Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running
The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture system...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/2/695 |
| _version_ | 1797339346951471104 |
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| author | Matthew B. Rhudy Joseph M. Mahoney Allison R. Altman-Singles |
| author_facet | Matthew B. Rhudy Joseph M. Mahoney Allison R. Altman-Singles |
| author_sort | Matthew B. Rhudy |
| collection | DOAJ |
| description | The knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications. |
| first_indexed | 2024-03-08T09:46:38Z |
| format | Article |
| id | doaj.art-7251492ec37041198bd3f5a0a2721eaa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:38Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7251492ec37041198bd3f5a0a2721eaa2024-01-29T14:18:28ZengMDPI AGSensors1424-82202024-01-0124269510.3390/s24020695Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for RunningMatthew B. Rhudy0Joseph M. Mahoney1Allison R. Altman-Singles2Mechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA 19610, USAMechanical Engineering, Alvernia University, Reading, PA 19607, USAMechanical Engineering, The Pennsylvania State University, Berks College, Reading, PA 19610, USAThe knee flexion angle is an important measurement for studies of the human gait. Running is a common activity with a high risk of knee injury. Studying the running gait in realistic situations is challenging because accurate joint angle measurements typically come from optical motion-capture systems constrained to laboratory settings. This study considers the use of shank and thigh inertial sensors within three different filtering algorithms to estimate the knee flexion angle for running without requiring sensor-to-segment mounting assumptions, body measurements, specific calibration poses, or magnetometers. The objective of this study is to determine the knee flexion angle within running applications using accelerometer and gyroscope information only. Data were collected for a single test participant (21-year-old female) at four different treadmill speeds and used to validate the estimation results for three filter variations with respect to a Vicon optical motion-capture system. The knee flexion angle filtering algorithms resulted in root-mean-square errors of approximately three degrees. The results of this study indicate estimation results that are within acceptable limits of five degrees for clinical gait analysis. Specifically, a complementary filter approach is effective for knee flexion angle estimation in running applications.https://www.mdpi.com/1424-8220/24/2/695inertial measurement unitsgait analysiskinematic constraintsKalman filtering |
| spellingShingle | Matthew B. Rhudy Joseph M. Mahoney Allison R. Altman-Singles Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running Sensors inertial measurement units gait analysis kinematic constraints Kalman filtering |
| title | Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running |
| title_full | Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running |
| title_fullStr | Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running |
| title_full_unstemmed | Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running |
| title_short | Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running |
| title_sort | knee angle estimation with dynamic calibration using inertial measurement units for running |
| topic | inertial measurement units gait analysis kinematic constraints Kalman filtering |
| url | https://www.mdpi.com/1424-8220/24/2/695 |
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