This study is aimed at exploring a technology that can use the human physiological information, such as Force Myography (FMG) signals to provide sensory feedback to prosthetic hand users. This is based on the principle that with the intent to move the prosthetic hand, the existing limbs in the arm recruit specific group of muscles. These muscles react with a change in the cross-sectional area; piezoelectric sensors placed on these muscles will generate a voltage (FMG signals), in response to the change in muscle volume. The correlation between the amplitude of the FMG signals and intensity of pressure on fingertips during grasping is then computed and a dynamic relation (model) is established through system identification in MATLAB. The estimated models generated a fitting accuracy of more than 80%. The model is then programmed into the Arduino microcontroller, so that a real-time and proportional force feedback is channeled to amputees through a micro actuator. Obtaining such percentages of accuracy in sensory feedback without relying on touch sensors on the prosthetic hand that could be affected by mechanical wear and other interaction factors is promising. Applying advanced signal processing and classification techniques may also refine the findings to better capture and correlate the force variations with the sensory feedback.

中文翻译：

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前臂FMG信号在闭环模态匹配的感觉反馈刺激中的应用

这项研究的目的是探索一种可以使用人类生理信息的技术，例如力肌成像（FMG）信号，以向假肢使用者提供感觉反馈。这是基于以下原则：为了移动假手，手臂中现有的四肢会吸收特定的肌肉群。这些肌肉会随着横截面积的变化而发生反应。响应于肌肉体积的变化，放置在这些肌肉上的压电传感器将产生电压（FMG信号）。然后计算FMG信号的振幅与抓握过程中指尖压力强度之间的相关性，并通过MATLAB中的系统识别建立动态关系（模型）。估计的模型产生的拟合精度超过80％。然后将模型编程到Arduino微控制器中，以便通过微型执行器将实时和成比例的力反馈传递给截肢者。在不依赖于可能受到机械磨损和其他相互作用因素影响的假肢上的触摸传感器的情况下，获得这样的百分比的感觉反馈准确性是有希望的。应用先进的信号处理和分类技术还可以完善发现，以更好地捕获和将力的变化与感觉反馈相关联。