Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network
Background: Gait analysis using three-dimensional motion capture systems (3D motion capture) provides a combination of kinematic and kinetic measurements for quantifying and characterizing the motion and loads, respectively, of lower extremity joints during human movement. However, their high cost a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Arthroplasty Today |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352344125000433 |
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| author | Stefano A. Bini, MD Nicholas Gillian, PhD Thomas A. Peterson, PhD Richard B. Souza, PhD, PT Brooke Schultz, MS, ACE-CPT Wojciech Mormul, MS Marek K. Cichoń, MS Agnieszka Barbara Szczotka, MS Ivan Poupyrev, PhD |
| author_facet | Stefano A. Bini, MD Nicholas Gillian, PhD Thomas A. Peterson, PhD Richard B. Souza, PhD, PT Brooke Schultz, MS, ACE-CPT Wojciech Mormul, MS Marek K. Cichoń, MS Agnieszka Barbara Szczotka, MS Ivan Poupyrev, PhD |
| author_sort | Stefano A. Bini, MD |
| collection | DOAJ |
| description | Background: Gait analysis using three-dimensional motion capture systems (3D motion capture) provides a combination of kinematic and kinetic measurements for quantifying and characterizing the motion and loads, respectively, of lower extremity joints during human movement. However, their high cost and limited accessibility impact their utility. Wearable inertial motion sensors offer a cost-effective alternative to measure simple temporospatial variables, but more complex kinematic variables require machine learning interfaces. We hypothesize that kinematic measures about the knee collected using motion capture can be replicated by coupling raw data collected from inertial measurement units (IMUs) to machine learning algorithms. Methods: Data from 40 healthy participants performing fixed walking, stair climbing, and sit-to-stand tasks were collected using both 3D motion capture and IMUs. Sequence to sequence convolutional neural networks were trained to map IMU data to three motion capture kinematic outputs: right knee angle, right knee angular velocity, and right hip angle. Model performance was assessed using mean absolute error. Results: The convolutional neural network models exhibited high accuracy in replicating motion capture-derived kinematic variables. Mean absolute error values for right knee angle ranged from 4.30 ± 1.55 to 5.79 ± 2.93 degrees, for right knee angular velocity from 7.82 ± 3.01 to 22.16 ± 9.52 degrees per second, and for right hip angle from 4.82 ± 2.29 to 8.63 ± 4.73 degrees. Task-specific variations in accuracy were observed. Conclusions: The findings highlight the potential of leveraging raw data from wearable inertial sensors and machine learning algorithms to reproduce gait lab-quality kinematic data outside the laboratory settings for the study of knee function following joint injury, surgery, or the progression of joint disease. |
| format | Article |
| id | doaj-art-d468d774ba924dca99fbbb2c754ad07e |
| institution | Kabale University |
| issn | 2352-3441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Arthroplasty Today |
| spelling | doaj-art-d468d774ba924dca99fbbb2c754ad07e2025-08-20T03:26:33ZengElsevierArthroplasty Today2352-34412025-06-013310165610.1016/j.artd.2025.101656Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural NetworkStefano A. Bini, MD0Nicholas Gillian, PhD1Thomas A. Peterson, PhD2Richard B. Souza, PhD, PT3Brooke Schultz, MS, ACE-CPT4Wojciech Mormul, MS5Marek K. Cichoń, MS6Agnieszka Barbara Szczotka, MS7Ivan Poupyrev, PhD8Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA; Corresponding author. Department of Orthopaedics, University of California San Francisco, 500 Parnassus Avenue, MU 323-W, San Francisco, CA 94143.Google Advanced Technology & Projects (ATAP) Invention Studio, Palo Alto, CA, USADepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USADepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA; Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, USADepartment of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USADepartment of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, USADepartment of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, USADepartment of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, USADepartment of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, CA, USABackground: Gait analysis using three-dimensional motion capture systems (3D motion capture) provides a combination of kinematic and kinetic measurements for quantifying and characterizing the motion and loads, respectively, of lower extremity joints during human movement. However, their high cost and limited accessibility impact their utility. Wearable inertial motion sensors offer a cost-effective alternative to measure simple temporospatial variables, but more complex kinematic variables require machine learning interfaces. We hypothesize that kinematic measures about the knee collected using motion capture can be replicated by coupling raw data collected from inertial measurement units (IMUs) to machine learning algorithms. Methods: Data from 40 healthy participants performing fixed walking, stair climbing, and sit-to-stand tasks were collected using both 3D motion capture and IMUs. Sequence to sequence convolutional neural networks were trained to map IMU data to three motion capture kinematic outputs: right knee angle, right knee angular velocity, and right hip angle. Model performance was assessed using mean absolute error. Results: The convolutional neural network models exhibited high accuracy in replicating motion capture-derived kinematic variables. Mean absolute error values for right knee angle ranged from 4.30 ± 1.55 to 5.79 ± 2.93 degrees, for right knee angular velocity from 7.82 ± 3.01 to 22.16 ± 9.52 degrees per second, and for right hip angle from 4.82 ± 2.29 to 8.63 ± 4.73 degrees. Task-specific variations in accuracy were observed. Conclusions: The findings highlight the potential of leveraging raw data from wearable inertial sensors and machine learning algorithms to reproduce gait lab-quality kinematic data outside the laboratory settings for the study of knee function following joint injury, surgery, or the progression of joint disease.http://www.sciencedirect.com/science/article/pii/S2352344125000433Knee kinematicsInertial motion sensorsMachine learningGait analysisMusculoskeletal researchWearables |
| spellingShingle | Stefano A. Bini, MD Nicholas Gillian, PhD Thomas A. Peterson, PhD Richard B. Souza, PhD, PT Brooke Schultz, MS, ACE-CPT Wojciech Mormul, MS Marek K. Cichoń, MS Agnieszka Barbara Szczotka, MS Ivan Poupyrev, PhD Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network Arthroplasty Today Knee kinematics Inertial motion sensors Machine learning Gait analysis Musculoskeletal research Wearables |
| title | Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network |
| title_full | Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network |
| title_fullStr | Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network |
| title_full_unstemmed | Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network |
| title_short | Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network |
| title_sort | unlocking gait analysis beyond the gait lab high fidelity replication of knee kinematics using inertial motion units and a convolutional neural network |
| topic | Knee kinematics Inertial motion sensors Machine learning Gait analysis Musculoskeletal research Wearables |
| url | http://www.sciencedirect.com/science/article/pii/S2352344125000433 |
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