Murai Akihiko, Kurosaki Kosuke, Yamane Katsu, Nakamura Yoshihiko
Department of Mechano-Informatics, University of Tokyo, 7-3-1, Hongo, bukyo-ku, Tokyo, 113-8656, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6546-9. doi: 10.1109/IEMBS.2009.5334504.
In this paper, we propose a method for realtime estimation of whole-body muscle tensions. The main problem of muscle tension estimation is that there are infinite number of solutions to realize a particular joint torque due to the actuation redundancy. Numerical optimization techniques, e.g. quadratic programming, are often employed to obtain a unique solution, but they are usually computationally expensive. For example, our implementation of quadratic programming takes about 0.17 sec per frame on the musculoskeletal model with 274 elements, which is far from realtime computation. Here, we propose to reduce the computational cost by using EMG data and by reducing the number of unknowns in the optimization. First, we compute the tensions of muscles with surface EMG data based on a biological muscle data, which is a very efficient process. We also assume that their synergists have the same activity levels and compute their tensions with the same model. Tensions of the remaining muscles are then computed using quadratic programming, but the number of unknowns is significantly reduced by assuming that the muscles in the same heteronymous group have the same activity level. The proposed method realizes realtime estimation and visualization of the whole-body muscle tensions that can be applied to sports training and rehabilitation.
在本文中，我们提出了一种用于实时估计全身肌肉张力的方法。肌肉张力估计的主要问题在于，由于驱动冗余，实现特定关节扭矩存在无数种解决方案。数值优化技术，例如二次规划，常被用于获得唯一解，但它们通常计算成本很高。例如，我们在具有274个元素的肌肉骨骼模型上实现的二次规划，每帧大约需要0.17秒，这远非实时计算。在此，我们提议通过使用肌电图（EMG）数据以及减少优化中的未知数数量来降低计算成本。首先，我们基于生物肌肉数据，利用表面肌电图数据计算肌肉张力，这是一个非常高效的过程。我们还假设其协同肌具有相同的活动水平，并使用相同的模型计算它们的张力。然后，使用二次规划计算其余肌肉的张力，但通过假设同一异名肌组中的肌肉具有相同的活动水平，未知数的数量显著减少。所提出的方法实现了全身肌肉张力的实时估计和可视化，可应用于运动训练和康复。
