Abstract

With the advancements in micro-electromechanical systems (MEMS) technologies, magnetic and inertial sensors are becoming more and more accurate, lightweight, smaller in size as well as low-cost, which in turn boosts their applications in human movement analysis. However, challenges still exist in the field of sensor orientation estimation, where magnetic disturbance represents one of the obstacles limiting their practical application. The objective of this paper is to systematically analyze exactly how magnetic disturbances affects the attitude and heading estimation for a magnetic and inertial sensor. First, we reviewed four major components dealing with magnetic disturbance, namely decoupling attitude estimation from magnetic reading, gyro bias estimation, adaptive strategies of compensating magnetic disturbance and sensor fusion algorithms. We review and analyze the features of existing methods of each component. Second, to understand each component in magnetic disturbance rejection, four representative sensor fusion methods were implemented, including gradient descent algorithms, improved explicit complementary filter, dual-linear Kalman filter and extended Kalman filter. Finally, a new standardized testing procedure has been developed to objectively assess the performance of each method against magnetic disturbance. Based upon the testing results, the strength and weakness of the existing sensor fusion methods were easily examined, and suggestions were presented for selecting a proper sensor fusion algorithm or developing new sensor fusion method.

Highlights

  • Advancements in micro-electromechanical systems (MEMS) technologies have made magnetic and inertial sensors more and more accurate, lightweight and low-cost, which has greatly promoted their usage in human motion analysis

  • This paper focuses on analyzing each of the four key components dealing and aware that sensor fusion algorithms (SFA) is not the only important component in a magnetic and inertial measurement unit (MIMU) when estimating orientation, because the SFA cannot be adapted to various ambient magnetic environments without the assistance of the other three components

  • We focus on enabling a better understanding of how magnetic disturbances influence the attitude and heading in a MIMU-based orientation estimation algorithm, as well as the performance of current popular SFAs under standardized test conditions

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Summary

Introduction

Advancements in micro-electromechanical systems (MEMS) technologies have made magnetic and inertial sensors more and more accurate, lightweight and low-cost, which has greatly promoted their usage in human motion analysis. These applications include walking speed estimation [1], gait analysis [2], pedestrian dead-reckoning (PDR) [3], activity classification [4], etc. In quasi-static condition and in a magnetically clean environment, the attitude of a sensor can be calculated from the measured gravitational acceleration, and the heading can be calculated from the measured geomagnetic field.

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