A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices
Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/9/2759 |
| _version_ | 1811184742444302336 |
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| author | Eric Allseits Kyoung Jae Kim Christopher Bennett Robert Gailey Ignacio Gaunaurd Vibhor Agrawal |
| author_facet | Eric Allseits Kyoung Jae Kim Christopher Bennett Robert Gailey Ignacio Gaunaurd Vibhor Agrawal |
| author_sort | Eric Allseits |
| collection | DOAJ |
| description | Tele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland–Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle. |
| first_indexed | 2024-04-11T13:17:35Z |
| format | Article |
| id | doaj.art-ef99fb38c483498980c8eba5d3284852 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:17:35Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ef99fb38c483498980c8eba5d32848522022-12-22T04:22:20ZengMDPI AGSensors1424-82202018-08-01189275910.3390/s18092759s18092759A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health DevicesEric Allseits0Kyoung Jae Kim1Christopher Bennett2Robert Gailey3Ignacio Gaunaurd4Vibhor Agrawal5Department of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USADepartment of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USADepartment of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USADepartment of Physical Therapy, Miller School of Medicine, University of Miami, Miami, FL 33136, USAMiami Veterans Affairs Healthcare System, Miami, FL 33125, USADepartment of Biomedical Engineering, University of Miami, Coral Gables, FL 33146, USATele-rehabilitation of patients with gait abnormalities could benefit from continuous monitoring of knee joint angle in the home and community. Continuous monitoring with mobile devices can be restricted by the number of body-worn sensors, signal bandwidth, and the complexity of operating algorithms. Therefore, this paper proposes a novel algorithm for estimating knee joint angle using lower limb angular velocity, obtained with only two leg-mounted gyroscopes. This gyroscope only (GO) algorithm calculates knee angle by integrating gyroscope-derived knee angular velocity signal, and thus avoids reliance on noisy accelerometer data. To eliminate drift in gyroscope data, a zero-angle update derived from a characteristic point in the knee angular velocity is applied to every stride. The concurrent validity and construct convergent validity of the GO algorithm was determined with two existing IMU-based algorithms, complementary and Kalman filters, and an optical motion capture system, respectively. Bland–Altman analysis indicated a high-level of agreement between the GO algorithm and other measures of knee angle.http://www.mdpi.com/1424-8220/18/9/2759inertial measurement unit (IMU)gyroscopeknee angular velocityknee joint anglegait analysismobile health |
| spellingShingle | Eric Allseits Kyoung Jae Kim Christopher Bennett Robert Gailey Ignacio Gaunaurd Vibhor Agrawal A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices Sensors inertial measurement unit (IMU) gyroscope knee angular velocity knee joint angle gait analysis mobile health |
| title | A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices |
| title_full | A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices |
| title_fullStr | A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices |
| title_full_unstemmed | A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices |
| title_short | A Novel Method for Estimating Knee Angle Using Two Leg-Mounted Gyroscopes for Continuous Monitoring with Mobile Health Devices |
| title_sort | novel method for estimating knee angle using two leg mounted gyroscopes for continuous monitoring with mobile health devices |
| topic | inertial measurement unit (IMU) gyroscope knee angular velocity knee joint angle gait analysis mobile health |
| url | http://www.mdpi.com/1424-8220/18/9/2759 |
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