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Sensors (Basel). 2019 Jan 31;19(3). pii: E596. doi: 10.3390/s19030596.

Validation of Thigh Angle Estimation Using Inertial Measurement Unit Data against Optical Motion Capture Systems.

Author information

1
Department of Electrical and Computer Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka. nimsiri.a@sliit.lk.
2
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University of Technology, Perth 6102, Australia. I.Murray@curtin.edu.au.
3
School of Civil and Mechanical Engineering, Curtin University of Technology, Perth 6102, Australia. Shiva.Sharif@curtin.edu.au.

Abstract

Inertial measurement units are commonly used to estimate the orientation of sections of sections of human body in inertial navigation systems. Most of the algorithms used for orientation estimation are computationally expensive and it is difficult to implement them in real-time embedded systems with restricted capabilities. This paper discusses a computationally inexpensive orientation estimation algorithm (Gyro Integration-Based Orientation Filter-GIOF) that is used to estimate the forward and backward swing angle of the thigh (thigh angle) for a vision impaired navigation aid. The algorithm fuses the accelerometer and gyroscope readings to derive the single dimension orientation in such a way that the orientation is corrected using the accelerometer reading when it reads gravity only or otherwise integrate the gyro reading to estimate the orientation. This strategy was used to reduce the drift caused by the gyro integration. The thigh angle estimated by GIOF was compared against the Vicon Optical Motion Capture System and reported a mean correlation of 99.58% for 374 walking trials with a standard deviation of 0.34%. The Root Mean Square Error (RMSE) of the thigh angle estimated by GIOF compared with Vicon measurement was 1.8477°. The computation time on an 8-bit microcontroller running at 8 MHz for GIOF is about a half of that of Complementary Filter implementation. Although GIOF was only implemented and tested for estimating pitch of the IMU, it can be easily extended into 2D to estimate both pitch and roll.

KEYWORDS:

human gait analysis; inertial measurement units; sensor fusion

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