Accuracy of a 3D temporal scanning system for gait analysis: Comparative with a marker-based photogrammetry system

Nicolau, Ana  Virginia Ruescas; Rosario, Helios De; Della-Vedova, Fermín Basso; Bernabé, Eduardo Parrilla; Juan, M. Carmen; López-Pascual, Juan

doi:10.1016/j.gaitpost.2022.07.001

Cited by 12 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the included studies, nineteen [ 31 , 32 , 33 , 34 , 35 , 36 , 38 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ] assessed the concurrent validity and accuracy of MBS and MCBS systems in gait analysis. A total of 402 healthy subjects and nineteen patients (Parkinson’s disease) were included in the studies ( Table 4 and Table 5 ).…”

Section: Resultsmentioning

confidence: 99%

“…A difference can be observed when comparing the kinematic outcome measures in the different planes of the movements. In general, poor correlations and higher errors were measured in the transverse plane for all joints, while the highest correlations can be measured in the sagittal plane [ 43 , 45 , 47 , 50 , 51 , 52 ]. Ripic et al [ 52 ] hypothesized that given the choice to use unconstrained skeletal models in his study and the current availability of key point estimations in the markerless model, larger differences in the frontal and transverse planes may be expected and result in lower agreement between systems given the limited ROM in these planes [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis

Scataglini,

Abts,

Van Bocxlaer

et al. 2024

Sensors

View full text Add to dashboard Cite

(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis

Scataglini,

Abts,

Van Bocxlaer

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…We selected this system for two reasons. First, it has been validated as a good markerless alternative to photogrammetry-based motion capture systems [8] and second, the mesh topology it provides is dense (50k vertices) and has anatomical correspondence with errors comparable to those of manual palpation procedures as explained in [9] . We used the OBJ format to represent the meshes.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Multimodal human motion dataset of 3D anatomical landmarks and pose keypoints

Ruescas-Nicolau,

Medina-Ripoll,

Parrilla Bernabé

et al. 2024

Data in Brief

Self Cite

View full text Add to dashboard Cite

“…Move4D was validated as a good alternative to marker-based MoCap using photogrammetry [ 41 ] and its homologous mesh topology allows for considering a set of fixed vertices as representative of anatomical landmark locations [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications?

Ruescas-Nicolau,

Medina-Ripoll,

de Rosario

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

In biomechanics, movement is typically recorded by tracking the trajectories of anatomical landmarks previously marked using passive instrumentation, which entails several inconveniences. To overcome these disadvantages, researchers are exploring different markerless methods, such as pose estimation networks, to capture movement with equivalent accuracy to marker-based photogrammetry. However, pose estimation models usually only provide joint centers, which are incomplete data for calculating joint angles in all anatomical axes. Recently, marker augmentation models based on deep learning have emerged. These models transform pose estimation data into complete anatomical data. Building on this concept, this study presents three marker augmentation models of varying complexity that were compared to a photogrammetry system. The errors in anatomical landmark positions and the derived joint angles were calculated, and a statistical analysis of the errors was performed to identify the factors that most influence their magnitude. The proposed Transformer model improved upon the errors reported in the literature, yielding position errors of less than 1.5 cm for anatomical landmarks and 4.4 degrees for all seven movements evaluated. Anthropometric data did not influence the errors, while anatomical landmarks and movement influenced position errors, and model, rotation axis, and movement influenced joint angle errors.

show abstract

Accuracy of a 3D temporal scanning system for gait analysis: Comparative with a marker-based photogrammetry system

Cited by 12 publications

References 28 publications

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis

Multimodal human motion dataset of 3D anatomical landmarks and pose keypoints

A Deep Learning Model for Markerless Pose Estimation Based on Keypoint Augmentation: What Factors Influence Errors in Biomechanical Applications?

Contact Info

Product

Resources

About