Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation.

Duc Cong Dang,Young Soo Suh

doi:10.3390/s21103428

Abstract

This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources.

Highlights

The advent of Micro Electro Mechanical Systems (MEMS) technology has made it possible for Inertial Measurement Units (IMU) to attract significant attention due to their miniaturized, low power consumption, and low cost [1]
This paper mainly focuses on the attitude estimation problem in a continuous walking scenario with inertial sensor attached on human upper body other than on one’s feet
Double stance (DS) indices were equivalent to low peak indices of the vertical position

Summary

Introduction

The advent of Micro Electro Mechanical Systems (MEMS) technology has made it possible for Inertial Measurement Units (IMU) to attract significant attention due to their miniaturized, low power consumption, and low cost [1]. The use of inertial sensors has been playing a key role in a wide range of applications requiring attitude (i.e., roll and pitch) and/or heading (i.e., yaw) information such as indoor localization [2,3], unmanned underwater vehicles (UAVs) and spacecraft inertial navigation [4,5], human activity and gesture recognition [6,7,8,9], robots and drones control and stabilization [10,11]. Only the attitude is required without the heading information for various applications such as robot and vehicle stability control [15], human balance issues [16], and gait analysis [17,18,19]. The magnetometer is omitted and only the attitude estimation problem is considered using a six-DOF inertial sensor comprised of a triaxial accelerometer and a triaxial gyroscope

Methods

Results

Conclusion