Fusion of Inertial/Magnetic Sensor Measurements and Map Information for Pedestrian Tracking

Abstract

Abstract:The wearable inertial/magnetic sensor based human motion analysis plays an important role in many biomedical applications, such as physical therapy, gait analysis and rehabilitation. One of the main challenges for the lower body bio-motion analysis is how to reliably provide position estimations of human subject during walking. In this paper, we propose a particle filter based human position estimation method using a foot-mounted inertial and magnetic sensor module, which not only uses the traditional zero velocity update (ZUPT), but also applies map information to further correct the acceleration double integration drift and thus improve estimation accuracy. In the proposed method, a simple stance phase detector is designed to identify the stance phase of a gait cycle based on gyroscope measurements. For the non-stance phase during a gait cycle, an acceleration control variable derived from ZUPT information is introduced in the process model, while vector map information is taken as binary pseudo-measurements to further enhance position estimation accuracy and reduce uncertainty of walking trajectories. A particle filter is then designed to fuse ZUPT information and binary pseudo-measurements together. The proposed human position estimation method has been evaluated with closed-loop walking experiments in indoor and outdoor environments. Results of comparison study have illustrated the effectiveness of the proposed method for application scenarios with useful map information.

Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

“…The basic principle is to measure the triaxial acceleration and angular velocity of the motion by these sensors, and obtain the trajectory of monitoring points through integral operations [15][16][17][18]. However, inertial sensors may inevitably throw off errors that accumulate over time [19][20][21]. The accumulative and drift error is the biggest challenge faced by WS-based HMT systems.…”

“…Some achievements on IMU/TOA fusion have been reported in many studies (e.g., [20,21,23,25]) However, state-of-the-art studies, (e.g., [23,27]), mainly face the following two drawbacks:…”

Research on the Heading Calibration for Foot-Mounted Inertial Pedestrian-Positioning System Based on Accelerometer Attitude

“…Some papers introduce external sensors, such as WIFI, a iBeacon ultra-wideband, and so on [26]. Then, the standard information obtained from the external sensors is used to reduce the positioning error [27][28][29][30][31]. However, the method mentioned above increases the complexity of pedestrian position hardware, and a new error source will develop, which can degrade the hardware's accuracy.…”

Indoor Tracking by RFID Fusion with IMU Data

“…However, this method requires a priori information about the environment and the system. Bao et al [8] fused inertial sensors and maps of information, while Gu [9] proposed a particle filter-based human position Manuscript estimation method using a map of the monitored area. Clearly, to use these two methods, a map of the environment is a necessity.…”