Goodman Sarah E, Hasson Christopher J
Neuromotor Systems Laboratory, Department of Bioengineering, Northeastern University, Boston, MA, United States.
Neuromotor Systems Laboratory, Department of Physical Therapy, Movement and Rehabilitation Sciences, Northeastern University, Boston, MA, United States.
Front Hum Neurosci. 2017 Nov 10;11:531. doi: 10.3389/fnhum.2017.00531. eCollection 2017.
There is an old saying that you must walk a mile in someone's shoes to truly understand them. This mini-review will synthesize and discuss recent research that attempts to make humans "walk a mile" in an artificial musculoskeletal system to gain insight into the principles governing human movement control. In this approach, electromyography (EMG) is used to sample human motor commands; these commands serve as inputs to mathematical models of muscular dynamics, which in turn act on a model of skeletal dynamics to produce a simulated motor action in real-time (i.e., the model's state is updated fast enough produce smooth motion without noticeable transitions; Manal et al., 2002). In this mini-review, these are termed myoelectric musculoskeletal models (MMMs). After a brief overview of typical MMM design and operation principles, the review will highlight how MMMs have been used for understanding human sensorimotor control and learning by evoking apparent alterations in a user's biomechanics, neural control, and sensory feedback experiences.
有句老话说,要真正理解一个人,你得穿上他的鞋子走一英里路。这篇小型综述将综合并讨论最近的研究,这些研究试图让人类在人工肌肉骨骼系统中“走一英里路”,以深入了解人类运动控制的原理。在这种方法中,肌电图(EMG)用于采集人类运动指令;这些指令作为肌肉动力学数学模型的输入,该模型进而作用于骨骼动力学模型以实时产生模拟运动动作(即,模型状态更新得足够快,以产生平滑运动且无明显过渡;马纳尔等人,2002年)。在这篇小型综述中,这些被称为肌电肌肉骨骼模型(MMM)。在简要概述典型的MMM设计和操作原理之后,该综述将重点介绍MMM如何通过引发用户生物力学、神经控制和感觉反馈体验的明显变化,用于理解人类感觉运动控制和学习。