Improved formulation of the IMU and MARG orientation gradient descent algorithm for motion tracking in human-machine interfaces

Wearable motion tracking systems are becoming increasingly popular in human-machine interfaces. For inertial measurement, it is vital to efficiently fuse inertial, gyroscopic, and magnetometer data for spatial orientation. We introduce a new algorithm for this fusion based on using gradient descent to correct for the integral error in calculating the orientation quaternion of a rotating body. The algorithm is an improved formulation of the well-known estimation of orientation using a gradient descent algorithm. The new formulation ensures that the gradient descent algorithm uses the steepest descent, resulting in a five order of magnitude increase in the precision of the calculated orientation quaternion. We have also converted the algorithm to use fixed point integers instead of floating point numbers to more than double the speed of the calculations on the types of processors used with Inertial Measurement Units (IMUs) and Magnetic, Angular Rate and Gravity sensors (MARGs). This enables the corrections to not only be faster than the original formulations, but also remain valid for a larger range of inputs. The improved efficiency and accuracy show significant potential for increasing the scope of inertial measurement in applications where low power or greater precision is necessary such as very small wearable or implantable systems.

[1]  Ravi Vaidyanathan,et al.  A Feature Ranking Strategy to Facilitate Multivariate Signal Classification , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Ken Shoemake,et al.  Animating rotation with quaternion curves , 1985, SIGGRAPH.

[3]  Anderson Maciel,et al.  LOP-cursor: Fast and precise interaction with tiled displays using one hand and levels of precision , 2012, 2012 IEEE Symposium on 3D User Interfaces (3DUI).

[4]  Gianluigi Ferrari,et al.  On single sensor-based inertial navigation , 2016, 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[5]  Antonio Artés-Rodríguez,et al.  A novel feature extraction technique for human activity recognition , 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

[6]  Lin Xu,et al.  Micro-IMU-based motion tracking system for virtual training , 2015, 2015 34th Chinese Control Conference (CCC).

[7]  Gianluigi Ferrari,et al.  Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done Through BSN-Based Kinematic Variables? , 2014, IEEE Internet of Things Journal.

[8]  Richard B. Woodward,et al.  Gait Analysis Using Pervasive Motion Tracking and Mechanomyography Fusion , 2015 .

[9]  Brian Caulfield,et al.  Evaluating squat performance with a single inertial measurement unit , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[10]  Roger D. Quinn,et al.  A Dual Mode Human-Robot Teleoperation Interface Based on Airflow in the Aural Cavity , 2007, Int. J. Robotics Res..

[11]  Jean-Philippe Diguet,et al.  Electronic navigational chart generator for a marine mobile augmented reality system , 2014, 2014 Oceans - St. John's.

[12]  S. W. Shepperd.  Quaternion from rotation matrix. [four-parameter representation of coordinate transformation matrix] , 1978 .

[13]  Ravi Vaidyanathan,et al.  Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs? , 2017, Journal of neurologic physical therapy : JNPT.

[14]  James Diebel,et al.  Representing Attitude : Euler Angles , Unit Quaternions , and Rotation Vectors , 2006 .

[15]  Péter Szolgay,et al.  A measurement system for wrist movements in biomedical applications , 2015, 2015 European Conference on Circuit Theory and Design (ECCTD).

[16]  Jeffrey M. Friesen,et al.  State estimation for tensegrity robots , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[17]  William Harwin,et al.  Measuring motion with kinematically redundant accelerometer arrays: Theory, simulation and implementation , 2013 .

[18]  Ravi Vaidyanathan,et al.  Upper-limb prosthetic control using wearable multichannel mechanomyography , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

[19]  Panos Marantos,et al.  UAV State Estimation Using Adaptive Complementary Filters , 2016, IEEE Transactions on Control Systems Technology.

[20]  William Rowan Hamilton,et al.  ON QUATERNIONS, OR ON A NEW SYSTEM OF IMAGINARIES IN ALGEBRA , 1847 .

[21]  Gianluigi Ferrari,et al.  Inertial BSN-Based Characterization and Automatic UPDRS Evaluation of the Gait Task of Parkinsonians , 2016, IEEE Transactions on Affective Computing.

[22]  Michael Zyda,et al.  Orientation tracking for humans and robots using inertial sensors , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[23]  Vikram Kapila,et al.  Toward Mobile Mixed-Reality Interaction With Multi-Robot Systems , 2017, IEEE Robotics and Automation Letters.

[24]  Domenico Prattichizzo,et al.  Using inertial and magnetic sensors for hand tracking and rendering in wearable haptics , 2015, 2015 IEEE World Haptics Conference (WHC).

[25]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.