J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
J Biomech. 2023 Sep;158:111764. doi: 10.1016/j.jbiomech.2023.111764. Epub 2023 Aug 9.
Obtaining large biomechanical datasets for machine learning is an ongoing challenge. Physics-based simulations offer one approach for generating large datasets, but many simulation methods, such as computed muscle control (CMC), are computationally costly. In contrast, interpolation methods, such as inverse distance weighting (IDW), are computationally fast. We examined whether IDW is a low-cost and accurate approach for interpolating muscle activations from CMC.IDW was evaluated using lateral pinch simulations in OpenSim. Simulated pinch data were organized into grids of varying sparsity (high, medium, and low density), where each grid point represented the muscle activations associated with a unique combination of mass and height of a young adult. For each grid, muscle activations were calculated via CMC and IDW for 108 random mass-height pairs that were not coincident with simulation grid vertices. We evaluated the interpolation errors from IDW for each grid, as well as the sensitivity of lateral pinch force to these errors. The root mean square error (RMSE) associated with interpolated muscle activations decreased with increasing grid density and never exceeded 4%. While CMC received a target thumb-tip force of 40 N, errors from the interpolated muscle activations never impacted the simulated force magnitude by more than 0.1 N. Furthermore, the computation time for CMC simulations averaged 4.22 core-minutes, while IDW averaged 0.95 core-seconds per mass-height pair.These results indicate IDW is a practical approach for rapidly estimating muscle activations from sparse CMC datasets. Future works could adapt our IDW approach to evaluate other tasks, biomechanical features, and/or populations.
获取用于机器学习的大型生物力学数据集是一个持续存在的挑战。基于物理的模拟为生成大型数据集提供了一种方法，但许多模拟方法（如计算肌肉控制（CMC））计算成本很高。相比之下，插值方法（如反向距离加权（IDW））计算速度很快。我们研究了 IDW 是否是一种从 CMC 中插值肌肉激活的低成本且准确的方法。IDW 使用 OpenSim 中的横向捏合模拟进行了评估。模拟捏合数据被组织成具有不同稀疏度（高密度、中密度和低密度）的网格，每个网格点代表与年轻成年人的质量和高度的唯一组合相关联的肌肉激活。对于每个网格，通过 CMC 和 IDW 计算了 108 个随机质量-高度对的肌肉激活，这些对不与模拟网格顶点重合。我们评估了每个网格的 IDW 插值误差，以及这些误差对横向捏合力的敏感性。与插值肌肉激活相关的均方根误差（RMSE）随着网格密度的增加而减小，并且从未超过 4%。当 CMC 接收目标拇指指尖力为 40 N 时，插值肌肉激活的误差从未超过模拟力大小的 0.1 N。此外，CMC 模拟的计算时间平均为 4.22 核分钟，而 IDW 平均为每个质量-高度对 0.95 核秒。这些结果表明，IDW 是一种从稀疏 CMC 数据集中快速估计肌肉激活的实用方法。未来的工作可以采用我们的 IDW 方法来评估其他任务、生物力学特征和/或人群。
