A novel dataset and deep learning-based approach for marker-less motion capture during gait

Vafadar, Saman; Skalli, Wafa; Bonnet-Lebrun, Aurore; Khalifé, Marc; Renaudin, Mathis; Hamza, Amine; Gajny, Laurent

doi:10.1016/j.gaitpost.2021.03.003

Cited by 20 publications

(29 citation statements)

References 24 publications

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“…The dataset used in this research was a multi-view human gait dataset, namely, the ENSAM dataset collected in a previous study [ 5 ]. The dataset contained a total of 43 subjects (19 females and 24 males; age range: 6–44 years; weight: 56.0 ± 20.7 kg; height: 159.2 ± 21.5 cm) which were split into a training set of 27 subjects (14 females and 13 males; age range: 8–41 years; weight: 54.0 ± 20.2 kg; height: 157.7 ± 21.8 cm) and a test set of 16 subjects (5 females and 11 males; age range: 6–44 years; weight: 59.6 ± 21.8 kg; height: 161.8 ± 21.3 cm).…”

Section: Methodsmentioning

confidence: 99%

“…The focus of human pose estimation is the calculation of human body keypoint coordinates based on images. Combined with kinematic analysis, it has the potential for many applications in different fields, e.g., ergonomics [ 1 , 2 , 3 , 4 ] or orthopedics [ 5 , 6 ]. In vision-based human pose estimation tasks, the datasets used for training and testing models often consist of images where the human face is clearly visible.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications

Jiang

Skalli

Siadat

et al. 2022

Sensors

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The face blurring of images plays a key role in protecting privacy. However, in computer vision, especially for the human pose estimation task, machine-learning models are currently trained, validated, and tested on original datasets without face blurring. Additionally, the accuracy of human pose estimation is of great importance for kinematic analysis. This analysis is relevant in areas such as occupational safety and clinical gait analysis where privacy is crucial. Therefore, in this study, we explore the impact of face blurring on human pose estimation and the subsequent kinematic analysis. Firstly, we blurred the subjects’ heads in the image dataset. Then we trained our neural networks using the face-blurred and the original unblurred dataset. Subsequently, the performances of the different models, in terms of landmark localization and joint angles, were estimated on blurred and unblurred testing data. Finally, we examined the statistical significance of the effect of face blurring on the kinematic analysis along with the strength of the effect. Our results reveal that the strength of the effect of face blurring was low and within acceptable limits (<1°). We have thus shown that for human pose estimation, face blurring guarantees subject privacy while not degrading the prediction performance of a deep learning model.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications

Jiang

Skalli

Siadat

et al. 2022

Sensors

Self Cite

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“…Vafadar et al [28] performed markerless gait analysis by first reconstructing an accurate human pose in 3D from multiple camera views. They collected a gait-specific dataset composed by 31 participants, 22 with normal gait and 9 with pathological gait.…”

Section: Related Workmentioning

confidence: 99%

Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study

Moro

Marchesi

Hesse

et al. 2022

Sensors

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The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

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“…In such studies, markerless motion capture has shown great promise. Specifically, focusing on the ability to detect lower extremity movement, multiple studies have indicated that markerless motion capture can efficiently capture spatiotemporal joint kinematic variables (Clark et al, 2013;Sandau et al, 2014;Mentiplay et al, 2015;Rocha et al, 2018) with moderate-to-high agreement during tasks such as a single leg squat (Perrott et al, 2017;Kotsifaki et al, 2018;Tipton et al, 2019), vertical jump (Drazan et al, 2021), countermovement jump (Kotsifaki et al, 2018), stair climbing (Ogawa et al, 2017), walking (Ceseracciu et al, 2014;Sandau et al, 2014;Kanko et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Takeda et al, 2021;Vafadar et al, 2021), running (Corazza et al, 2006;Macpherson et al, 2016;Pagnon et al, 2021), gymnastics tasks (Corazza et al, 2006(Corazza et al, , 2010Mündermann et al, 2007), and clinical evaluations (Eltoukhy et al, 2017;Mauntel et al, 2021). To date, the highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006(Corazza et al, , 2007(Corazza et al, , 2008(Corazza et al, , 2010Mündermann et al, 2006bMündermann et al, , 2007.…”

Section: Strengths Agreement Between Markerless and Marker-based Systemsmentioning

confidence: 99%

“…To date, the highest accuracy with markerless motion capture has been achieved when fitting a prior articulated model to a 3D surface visual hull reconstruction using matching algorithms (Corazza et al, 2006(Corazza et al, , 2007(Corazza et al, , 2008(Corazza et al, , 2010Mündermann et al, 2006bMündermann et al, , 2007. More recently, however, the application of deep learning algorithms, keypoint detection approaches for biomechanical assessment are beginning to show similar or greater accuracies and illustrate significant promise for the future of markerless motion capture in the sports medicine domain (Drazan et al, 2021;Kanko et al, 2021;Needham et al, 2021;Pagnon et al, 2021;Stenum et al, 2021;Vafadar et al, 2021). It is important to note that the majority of validation studies utilizing markerless motion capture to assess joint kinematics evaluate relatively slow movements such as walking, or single plane motions such as the sagittal plane during jumping.…”

Section: Strengths Agreement Between Markerless and Marker-based Systemsmentioning

confidence: 99%

A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

Armitano-Lago

Dominic

Kiefer

2022

Front. Sports Act. Living

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Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand the capacities and considerations for implementing these types of systems. The current review provides a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis related to the successful adoption of markerless motion capture technology for the assessment of lower-limb musculoskeletal kinematics in sport medicine and performance settings. 31 articles met the a priori inclusion criteria of this analysis. Findings from the analysis indicate that the improving accuracy of these systems via the refinement of machine learning algorithms, combined with their cost efficacy and the enhanced ecological validity outweighs the current weaknesses and threats. Further, the analysis makes clear that there is a need for multidisciplinary collaboration between sport scientists and computer vision scientists to develop accurate clinical and research applications that are specific to sport. While work remains to be done for broad application, markerless motion capture technology is currently on a positive trajectory and the data from this analysis provide an efficient roadmap toward widespread adoption.

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A novel dataset and deep learning-based approach for marker-less motion capture during gait

Cited by 20 publications

References 24 publications

Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications

Effect of Face Blurring on Human Pose Estimation: Ensuring Subject Privacy for Medical and Occupational Health Applications

Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study

A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

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