Nguyen Thanh-Hai, Ngo Ba-Viet, Nguyen Thanh-Nghia, Vu Chi Cuong
Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Technology and Education, 01 Vo Van Ngan Street, Linh Chieu Ward, Ho Chi Minh City 700000, Vietnam.
Micromachines (Basel). 2023 Jul 13;14(7):1411. doi: 10.3390/mi14071411.
Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and healthcare. However, most of these studies have focused on a specific area, such as fabrication, data analysis, or experimentation. This approach can lead to challenges regarding the reliability, accuracy, or connectivity of the components. Therefore, there is a pressing need to consider the sensor's placement in an overall system and find ways to maximize the efficiency of such flexible sensors. This paper proposes a fabrication method for soft capacitive pressure sensors with spacer fabric, conductive inks, and encapsulation glue. The sensor exhibits a good sensitivity of 0.04 kPa, a fast recovery time of 7 milliseconds, and stability of 10,000 cycles. We also evaluate how to connect the sensor to other traditional sensors or hardware components. Some machine learning models are applied to these built-in soft sensors. As expected, the embedded wearables achieve a high accuracy of 96% when recognizing human walking phases.
软传感器因其在实际项目中的多功能性而受到全球研究人员的广泛关注。软传感器在生活的诸多方面已有许多应用，包括人机接口、柔性电子、医疗监测和医疗保健。然而，这些研究大多集中在特定领域，如制造、数据分析或实验。这种方法可能会在组件的可靠性、准确性或连接性方面带来挑战。因此，迫切需要考虑传感器在整个系统中的放置位置，并找到方法来最大化此类柔性传感器的效率。本文提出了一种使用间隔织物、导电油墨和封装胶水制造软电容式压力传感器的方法。该传感器具有0.04 kPa的良好灵敏度、7毫秒的快速恢复时间和10000次循环的稳定性。我们还评估了如何将该传感器与其他传统传感器或硬件组件连接。一些机器学习模型被应用于这些内置的软传感器。不出所料，嵌入式可穿戴设备在识别人类行走阶段时实现了96%的高精度。
