Demircan Emel, Khatib Oussama, Wheeler Jason, Delp Scott
Mechanical Engineering Department, Stanford University, Stanford, CA 94305, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6534-7. doi: 10.1109/IEMBS.2009.5333148.
In this paper we present methods to track and characterize human dynamic skills using motion capture and electromographic sensing. These methods are based on task-space control to obtain the joint kinematics and extract the key physiological parameters and on computed muscle control to solve the muscle force distribution problem. We also present a dynamic control and analysis framework that integrates these metrics for the purpose of reconstructing and analyzing sports motions in real-time.
在本文中，我们提出了利用动作捕捉和肌电图传感来跟踪和表征人类动态技能的方法。这些方法基于任务空间控制以获取关节运动学并提取关键生理参数，以及基于计算肌肉控制来解决肌肉力分布问题。我们还提出了一个动态控制和分析框架，该框架整合这些指标以实时重建和分析运动动作。
