TY - GEN
T1 - Estimation of IMU and MARG orientation using a gradient descent algorithm
AU - Madgwick, Sebastian O.H.
AU - Harrison, Andrew J.L.
AU - Vaidyanathan, Ravi
N1 - © 2011 IEEE
PY - 2011
Y1 - 2011
N2 - This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.
AB - This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.
UR - http://www.scopus.com/inward/record.url?scp=80055059186&partnerID=8YFLogxK
U2 - 10.1109/ICORR.2011.5975346
DO - 10.1109/ICORR.2011.5975346
M3 - Conference contribution
C2 - 22275550
AN - SCOPUS:80055059186
SN - 9781424498628
T3 - IEEE International Conference on Rehabilitation Robotics
SP - 5975346
BT - 2011 IEEE International Conference on Rehabilitation Robotics, ICORR 2011 - Rehab Week Zurich 2011
T2 - Rehab Week Zurich 2011 - 2011 IEEE International Conference on Rehabilitation Robotics, ICORR 2011
Y2 - 27 June 2011 through 1 July 2011
ER -