Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System?
Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. T...
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2025-03-01
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| author | Joel Carriere Michele L. Oliver Andrew Hamilton-Wright Calvin Young Karen D. Gordon |
| author_facet | Joel Carriere Michele L. Oliver Andrew Hamilton-Wright Calvin Young Karen D. Gordon |
| author_sort | Joel Carriere |
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| description | Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion–extension (FE) and radial–ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>E</mi><mo>−</mo><mn>99</mn></mrow></semantics></math></inline-formula>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>U</mi><mi>D</mi><mo>−</mo><mn>25.2</mn></mrow></semantics></math></inline-formula>). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM. |
| format | Article |
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| institution | OA Journals |
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| spelling | doaj-art-a426cc5b6c074f268a7ac0458b2ec59c2025-08-20T02:09:10ZengMDPI AGApplied Sciences2076-34172025-03-01157355210.3390/app15073552Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System?Joel Carriere0Michele L. Oliver1Andrew Hamilton-Wright2Calvin Young3Karen D. Gordon4School of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, CanadaSchool of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, CanadaSchool of Computer Science, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, CanadaDepartment of Systems Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, CanadaSchool of Engineering, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, CanadaWrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion–extension (FE) and radial–ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>F</mi><mi>E</mi><mo>−</mo><mn>99</mn></mrow></semantics></math></inline-formula>; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>U</mi><mi>D</mi><mo>−</mo><mn>25.2</mn></mrow></semantics></math></inline-formula>). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM.https://www.mdpi.com/2076-3417/15/7/3552machine learningmotion captureDeepLabCutAniposedeep neural networks |
| spellingShingle | Joel Carriere Michele L. Oliver Andrew Hamilton-Wright Calvin Young Karen D. Gordon Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? Applied Sciences machine learning motion capture DeepLabCut Anipose deep neural networks |
| title | Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? |
| title_full | Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? |
| title_fullStr | Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? |
| title_full_unstemmed | Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? |
| title_short | Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System? |
| title_sort | can machine learning enhance computer vision predicted wrist kinematics determined from a low cost motion capture system |
| topic | machine learning motion capture DeepLabCut Anipose deep neural networks |
| url | https://www.mdpi.com/2076-3417/15/7/3552 |
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