Screen-Printed Highly Sensitive and Anisotropic Strain Sensors With Asymmetrical Inner Concave Honeycomb Cross-Conducting Structure for Health Monitoring of Medical Electrophysiological Signals

摘要

Flexible wearable strain sensors show great potential in fields, such as distributed flexible electronics, intelligent sensing robots, and small wearable physiological signal monitoring systems. Nevertheless, strain sensors made of low-cost materials can only sense strain in a single direction, while lacking the ability to identify strain direction and sense multiple directions. Furthermore, high sensitivity in a wide sensing range is required for the detection of electrophysiological signals from microskin surface deformations in human health monitoring. To overcome this key challenge, we propose a flexible polyamide (PA)/silver nanowire strain sensor with an asymmetric concave honeycomb cross-conducting network structure (ACHCN-structure). Through structure design optimization and screen printing techniques, it achieves multidimensional strain direction recognition and high sensitivity over a wide sensing range. It is shown that the sensor can achieve a strain gauge factor (GF) of 102735.17 and 78% wide sensing range response and efficient identification of different velocity frequencies. The relative electrical resistance change curve remains continuously stable over 2500 strain stretch release cycles. The sensor uses a power of only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.2814 \mu \text{W}$ </tex-math></inline-formula> at an operating voltage of 0.001 V. In addition, we combine flexible polyamide/silver nanowire strain sensors and 3D convolutional deep learning algorithms together to form a novel wearable voice interface platform (NWVIP). Through training tests, NWVIP has an accuracy rate of 83.25% and can effectively recognize different words vocalized or throat physiological motions. Finally, the sensor is used for motion detection during human arm, elbow and leg movements, and health monitoring during throat and pulse.