利用肌肉骨骼模型为三维人体姿势、运动学、动力学和肌肉估算生成物理上一致的数据

IF 2.6 2区 工程技术 Q2 MECHANICS Multibody System Dynamics Pub Date : 2024-08-12 DOI:10.1007/s11044-024-10021-5
Ali Nasr, Kevin Zhu, John McPhee
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引用次数: 0

摘要

人体动作捕捉技术被广泛应用于娱乐、体育、医疗、增强现实、虚拟现实和机器人等行业。然而,运动捕捉数据只能让用户在运动学层面分析人体运动。为了研究相应的动力学和肌肉特性,还需要额外的传感器(如力板和肌电图传感器)来收集相关数据。从多个来源收集、处理和同步数据既费力又费时。本研究提出了一种生成现有运动捕捉数据集的动态和肌肉属性的方法。为此,我们的方法利用由 14 个关节、40 个自由度和 15 个节段组成的肌肉骨骼模型,通过运动学、动力学和肌肉建模重建运动。与当前的物理模拟器相比,我们的方法还能推断肌肉属性,确保人体模型逼真。我们的所有术语和表述都符合国际生物力学学会的标准。此外,我们的综合肌肉骨骼模型允许用户预先选择执行动作的人的各种人体测量特征,如身高、体重、运动水平、手型和皮肤温度,这些特征通常无法在没有适当注释的情况下从单目视频中估算出来。我们在 Human3.6M 数据集上应用了我们的方法,结果表明我们重建的运动在运动学上与地面真实标记相似,同时与文献中的实验数据相比,在动态上也是可信的。生成的数据(Human3.6M+)可供下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Using musculoskeletal models to generate physically-consistent data for 3D human pose, kinematic, dynamic, and muscle estimation

Human motion capture technology is utilized in many industries, including entertainment, sports, medicine, augmented reality, virtual reality, and robotics. However, motion capture data only allows the user to analyze human movement at a kinematic level. In order to study the corresponding dynamics and muscle properties, additional sensors such as force plates and electromyography sensors are needed to collect the relevant data. Collecting, processing, and synchronizing data from multiple sources could be laborious and time-consuming. This study proposes a method to generate the dynamics and muscle properties of existing motion capture datasets. To do so, our method reconstructs motions via kinematics, dynamics, and muscle modeling with a musculoskeletal model consisting of 14 joints, 40 degrees of freedom, and 15 segments. Compared to current physics simulators, our method also infers muscle properties to ensure our human model is realistic. We have met International Society of Biomechanics standards for all terminologies and representations. Furthermore, our integrated musculoskeletal model allows the user to preselect various anthropometric features of the human performing the motion, such as height, mass, level of athleticism, handedness, and skin temperature, which are often infeasible to estimate from monocular videos without appropriate annotations. We apply our method on the Human3.6M dataset and show that our reconstructed motion is kinematically similar to the ground truth markers while being dynamically plausible when compared to experimental data found in literature. The generated data (Human3.6M+) is available for download.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
期刊最新文献
Development of an identification method for the minimal set of inertial parameters of a multibody system Vibration transmission through the seated human body captured with a computationally efficient multibody model Data-driven inverse dynamics modeling using neural-networks and regression-based techniques Load torque estimation for cable failure detection in cable-driven parallel robots: a machine learning approach Mutual information-based feature selection for inverse mapping parameter updating of dynamical systems
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