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.
Highlights
Wearable inertial/magnetic sensor based human lower body motion analysis has been widely applied in a variety of applications, such as animation, entertainment, sports training, gait analysis and rehabilitation [1,2,3]
The main contributions of this paper include: (1) we design a simple and reliable method of key gait events detection to identify the stance phase of a gait cycle based on the gyroscope measurements; (2) in the framework of Bayesian dynamics, we introduce an acceleration control variable derived from zero velocity update (ZUPT) in the process model for the non-stance phases, while the map information is taken as the binary pseudo-measurement to further reduce uncertainty of walking trajectories; (3) a particle filter is designed to fuse ZUPT information and binary pseudo-measurements together
As can see from the figure, it is very clear that the proposed method can achieve the smallest position errors compared with the other three traditional ZUPT methods, which means that the integration of ZUPT and map information can improve the position estimation accuracy significantly
Summary
Wearable inertial/magnetic sensor based human lower body motion analysis has been widely applied in a variety of applications, such as animation, entertainment, sports training, gait analysis and rehabilitation [1,2,3]. Due to the GPS signal attenuation caused by buildings, tunnels, and other construction materials, GPS may not be applicable for robust and accurate indoor human position estimation [6,7] Alterative solutions, such as WiFi. Sensors 2017, 17, 340 and Bluetooth beacons, magnetometer, vision, or ultrasound for indoor localization have been explored so far [8,9], but such systems require extensive setup and calibration of the tracking volume, which may be of limited size and may suffer from occlusion. Foxlin [15] and Godha et al [16] proposed to reset the integrated velocity to zero directly during the stance phases by introducing ZUPT as pseudo-measurements into an extended Kalman filter Such methods only used ZUPT information in stance phases, and ignored accumulated errors in non-stance phases of gait cycles.
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