Simultaneous identification of human body model parameters and gait trajectory from 3D motion capture data

Ziegler, Jakob; Reiter, Alexander; Gattringer, Hubert; Müller, Andreas

doi:10.1016/j.medengphy.2020.08.009

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…Based on the model parameters derived from inaccurate anthropomorphic data, Ziegler et al proposed a comprehensive identification method for geometric parameters of the human lower limb kinematics model based on three-dimensional marker position and parameters for determining (cycling) gait trajectory [ 8 ]. Kimpara et al proposed an active driving system based on the human body model with the cooperation of team members.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Liu

Ren

Qin³

2021

Computational Intelligence and Neuroscience

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“…9, the Fourier series converge towards pseudo-constant values, confirming, as expected, the quasi-periodicity of the joint trajectories. The quasi-periodicity of gait was already used for motion analysis in the literature [10]- [12] and seems to be a valid assumption even for patients.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, during gait, they showed the superiority of using Fourier series over using standard constant acceleration model of joint temporal evolution. The assumption that the joint trajectories can be modelled by using low-order Fourier series has already been made in the literature [10]- [12]. Fourier representation was also largely used for spatio-temporal parameters assessment using IMUs [13].…”

Section: A Related Workmentioning

confidence: 99%

Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment

Mallat

Bonnet

Dumas

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study investigates the possibility of estimating lower-limb joint kinematics and meaningful performance indexes for physiotherapists, during gait on a treadmill based on data collected from a sparse placement of new Visual Inertial Measurement Units (VIMU) and the use of an Extended Kalman Filter (EKF). The proposed EKF takes advantage of the biomechanics of the human body and of the investigated task to reduce sensor inaccuracies. Two state-vector formulations, one based on the use of constant acceleration model and one based on Fourier series, and the tuning of their corresponding parameters were analyzed. The constant acceleration model, due to its inherent inconsistency for human motion, required a cumbersome optimisation process and needed the a-priori knowledge of reference joint trajectories for EKF parameters tuning. On the other hand, the Fourier series formulation could be used without a specific parameters tuning process. In both cases, the average root mean square difference and correlation coefficient between the estimated joint angles and those reconstructed with a reference stereophotogrammetric system was 3.5deg and 0.70, respectively. Moreover, the stride lengths were estimated with a normalized root mean square difference inferior to 2% when using the forward kinematics model receiving as input the estimated joint angles. The popular gait deviation index was also estimated and showed similar results very close to 100, using both the proposed method and the reference stereophotogrammetric system. Such consistency was obtained using only three wireless and affordable VIMU located at the pelvis and both heels and tracked using two affordable RGB cameras. Being further easy-to-use and suitable for applications taking place outside of the laboratory, the proposed method thus represents a good compromise between accurate reference stereophotogrammetric systems and markerless ones for which accuracy is still under debate.

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“…Scaling of a generic model is performed with the aim of matching the gross anthropometry of a subject. This process linearly scales each body (e.g., femur and pelvis) to match measured segment lengths, anthropometric table values, or most commonly, experimentally acquired motion capture marker positions [8][9][10]. Along with model geometry, other parameters including joint positions, segment mass and inertia, and MTU pathways are also scaled.…”

Section: Model Customisationmentioning

confidence: 99%

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

et al. 2020

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Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.

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Simultaneous identification of human body model parameters and gait trajectory from 3D motion capture data

Cited by 17 publications

References 33 publications

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

[Retracted] Study on 3D Clothing Color Application Based on Deep Learning‐Enabled Macro‐Micro Adversarial Network and Human Body Modeling

Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

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