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首页> 外文会议>IEEE Annual Conference on Decision and Control >Tremor Quantification through Data-driven Nonlinear System Modeling
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Tremor Quantification through Data-driven Nonlinear System Modeling

机译：通过数据驱动的非线性系统建模震颤量化

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摘要

Tremor is a repetitive uncontrollable movement of a body part that represents a cardinal symptom in several widespread neurological diseases. Being often related to a life-long incurable condition, tremor has to be monitored in order to follow disease status and adjust the therapy. This paper proposes a novel method of tremor quantification by modeling the repetitive movement underlying tremor as a periodic solution to an autonomous nonlinear system whose parameters are estimated from the data. A 3D fused acceleration measurement in a smart phone is utilized for data acquisition. The measured trajectory is seen as a superposition of a slow voluntary translational-rotational movement and a involuntary repetitive component produced by autonomous nonlinear dynamics. The repetitive part of the data is extracted by an event-based procedure relying on filtering and scaling of a position estimate obtained from the measurements. A planar representation of the underlying dynamics is used for estimating the parameters of a second-order system with a polynomial nonlinearity. Then, the tremor amplitude is captured by the scaling coefficients while the frequency content is defined by the nonlinear dynamical model. The efficacy of the proposed quantification approach is demonstrated on experimental data.
机译：震颤是一种重复的身体部位的无法控制运动，代表了几种广泛的神经疾病中的基本症状。经常与终身可现力的情况有关，必须监测震颤以遵循疾病状态并调整治疗。本文提出了一种通过将震颤的重复运动建模为周期性解决方案来提出了一种新的震颤量化方法，作为自主非线性系统的估计的自主非线性系统。智能手机中的3D熔融加速度测量用于数据采集。测量的轨迹被视为慢自愿平移 - 旋转运动的叠加和自主非线性动力学产生的非自愿重复组件。数据的重复部分是通过基于事件的过程提取，依赖于从测量中获得的位置估计的滤波和缩放。基础动态的平面表示用于估计具有多项式非线性的二阶系统的参数。然后，在由非线性动力学模型定义频率内容的同时由缩放系数捕获颤振幅度。在实验数据上证明了所提出的量化方法的功效。

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