Autonomous positioning optimization method of flexible exoskeleton robot based on gait classification by SVM

Aiming at the problem that the stability of virtual inertial measurement unit (VIMU) of the flexible exoskeleton decreases in complex gait, an autonomous positioning optimization method based on support vector machine (SVM) gait classification was studied. The SVM algorithm model was used to identify multiple conventional gait types of the flexible exoskeleton, and different convolution-long-short-term memory (VGG-LSTM) mixed neural network models were constructed according to the gait types. By judging the failure of the actual inertial measurement unit (IMU), the VIMU was used to form a robust and autonomous positioning method with system reconfiguration capabilities. The research result shows that, the gait classification method based on SVM in complex gaits can reduce the complexity of the VGG-LSTM neural network model while ensuring the accuracy of VIMU. When the IMU at the end of the robot limb fails in conventional gaits, the autonomous positioning performance of the system after reconstruction is basically the same as that without failures. The positioning error of the reconstructed navigation system is less than 2.5% of the travel distance. © 2020, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.