and random forgery signature verification.

Acceleration data in 3 axes during a signature process can be collected to authenticate users. Because of complex structures of Chinese signature, the process of signing in the air is hard to be forged, but it also increases differences between signatures performed by the same user which brings more difficulties in authentication. Classical verification methods applied to 2-D signature or hand gesture cannot solve this problem. In order to improve the performance of in-air Chinese signature verification, the classical Global Sequence Alignment (GSA) algorithm was improved, and the interpolation was applied to matching sequences. Different from classical GSA algorithm which uses matching score to measure similarity between sequences, two distance indexes, Euclidean distance and absolute value distance, were introduced to calculate the differences between sequences after interpolation. Experimental results show that both of the two improved GSA algorithms can improve the accuracy of authentication, the Equal Error Rate (EER) of them are decreased by 37.6% and 52.6% respectively compared with the classical method.