Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits
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...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/21/8398 |
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| author | Michael V. Potter Stephen M. Cain Lauro V. Ojeda Reed D. Gurchiek Ryan S. McGinnis Noel C. Perkins |
| author_facet | Michael V. Potter Stephen M. Cain Lauro V. Ojeda Reed D. Gurchiek Ryan S. McGinnis Noel C. Perkins |
| author_sort | Michael V. Potter |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:39:50Z |
| format | Article |
| id | doaj.art-2113737f2acd4597a11bcbfaea8dc60c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:39:50Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2113737f2acd4597a11bcbfaea8dc60c2023-11-24T06:47:27ZengMDPI AGSensors1424-82202022-11-012221839810.3390/s22218398Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking GaitsMichael V. Potter0Stephen M. Cain1Lauro V. Ojeda2Reed D. Gurchiek3Ryan S. McGinnis4Noel C. Perkins5Department of Physics and Engineering, Francis Marion University, Florence, SC 29506, USADepartment of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USADepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Bioengineering, Stanford University, Stanford, CA 94305, USADepartment of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USADepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USAInertial 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.https://www.mdpi.com/1424-8220/22/21/8398IMUmotion capturesensor fusionabnormal gait |
| spellingShingle | Michael V. Potter Stephen M. Cain Lauro V. Ojeda Reed D. Gurchiek Ryan S. McGinnis Noel C. Perkins Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits Sensors IMU motion capture sensor fusion abnormal gait |
| title | Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits |
| title_full | Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits |
| title_fullStr | Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits |
| title_full_unstemmed | Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits |
| title_short | Evaluation of Error-State Kalman Filter Method for Estimating Human Lower-Limb Kinematics during Various Walking Gaits |
| title_sort | evaluation of error state kalman filter method for estimating human lower limb kinematics during various walking gaits |
| topic | IMU motion capture sensor fusion abnormal gait |
| url | https://www.mdpi.com/1424-8220/22/21/8398 |
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