DeepLabCut custom-trained model and the refinement function for gait analysis
Abstract The current gold standard for the study of human movement is the marker-based motion capture system that offers high precision but constrained by costs and controlled environments. Markerless pose estimation systems emerge as ecological alternatives, allowing unobtrusive data acquisition in...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85591-1 |
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| author | Giulia Panconi Stefano Grasso Sara Guarducci Lorenzo Mucchi Diego Minciacchi Riccardo Bravi |
| author_facet | Giulia Panconi Stefano Grasso Sara Guarducci Lorenzo Mucchi Diego Minciacchi Riccardo Bravi |
| author_sort | Giulia Panconi |
| collection | DOAJ |
| description | Abstract The current gold standard for the study of human movement is the marker-based motion capture system that offers high precision but constrained by costs and controlled environments. Markerless pose estimation systems emerge as ecological alternatives, allowing unobtrusive data acquisition in natural settings. This study compares the performance of two popular markerless systems, OpenPose (OP) and DeepLabCut (DLC), in assessing locomotion. Forty healthy subjects walked along a 5 m walkway equipped with four force platforms and a camera. Gait parameters were obtained using OP “BODY_25” Pre-Trained model (OPPT), DLC “Model Zoo full_human” Pre-Trained model (DLCPT) and DLC Custom-Trained model (DLCCT), then compared with those acquired from the force platforms as reference system. Our results showed that DLCCT outperformed DLCPT and OPPT, highlighting the importance of leveraging DeepLabCut transfer learning to enhance the pose estimation performance with a custom-trained neural networks. Moreover, DLCCT, with the implementation of the DLC refinement function, offers the most promising markerless pose estimation solution for evaluating locomotion. Therefore, our data provide insights into the DLC training and refinement processes required to achieve optimal performance. This study proposes perspectives for clinicians and practitioners seeking accurate low-cost methods for movement assessment beyond laboratory settings. |
| format | Article |
| id | doaj-art-0c89251eceea4807b2ab9522bb25d7e3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0c89251eceea4807b2ab9522bb25d7e32025-01-19T12:18:04ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85591-1DeepLabCut custom-trained model and the refinement function for gait analysisGiulia Panconi0Stefano Grasso1Sara Guarducci2Lorenzo Mucchi3Diego Minciacchi4Riccardo Bravi5Department of Experimental and Clinical Medicine, University of FlorenceDepartment of Physiology and Pharmacology, SAPIENZA University of RomeDepartment of Information Engineering, University of FlorenceDepartment of Information Engineering, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceDepartment of Experimental and Clinical Medicine, University of FlorenceAbstract The current gold standard for the study of human movement is the marker-based motion capture system that offers high precision but constrained by costs and controlled environments. Markerless pose estimation systems emerge as ecological alternatives, allowing unobtrusive data acquisition in natural settings. This study compares the performance of two popular markerless systems, OpenPose (OP) and DeepLabCut (DLC), in assessing locomotion. Forty healthy subjects walked along a 5 m walkway equipped with four force platforms and a camera. Gait parameters were obtained using OP “BODY_25” Pre-Trained model (OPPT), DLC “Model Zoo full_human” Pre-Trained model (DLCPT) and DLC Custom-Trained model (DLCCT), then compared with those acquired from the force platforms as reference system. Our results showed that DLCCT outperformed DLCPT and OPPT, highlighting the importance of leveraging DeepLabCut transfer learning to enhance the pose estimation performance with a custom-trained neural networks. Moreover, DLCCT, with the implementation of the DLC refinement function, offers the most promising markerless pose estimation solution for evaluating locomotion. Therefore, our data provide insights into the DLC training and refinement processes required to achieve optimal performance. This study proposes perspectives for clinicians and practitioners seeking accurate low-cost methods for movement assessment beyond laboratory settings.https://doi.org/10.1038/s41598-025-85591-1Deep learningDeepLabCutGait AnalysisOpenPosePose estimationVideo analysis |
| spellingShingle | Giulia Panconi Stefano Grasso Sara Guarducci Lorenzo Mucchi Diego Minciacchi Riccardo Bravi DeepLabCut custom-trained model and the refinement function for gait analysis Scientific Reports Deep learning DeepLabCut Gait Analysis OpenPose Pose estimation Video analysis |
| title | DeepLabCut custom-trained model and the refinement function for gait analysis |
| title_full | DeepLabCut custom-trained model and the refinement function for gait analysis |
| title_fullStr | DeepLabCut custom-trained model and the refinement function for gait analysis |
| title_full_unstemmed | DeepLabCut custom-trained model and the refinement function for gait analysis |
| title_short | DeepLabCut custom-trained model and the refinement function for gait analysis |
| title_sort | deeplabcut custom trained model and the refinement function for gait analysis |
| topic | Deep learning DeepLabCut Gait Analysis OpenPose Pose estimation Video analysis |
| url | https://doi.org/10.1038/s41598-025-85591-1 |
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