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Prosthesis Control Using Spike Rate Coding in the Retina Photoreceptor Cells

机译:视网膜光摄影细胞中尖峰率编码的假体控制

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Volitional control of prostheses is most commonly achieved by myoelectric signalling. The electromyograph (EMG) is detected and processed by a controller, that decodes and relates the signal to the corresponding position of the prosthetic. Myoelectric signalling is limited in users by two factors: lack of nerve endings corresponding to the position of the amputation, and neurological damage resulting in poor signal control. Improved prosthesis control has been demonstrated by the addition of feedback sensors based on computer vision and inertial measurement units. Computer vision requires a significant level of processing, resulting in a high latency and high power usage. In this paper, we propose a means of overcoming this limitation by use of in-vivo retinal signalling to complement EMG for improved control. This is demonstrated using a real-time conductance-based simulator as the sole method of control for an upper-limb prosthesis. Input image streams are received by a camera and used to activate the combined rod and cone photoreceptor cell responses. This in turn generates a spike train which is counted and averaged over time, and passed to an Arduino-based control system which modulates the behavior of the prosthesis. We seek to use this system to lower the experimental barriers of in-vivo ganglion electrical signalling by presenting a way to use retina emulation. A link to the simulator is provided.
机译:肌电信号最常见的假体的性能控制是最常见的。通过控制器检测和处理电磁素(EMG),该控制器被解码并将信号与假体的相应位置相关联。通过两个因素的用户在用户中受到限制:缺乏对应于截肢的位置的神经结束,以及导致信号控制不良的神经损伤。通过基于计算机视觉和惯性测量单元的反馈传感器,已经证明了改进的假体控制。计算机视觉需要大量的处理水平,导致高延迟和高功率使用率。在本文中,我们提出了一种通过使用体内视网膜信号来克服这种限制来补充EMG以改善控制。这是使用基于实时电导的模拟器来证明作为上肢假体的唯一控制方法。输入图像流由相机接收并用于激活组合的杆和锥形光感受器电池响应。这又产生了一列尖峰列车,其随着时间的推移计算和平均,并传递给基于Arduino的控制系统,该系统调节假肢的行为。我们寻求使用该系统来降低体内神经节电信带的实验障碍,通过呈现使用视网膜仿真。提供了与模拟器的链接。

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