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LightGBM is a fast distributed, high-performance gradient boosting framework based on decision tree algorithm, which can be used for sorting, classification, regression and many other machine learning tasks. In order to realize the classification of the terrain environment where the robot is located, this paper uses the gradient boosting tree LightGBM to identify and classify the IMU signals of the robot feet. First of all, the quaternion Euler transform and segmentation are performed on the signal. Secondly, the different period segments of the signal are analyzed and feature fused from the time domain and frequency domain respectively. Finally, the decision tree integrated hybrid classifier with the best parameters, namely LightGBM, is employed to classify the robot on the ground. The experimental results show that the accuracy of this method for the recognition of robot IMU signals on different surfaces reaches 90.7%.

With the continuous development of sensor technology, computer technology, artificial intelligence and other advanced technologies, there are more and more researches on trajectory tracking and detection technology, which have been widely used in urban planning, traffic management, safety control and other aspects. Trajectory tracking and detection has always been the focus of research by experts and scholars. The purpose of this study is to track and detect the spatial trajectory of the infected person under the current new crown virus epidemic, to timely and accurately understand the itinerary of the new crown virus infected person and to find out all the suspected contacts that the infected person may come into contact with. The current epidemic situation in various countries has made a certain contribution.