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Article

Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits

by
Michael V. Potter
1,*,
Stephen M. Cain
2,
Lauro V. Ojeda
3,
Reed D. Gurchiek
4,
Ryan S. McGinnis
5 and
Noel C. Perkins
3
1
Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USA
2
Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA
3
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
4
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
5
Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(21), 8398; https://doi.org/10.3390/s22218398
Submission received: 20 September 2022 / Revised: 26 October 2022 / Accepted: 28 October 2022 / Published: 1 November 2022
(This article belongs to the Special Issue Wearable or Markerless Sensors for Gait and Movement Analysis)
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Abstract

Inertial measurement units (IMUs) offer an attractive way to study human lower-limb kinematics without traditional laboratory constraints. We present an error-state Kalman filter method to estimate 3D joint angles, joint angle ranges of motion, stride length, and step width using data from an array of seven body-worn IMUs. Importantly, this paper contributes a novel joint axis measurement correction that reduces joint angle drift errors without assumptions of strict hinge-like joint behaviors of the hip and knee. We evaluate the method compared to two optical motion capture methods on twenty human subjects performing six different types of walking gait consisting of forward walking (at three speeds), backward walking, and lateral walking (left and right). For all gaits, RMS differences in joint angle estimates generally remain below 5 degrees for all three ankle joint angles and for flexion/extension and abduction/adduction of the hips and knees when compared to estimates from reflective markers on the IMUs. Additionally, mean RMS differences in estimated stride length and step width remain below 0.13 m for all gait types, except stride length during slow walking. This study confirms the method’s potential for non-laboratory based gait analysis, motivating further evaluation with IMU-only measurements and pathological gaits.
Keywords: IMU; motion capture; sensor fusion; abnormal gait IMU; motion capture; sensor fusion; abnormal gait

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MDPI and ACS Style

Potter, M.V.; Cain, S.M.; Ojeda, L.V.; Gurchiek, R.D.; McGinnis, R.S.; Perkins, N.C. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. Sensors 2022, 22, 8398. https://doi.org/10.3390/s22218398

AMA Style

Potter MV, Cain SM, Ojeda LV, Gurchiek RD, McGinnis RS, Perkins NC. Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. Sensors. 2022; 22(21):8398. https://doi.org/10.3390/s22218398

Chicago/Turabian Style

Potter, Michael V., Stephen M. Cain, Lauro V. Ojeda, Reed D. Gurchiek, Ryan S. McGinnis, and Noel C. Perkins. 2022. "Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits" Sensors 22, no. 21: 8398. https://doi.org/10.3390/s22218398

APA Style

Potter, M. V., Cain, S. M., Ojeda, L. V., Gurchiek, R. D., McGinnis, R. S., & Perkins, N. C. (2022). Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits. Sensors, 22(21), 8398. https://doi.org/10.3390/s22218398

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