Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor

• Published in: Micromachines (Basel) 2023-05, Vol.14 (6), p.1170
• Main Authors: Li, Yong, Zeng, Guopei, Wang, Luping, Tan, Ke
• Format: Article
• Language: English
• Subjects: Accelerometers; Accuracy; Algorithms; Calibration; Dead reckoning; Dead reckoning (Navigation); Deep learning; Extended Kalman filter; inertial measurement unit (IMU); Kalman filter; Location-based systems; Methods; Microelectromechanical systems; Natural language processing; Neural networks; pedestrian dead reckoning; Pedestrians; Sensors; stride-length estimation; Transformer model; Velocity; Walking
• ISSN: 2072-666X; 2072-666X
• DOI: 10.3390/mi14061170

Pedestrian dead reckoning (PDR) is a self-contained positioning technology and has been a significant research topic in recent years. Pedestrian-stride-length estimation is the core part of the PDR system and directly affects the performance of the PDR. The current stride-length-estimation method is difficult to adapt to changes in pedestrian walking speed, which leads to a rapid increase in the error of the PDR. In this paper, a new deep-learning model based on long short-term memory (LSTM) and Transformer, LT-StrideNet, is proposed to estimate pedestrian-stride length. Next, a shank-mounted PDR framework is built based on the proposed stride-length-estimation method. In the PDR framework, the detection of pedestrian stride is achieved by peak detection with a dynamic threshold. An extended Kalman filter (EKF) model is adopted to fuse the gyroscope, accelerometer, and magnetometer. The experimental results show that the proposed stride-length-estimation method can effectively adapt to changes in pedestrian walking speed, and our PDR framework has excellent positioning performance.