Abstract

In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58°, while the conventional method with the magnetic disturbance compensation mechanism produced 5.38°, showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment.

Highlights

  • Recent advances in wearable sensors and ubiquitous computing make it possible to respond to tremendous demand on health informatics related to telecare and home-monitoring for aging society.In biomechanics and rehabilitation, estimating a three-dimensional (3D) joint angle is an important requirement [1,2,3]

  • This study proposes a magnetic condition-independent 3D joint angle estimation method based on inertial measurement units (IMUs) signals

  • This study proposes a real-time IMU-based 3D joint angle estimation Kalman filter (KF), where a magnetometer is replaced by a kinematic constraint

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Summary

Introduction

Recent advances in wearable sensors and ubiquitous computing make it possible to respond to tremendous demand on health informatics related to telecare and home-monitoring for aging society.In biomechanics and rehabilitation, estimating a three-dimensional (3D) joint angle is an important requirement [1,2,3]. Marker-based optical motion capture systems have been successfully used to quantify joint kinematics by tracking the position of the surface markers during dynamic activities. These systems are expensive in general and suffer from occlusion. These systems are restricted to controlled laboratory settings. It is obvious that numerous applications can tremendously benefit by continuous monitoring of joint angles in an unconstrained daily environment (e.g., outdoors) [4,5,6]. Wearable inertial sensing is an emerging technology with a growing number of potential applications in human movement analysis, as this wearable technology can overcome the in-the-lab limitation and allows the user to perform daily life activities during the measurement

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