Tremor frequency based filter to extract voluntary movement of patients with essential tremor
Tremor frequency based filter to extract voluntary movement of patients with essential tremor
复制标题
基于震颤频率的滤波器提取特发性震颤患者的随意运动
DOI：
10.1109/biorob.2012.6290905
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
M. G. Fujie
中科院分区：
文献类型：
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作者：
Y. Matsumoto;M. Seki;T. Ando;Y. Kobayashi;H. Iijima;M. Nagaoka;M. G. Fujie
特发性震颤（ET）是指身体某个部位的不自主振动。 ET 患者在进行日常生活活动时面临严重困难。我们的动机是开发一个系统，使 ET 患者能够进行日常生活活动；因此，我们一直在为 ET 患者开发肌电控制的外骨骼机器人。然而，ET患者的肌电信号不仅包含随意运动信号，还包含震颤信号。因此，为了正确控制该机器人，必须从 ET 患者的肌电信号中去除颤抖信号。迄今为止，我们一直在开发一种过滤器来消除颤抖信号，这在这方面非常有效。然而，在进行随意运动和保持姿势时都会产生震颤信号，并且滤波器最终会衰减这两个信号。但是，为了准确地控制该机器人，在执行随意运动期间产生的信号预计不会被衰减。因此，在本文中，我们提出了一种仅衰减维持姿势期间产生的震颤信号的方法。为了实现这一目标，我们重点关注震动信号的频率。通过实验，我们确认了震颤信号的频率根据患者的运动状态而变化的特性。然后，我们使用频率作为开关，通过设置阈值来激活先前提出的滤波器。作为评估，将所提出的方法处理的信号输入到时延神经网络。由于减少了随意运动期间的衰减，所提出的方法成功地部分提高了识别率。然而，所提出的方法在震动信号频率变化很大的情况下无法识别。作为未来的工作，我们将回顾计算震颤信号频率并提高识别能力的方法。
Essential Tremor (ET) refers to involuntary oscillations of a part of the body. ET patients face serious difficulties in performing daily living activities. Our motivation is to develop a system that can enable ET patients to perform their daily living activities; hence we have been developing a myoelectric controlled exoskeletal robot for ET patients. However, the EMG signal of ET patients contains not only voluntary movement signals but also tremor signals. Accordingly, to control this robot correctly, tremor signals must be removed from the EMG signal of ET patients. To date, we have been developing a filter to remove tremor signals, which has been largely effective in this. However, tremor signals are generated both while voluntary movement is being performed and while a posture is being maintained, and the filter ended up attenuating both these signals. But, to control this robot accurately, the signal generated during performance of voluntary movement is expected not to be attenuated. Therefore, in this paper, we propose a method that attenuates only tremor signals arising during maintenance of a posture. To accomplish this objective, we focus on the frequency of tremor signals. From the experiment, we confirmed the characteristic that the frequency of tremor signals changed depending on the state of the patient's movement. We then used frequency as a switch to activate the previously proposed filter by setting a threshold. As an evaluation, signals processed by the proposed method were input to a time delay neural network. The proposed method succeeded in partly improving recognition due to reduction of attenuation during performance of voluntary movement. However, the proposed method failed recognition in cases where the frequency of tremor signals varied widely. As a future work we will review the method to calculate the frequency of tremor signals and improve recognition.
智能躯干紧身胸衣支持癌症骨转移患者翻身
DOI：
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发表时间：
2010
期刊：
IEEE/ASME Transactions on Mechatronics
影响因子：
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作者：
Takeshi Ando;You Kobayashi;Jun Okamoto;Mitsuru Takahashi;Masakatsu G.Fujie
通讯作者：
Masakatsu G.Fujie
DOI：
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发表时间：
2008
期刊：
影响因子：
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作者：
Takeshi ANDO;Jun Okamoto;Masakatsu G. Fujie
通讯作者：
Masakatsu G. Fujie