Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles

Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this p...

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Published in	Sensors (Basel, Switzerland) Vol. 19; no. 23; p. 5143
Main Authors	Adamowicz, Lukas, Gurchiek, Reed D., Ferri, Jonathan, Ursiny, Anna T., Fiorentino, Niccolo, McGinnis, Ryan S.
Format	Journal Article
Language	English
Published	Switzerland MDPI AG 24.11.2019 MDPI
Subjects	Activities of daily living Adult Algorithms Biomechanical Phenomena - physiology Calibration Female Fitness equipment Hip joint Hip Joint - physiology Humans Knee Joint - physiology Male Motion Motion capture Orientation - physiology Range of motion Range of Motion, Articular - physiology Sensors Velocity Walking Young Adult IMU motion tracking 3D joint kinematics joint angles inertial sensors wearable sensors functional rotation axes inertial measurement units
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ISSN	1424-8220 1424-8220
DOI	10.3390/s19235143

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Abstract	Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12.32 ° and 11.82 ° vs. 24.61 ° and 23.76 ° ) and flexion/extension joint angles ( 7.88 ° and 8.62 ° vs. 14.14 ° and 15.64 ° ). Also, ROM estimation error was less than 2.2 ° during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.
AbstractList	Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12.32 ° and 11.82 ° vs. 24.61 ° and 23.76 ° ) and flexion/extension joint angles ( 7.88 ° and 8.62 ° vs. 14.14 ° and 15.64 ° ). Also, ROM estimation error was less than 2.2 ° during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles. Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation (12.32° and 11.82° vs. 24.61° and 23.76°) and flexion/extension joint angles (7.88° and 8.62° vs. 14.14° and 15.64°). Also, ROM estimation error was less than 2.2° during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles. Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles. Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.
Author	Fiorentino, Niccolo McGinnis, Ryan S. Ferri, Jonathan Gurchiek, Reed D. Adamowicz, Lukas Ursiny, Anna T.
AuthorAffiliation	1 M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA; lukas.adamowicz95@gmail.com (L.A.); reed.gurchiek@uvm.edu (R.D.G.); Jonathan.Ferri@uvm.edu (J.F.); Anna.Ursiny@uvm.edu (A.T.U.) 2 Department of Mechanical Engineering, University of Vermont, Burlington, VT 05405, USA; Niccolo.Fiorentino@uvm.edu
AuthorAffiliation_xml	– name: 2 Department of Mechanical Engineering, University of Vermont, Burlington, VT 05405, USA; Niccolo.Fiorentino@uvm.edu – name: 1 M-Sense Research Group, University of Vermont, Burlington, VT 05405, USA; lukas.adamowicz95@gmail.com (L.A.); reed.gurchiek@uvm.edu (R.D.G.); Jonathan.Ferri@uvm.edu (J.F.); Anna.Ursiny@uvm.edu (A.T.U.)
Author_xml	– sequence: 1 givenname: Lukas surname: Adamowicz fullname: Adamowicz, Lukas – sequence: 2 givenname: Reed D. surname: Gurchiek fullname: Gurchiek, Reed D. – sequence: 3 givenname: Jonathan surname: Ferri fullname: Ferri, Jonathan – sequence: 4 givenname: Anna T. surname: Ursiny fullname: Ursiny, Anna T. – sequence: 5 givenname: Niccolo surname: Fiorentino fullname: Fiorentino, Niccolo – sequence: 6 givenname: Ryan S. orcidid: 0000-0001-8396-6967 surname: McGinnis fullname: McGinnis, Ryan S.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31771263$$D View this record in MEDLINE/PubMed
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Snippet	Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the...
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SubjectTerms	Activities of daily living Adult Algorithms Biomechanical Phenomena - physiology Calibration Female Fitness equipment Hip joint Hip Joint - physiology Humans Knee Joint - physiology Male Motion Motion capture Orientation - physiology Range of motion Range of Motion, Articular - physiology Sensors Velocity Walking Young Adult
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Title	Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles
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