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.

DeepLabCut custom-trained model and the refinement function for gait analysis / Panconi, Giulia; Grasso, Stefano; Guarducci, Sara; Mucchi, Lorenzo; Minciacchi, Diego; Bravi, Riccardo. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - ELETTRONICO. - 15:(2025), pp. 2364.1-2364.13. [10.1038/s41598-025-85591-1]

DeepLabCut custom-trained model and the refinement function for gait analysis

Panconi, Giulia;Guarducci, Sara;Mucchi, Lorenzo;Minciacchi, Diego;Bravi, Riccardo
2025

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.
2025
15
1
13
Goal 3: Good health and well-being
Goal 9: Industry, Innovation, and Infrastructure
Panconi, Giulia; Grasso, Stefano; Guarducci, Sara; Mucchi, Lorenzo; Minciacchi, Diego; Bravi, Riccardo
File in questo prodotto:
File Dimensione Formato  
s41598-025-85591-1.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: Tutti i diritti riservati
Dimensione 3.54 MB
Formato Adobe PDF
3.54 MB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1415124
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact