基于微结构织物压力传感器的人机界面力肌图(FMG)臂带

IF 3.5
Rayane Tchantchane, Hao Zhou, Shen Zhang, Gursel Alici
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引用次数: 0

摘要

由于其灵活性、灵敏度、低功耗和便携性,用于特定应用的可穿戴压力传感器的需求不断增长。具有微结构介电层的柔性电容压力传感器有望通过调整介电几何形状和材料特性来满足这些需求。基于有限元法(FEM)的有限元分析(Finite Element Analysis, FEA)能够预测传感器在各种输入和参数下的响应,从而为传感器的设计和开发提供方便。通过有限元分析,可以对压力传感器的性能进行基于微观结构的预测。提出了一种基于织物的电容式压力传感器,在介电层中增加了三角形棱镜微结构,灵敏度比非结构的传感器提高了4个数量级。该传感器灵敏度为5.52% kPa−¹(0.24-50 kPa),线性关系良好(R2 = 0.981),检测范围宽(0.24-330 kPa),机械稳定性为1000次循环。它的使用在一个四通道力肌图(FMG)臂章中得到了证明,在五名受试者中进行了验证,平均手势识别准确率为92%。该设备的应用进一步演示了控制假手和操作游戏,为HMI应用中智能可穿戴技术的进步铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Force Myography (FMG) Armband Based on Micro-Structured Textile-Pressure Sensors for Human-Machine Interface (HMI)

A Force Myography (FMG) Armband Based on Micro-Structured Textile-Pressure Sensors for Human-Machine Interface (HMI)

A Force Myography (FMG) Armband Based on Micro-Structured Textile-Pressure Sensors for Human-Machine Interface (HMI)

A Force Myography (FMG) Armband Based on Micro-Structured Textile-Pressure Sensors for Human-Machine Interface (HMI)

Wearable pressure sensors for specific applications are in growing demand due to their flexibility, sensitivity, low power consumption, and portability. Flexible capacitive pressure sensors with micro-structured dielectric layers have shown promise in meeting these demands by tuning the dielectric geometry and material properties. Finite Element Analysis (FEA) based on Finite Element Method (FEM) predicts the response of a sensor under various inputs and parameters and hence facilitates the design and development of sensors. By employing FEA, the performance pressure sensors can be predicted based on microstructures. A textile-based capacitive pressure sensor is presented, enhanced with a triangular prism micro-structure in the dielectric layer, improving sensitivity by up to four orders higher than its non-structured counterparts. The sensor demonstrates a remarkable sensitivity of 5.52% kPa¹(0.24–50 kPa), with linearity (R2 = 0.981), a wide sensing range (0.24–330 kPa), and mechanical stability >1000 cycles. Its use is demonstrated in a 4-channel force myography (FMG) armband, validated across five subjects with an average gesture recognition accuracy of 92% for common hand gestures. The applications of the device are further demonstrated to control a prosthetic hand and operate a game, paving the way for advancements in smart wearable technologies within HMI applications.

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