Smart Wearable Research Group, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.
School of Electronics & Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Sensors (Basel). 2024 Oct 10;24(20):6513. doi: 10.3390/s24206513.
In this work, a flexible textile-based capacitive respiratory sensor, based on a capacitive sensor structure, that does not require direct skin contact is designed, optimised, and evaluated using both computational modelling and empirical measurements. In the computational study, the geometry of the sensor was examined. This analysis involved observing the capacitance and frequency variations using a cylindrical model that mimicked the human body. Four designs were selected which were then manufactured by screen printing multiple functional layers on top of a polyester/cotton fabric. The printed sensors were characterised to detect the performance against phantoms and impacts from artefacts, normally present whilst wearing the device. A sensor that has an electrode ratio of 1:3:1 (sensor, reflector, and ground) was shown to be the most sensitive design, as it exhibits the highest sensitivity of 6.2% frequency change when exposed to phantoms. To ensure the replicability of the sensors, several batches of identical sensors were developed and tested using the same physical parameters, which resulted in the same percentage frequency change. The sensor was further tested on volunteers, showing that the sensor measures respiration with 98.68% accuracy compared to manual breath counting.
在这项工作中，设计了一种基于电容传感器结构的、无需直接皮肤接触的柔性纺织电容式呼吸传感器，通过计算建模和实证测量对其进行了优化和评估。在计算研究中，对传感器的几何形状进行了检查。该分析涉及使用模拟人体的圆柱形模型观察电容和频率的变化。选择了四个设计，然后通过在聚酯/棉织物上丝网印刷多个功能层来制造印刷传感器。对印刷传感器进行了特征检测，以检测对佩戴设备时通常存在的假人和异物冲击的性能。具有电极比为 1:3:1（传感器、反射器和地）的传感器被证明是最敏感的设计，因为它在暴露于假人时表现出最高的 6.2%频率变化灵敏度。为了确保传感器的可重复性，使用相同的物理参数开发并测试了多个批次的相同传感器，结果得到了相同的频率变化百分比。该传感器进一步在志愿者身上进行了测试，结果表明该传感器与手动呼吸计数相比，测量呼吸的准确率为 98.68%。
