Werling Keenon, Bianco Nicholas A, Raitor Michael, Stingel Jon, Hicks Jennifer L, Collins Steven H, Delp Scott L, Liu C Karen
Department of Computer Science, Stanford University, Stanford, California.
Department of Mechanical Engineering, Stanford University, Stanford, California.
bioRxiv. 2023 Sep 8:2023.06.15.545116. doi: 10.1101/2023.06.15.545116.
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 subjecťs 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 , and residual force magnitudes smaller than 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.
创建大规模的人体运动生物力学公共数据集，可能会在我们对人体运动、神经肌肉疾病和辅助设备的理解方面带来数据驱动的突破。然而，目前处理运动捕捉数据以及量化运动的运动学和动力学所需的人工成本很高，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，用于自动且标准化地从运动捕捉数据中量化人体运动动力学。我们先使用线性方法，然后进行非凸双层优化，以缩放肌肉骨骼模型的身体节段，将放置在实验对象身上的光学标记的位置与肌肉骨骼模型上的标记对齐，并根据运动过程中实验标记的轨迹计算身体节段运动学。然后，我们再应用一种线性方法，接着进行另一个非凸优化，以找到身体节段的质量，并微调运动学，以便在给定地面反作用力的相应轨迹时最小化残余力。与人类专家大约一天的人工工作相比，这种优化方法确定一个受试者的骨骼尺寸和运动运动学大约需要3 - 5分钟，确定动态一致的骨骼惯性属性以及微调运动学和动力学所需的计算时间不到30分钟。我们使用AddBiomechanics从先前发表的多活动数据集中自动重建关节角度和扭矩轨迹，与专家计算值实现了紧密对应，标记均方根误差小于 ，残余力大小小于峰值外力的 。最后，我们证实AddBiomechanics能够从合成步行数据中准确再现关节运动学和动力学，标记误差和残余负荷较低。我们已将该算法作为开源云服务发布在AddBiomechanics.org上，该服务免费提供，并要求用户同意与社区共享处理后且已去除身份标识的数据文件。截至撰写本文时，数百名研究人员已使用该原型工具处理并共享了来自约一千名实验对象的约一万个运动文件。降低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先进的生物力学分析，并共享更大、更准确的数据集。
