Origins of low frequency components of physiological tremor

生理性震颤（PT）是一种需要在人体运动计划中考虑的不自主运动，特别是高度准确的手指运动所需的运动。虽然PT的高频振荡的起源是有据可查的，但低频分量的起源并不清楚。PT的这个“未知”分量是最重要的，因为其幅度代表PT幅度的90%以上。因此，目前的建议，努力确定使用四项研究的PT的低频成分的起源。具体而言，这些研究将确定EMG活动，运动单位活动，肌梭和反射弧活动，以及感觉方式对低频PT振荡特性的作用。这些范例的选择基于完善的文献和初步结果。了解PT的组成部分将阐明这些肢体振荡产生的基本神经机制和机械因素，从而帮助确定PT在神经运动计划中的重要性。这项研究将使我们能够提高我们对运动系统的认识，使这些新发现可以纳入运动控制的神经模型。此外，知道PT的低频振荡的起源（PT振幅的大部分位于其中）可以导致新的和改进的策略来抵消在精确运动期间（诸如在显微外科手术期间）这些振荡的滋扰。我们的长期目标是转移这些新知识，例如，在手术机器人中实施更好的过滤技术，以减少PT并提高准确性。最后，我们可以利用这些知识开发简单的设备，可以区分正常和异常的震颤，比如我们目前使用智能手机。

Physiological tremor (PT) is an involuntary movement that needs to be taken into account within the human motor scheme, especially those needed for highly accurate finger movements. Although the origins of the high frequency oscillations of PT are well documented; the origin of low frequency component is not as well understood. This "unknown" component of PT is of first importance since its amplitude represents more than 90% of PT amplitude. Thus, the current proposal strives to identify the origins of low frequency component of PT using four studies. Specifically, these studies will identify the role of EMG activity, motor unit activity, muscle spindle and reflex arc activity, and sensory modalities on the characteristics of low frequency PT oscillations. These paradigms were chosen based on well-establish literature and preliminary results. Understanding the components of PT will clarify the basic neural mechanisms and mechanical factors involved in the generation of these limb oscillations, thus helping determine the importance of PT within the neural motor scheme. This research will allow us to improve our knowledge of the motor system such that these new discoveries could be incorporated into neural models of motor control. Also, knowing the origin of low frequency oscillations of PT, where the majority of PT amplitude lies, could lead to new and improved strategies to counteract the nuisance of these oscillations during precise movements such as during microsurgical procedures. Our long-term goals is to transfer this new knowledge, for example, for the implementation of better filtering techniques in surgical robots to reduce PT and improve accuracy. Finally, we can use this knowledge to develop simple devices that can differentiate normal from abnormal tremors such as we are currently doing using smart phones.