Hernandez Vincent, Venture Gentiane, Rezzoug Nasser, Gorce Philippe
HandiBio - EA 4322 - University of Toulon, France.
GVLAB - University of Agriculture and Technology of Tokyo, Tokyo, Japan.
J Biomech. 2017 May 24;57:131-135. doi: 10.1016/j.jbiomech.2017.03.021. Epub 2017 Apr 4.
In order to improve the evaluation of the force feasible set (FFS) of the upper-limb which is of great interest in the biomechanics field, this study proposes two additional techniques. The first one is based on the identification of the maximal isometric force (MIF) of Hill-based muscles models from sEMG and isometric force measurements at the hand. The second one considers muscles cocontraction. The FFS was computed with an upper-limb musculoskeletal model in three different cases. The first one (M1) considered binary muscular activation and a simple MIF scaling method based on the weight and muscle length of the subject. The second one (M2) introduces cocontraction factors determined from sEMG. The third one (M3) considers the cocontraction factors and the MIF identification. Finally, M1, M2 and M3 are compared with end-effector force measurement. M3 outperforms the two other methods on FFS prediction demonstrating the validity and the usefulness of MIF identification and the consideration of the cocontraction factors.
为了改进对上肢力可行集（FFS）的评估，这在生物力学领域具有重要意义，本研究提出了两种额外的技术。第一种基于从表面肌电图（sEMG）和手部等长力测量中识别基于希尔模型的肌肉最大等长力（MIF）。第二种考虑肌肉协同收缩。在三种不同情况下，使用上肢肌肉骨骼模型计算FFS。第一种情况（M1）考虑二元肌肉激活以及基于受试者体重和肌肉长度的简单MIF缩放方法。第二种情况（M2）引入了根据sEMG确定的协同收缩因子。第三种情况（M3）考虑了协同收缩因子和MIF识别。最后，将M1、M2和M3与末端执行器力测量进行比较。在FFS预测方面，M3优于其他两种方法，证明了MIF识别以及考虑协同收缩因子的有效性和实用性。
