Department of Computer Engineering, Bahcesehir University, Istanbul, 34353, Turkey.
Department of Biomedical Engineering, Bahcesehir University, Istanbul, 34353, Turkey.
Sci Rep. 2023 Nov 20;13(1):20308. doi: 10.1038/s41598-023-47337-9.
Estimating the human center of mass (CoM) has long been recognized as a highly complex process. A relatively recent and noteworthy technique for CoM estimation that has gained popularity is the statically equivalent serial chain (SESC). This technique employs a remodeling of the human skeleton as a serial chain where the end effector represents the CoM location. In this study, we aimed to evaluate the impact of model complexity on the estimation capability of the SESC technique. To achieve this, we designed and rigorously assessed four distinct models with varying complexities against the static center of pressure (CoP) as reference, by quantifying both the root-mean-square (RMS) and correlation metrics. In addition, the Bland-Altman analysis was utilized to quantify the agreement between the estimations and reference values. The findings revealed that increasing the model complexity significantly improved CoM estimation quality up to a specific threshold. The maximum observed RMS difference among the models reached 9.85 mm. However, the application and task context should be considered, as less complex models still provided satisfactory estimation performance. In conclusion, the evaluation of model complexity demonstrated its impact on CoM estimation using the SESC technique, providing insights into the trade-off between accuracy and complexity in practical applications.
估算人体质心（CoM）一直被认为是一个高度复杂的过程。一种相对较新的、备受关注的 CoM 估计技术是静态等效串联链（SESC）。该技术将人体骨骼重塑为串联链，末端效应器代表 CoM 位置。在这项研究中，我们旨在评估模型复杂性对 SESC 技术估计能力的影响。为此，我们设计并严格评估了四个具有不同复杂性的不同模型，以静息状态下的压力中心（CoP）为参考，通过量化均方根（RMS）和相关度指标来实现这一目标。此外，还利用 Bland-Altman 分析来量化估计值与参考值之间的一致性。研究结果表明，增加模型复杂性可显著提高 CoM 估计质量，直到达到特定的阈值。在这些模型中，最大的 RMS 差异观察值达到 9.85 毫米。然而，应该考虑应用和任务背景，因为较简单的模型仍然提供了令人满意的估计性能。总之，该模型复杂性的评估展示了其对 SESC 技术中 CoM 估计的影响，为实际应用中准确性和复杂性之间的权衡提供了深入了解。
