Lee Jong Hwa, Asakawa Deanna S, Dennerlein Jack T, Jindrich Devin L
Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, United States of America.
Department of Kinesiology, California State University, San Marcos, California, United States of America.
PLoS One. 2015 Apr 8;10(4):e0121712. doi: 10.1371/journal.pone.0121712. eCollection 2015.
We determined muscle attachment points for the index, middle, ring and little fingers in an OpenSim upper-extremity model. Attachment points were selected to match both experimentally measured locations and mechanical function (moment arms). Although experimental measurements of finger muscle attachments have been made, models differ from specimens in many respects such as bone segment ratio, joint kinematics and coordinate system. Likewise, moment arms are not available for all intrinsic finger muscles. Therefore, it was necessary to scale and translate muscle attachments from one experimental or model environment to another while preserving mechanical function. We used a two-step process. First, we estimated muscle function by calculating moment arms for all intrinsic and extrinsic muscles using the partial velocity method. Second, optimization using Simulated Annealing and Hooke-Jeeves algorithms found muscle-tendon paths that minimized root mean square (RMS) differences between experimental and modeled moment arms. The partial velocity method resulted in variance accounted for (VAF) between measured and calculated moment arms of 75.5% on average (range from 48.5% to 99.5%) for intrinsic and extrinsic index finger muscles where measured data were available. RMS error between experimental and optimized values was within one standard deviation (S.D) of measured moment arm (mean RMS error = 1.5 mm < measured S.D = 2.5 mm). Validation of both steps of the technique allowed for estimation of muscle attachment points for muscles whose moment arms have not been measured. Differences between modeled and experimentally measured muscle attachments, averaged over all finger joints, were less than 4.9 mm (within 7.1% of the average length of the muscle-tendon paths). The resulting non-proprietary musculoskeletal model of the human fingers could be useful for many applications, including better understanding of complex multi-touch and gestural movements.
我们在OpenSim上肢模型中确定了食指、中指、无名指和小指的肌肉附着点。选择附着点以匹配实验测量位置和力学功能（力臂）。尽管已经对手指肌肉附着进行了实验测量，但模型在许多方面与标本不同，如骨段比例、关节运动学和坐标系。同样，并非所有手指固有肌都有可用的力臂。因此，有必要在保留力学功能的同时，将肌肉附着点从一个实验或模型环境缩放并转换到另一个环境。我们采用了两步法。首先，我们使用部分速度法通过计算所有固有肌和外在肌的力臂来估计肌肉功能。其次，使用模拟退火算法和胡克-吉夫斯算法进行优化，找到了使实验和模型力臂之间的均方根（RMS）差异最小化的肌腱路径。对于有测量数据的食指固有肌和外在肌，部分速度法导致测量和计算力臂之间的方差解释率（VAF）平均为75.5%（范围为48.5%至99.5%）。实验值和优化值之间的RMS误差在测量力臂的一个标准差（S.D）之内（平均RMS误差 = 1.5毫米 < 测量S.D = 2.5毫米）。该技术两个步骤的验证使得能够估计其力臂未被测量的肌肉的附着点。在所有手指关节上平均，模型和实验测量的肌肉附着之间的差异小于4.9毫米（在肌腱路径平均长度的7.1%以内）。由此产生的人类手指非专有肌肉骨骼模型可用于许多应用，包括更好地理解复杂的多点触摸和手势动作。
