Open AccessPedestrian inertial navigation based on CNN-SVM gait recognition algorithm
Author(s) -
Xiaomeng Wu,
Lei Zhao,
Siyu Guo,
Lintong Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1903/1/012043
Subject(s) - inertial measurement unit , computer science , artificial intelligence , gait , computer vision , kalman filter , support vector machine , algorithm , inertial navigation system , zero crossing , pedestrian , inertial frame of reference , engineering , transport engineering , physiology , physics , quantum mechanics , voltage , electrical engineering , biology
Pedestrian inertial navigation technology based on inertial measurement unit (IMU) has been widely used in indoor and outdoor applications in recent years. But the IMU has a relatively low measurement accuracy that leads to error accumulation. Zero speed update algorithms (ZUPT) are often used to suppress the accumulation of errors. The key to the zero-speed update algorithm is to accurately find the stance phase in the pedestrian gait cycle. In this paper, an adaptive zero-speed detection algorithm based on CNN-SVM gait recognition is proposed for pedestrian positioning. First, the CNN-SVM algorithm is used to distinguish six gaits and find the optimal detection threshold according to different gaits. At the same time, it is proposed to use the zero-angle velocity update algorithm (ZARU) to correct the angle error, and to improve the accuracy of positioning by combining the information of zero-speed update and zero-angle velocity update through Kalman filter. Finally, the validity of the proposed algorithm is verified by experiments.