AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.
AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization.
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AddBiomechanics：通过顺序优化，根据人体运动数据自动进行模型缩放、逆运动学和逆动力学。
DOI：
10.1101/2023.06.15.545116
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Liu,CKaren
中科院分区：
文献类型：
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作者：
Werling,Keenon;Bianco,NicholasA;Raitor,Michael;Stingel,Jon;Hicks,JenniferL;Collins,StevenH;Delp,ScottL;Liu,CKaren
创建大规模的人体运动生物力学公共数据集，可以为我们对人体运动、神经肌肉疾病和辅助设备的理解带来数据驱动的突破。然而，目前处理运动捕捉数据和量化运动的运动学和动力学所需的手工工作是昂贵的，并且限制了大规模生物力学数据集的收集和共享。我们提出了一种名为AddBiomechanics的方法，可以从动作捕捉数据中自动化和标准化人体运动动力学的量化。我们使用线性方法，然后采用非凸双层优化来缩放肌肉骨骼模型的身体部分，将放置在实验对象上的光学标记的位置注册到肌肉骨骼模型上的标记上，并在运动期间计算给定实验标记轨迹的身体部分运动学。然后，我们采用线性方法，然后采用另一种非凸优化方法来查找体段质量并微调运动学，以在给定相应的地面反作用力轨迹的情况下最小化残余力。优化方法需要大约3-5分钟来确定受试者的骨骼尺寸和运动运动学，并且需要不到30分钟的计算来确定动态一致的骨骼惯性特性和微调的运动学和动力学，而人类专家则需要大约一天的手工工作。我们使用AddBiomechanics从先前发布的多活动数据集中自动重建关节角度和扭矩轨迹，实现与专家计算值的密切对应，标记均方根误差小于2厘米，剩余力大小小于峰值外力的2%。最后，我们证实AddBiomechanics可以准确地从合成的步行数据中再现关节运动学和动力学，具有低标记误差和残余载荷。我们已经在AddBiomechanics.org上将该算法作为开源云服务发布，该服务免费提供，并要求用户同意与社区共享处理过的和去识别的数据。在撰写本文时，数百名研究人员已经使用原型工具来处理和分享来自大约1000个实验对象的大约10,000个运动文件。减少处理和共享高质量人体运动生物力学数据的障碍，将使更多人能够以更低的成本使用最先进的生物力学分析，并共享更大、更准确的数据集。
Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject’s skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.