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基于Velostat压敏薄膜和柔性印刷电路板(FPC)设计制作了一种电阻式4×4柔性压力感知系统，并结合传感信号采集和处理、无线传输电路和反向传播(BP)人工神经网络学习方法，实现了压力图形信号的感知、监测和识别。结果表明，单个压力传感器单元在低压强范围(56.5～580 Pa)和高压强范围(580～960 Pa)内的灵敏度分别为0.315和1.13 kPa-1;动态响应时间和恢复时间分别约为0.75和1.29 s。该压力感知系统不仅可以实时监测压力分布图形，还可以通过压力图形数据集训练其三层BP人工神经网络，识别典型英文字母压力图形的正确率高达93%以上，在人机交互、健康监测和生物工程等领域具有应用潜力。
Abstract：A resistance-type 4×4 flexible pressure perception system was designed and fabricated based on Velostat pressure-sensitive films and flexible printed circuits(FPC). Combining with sensing signal acquisition, processing, wireless transmission circuits, and a back-propagation(BP) artificial neural network learning method, sensing, monitoring and recognizing of pressure graphic signals was realized. The results show that the sensitivities of a single pressure sensor unit in low pressure range(56.5-580 Pa) and high pressure range(580-960 Pa) are 0.315 and 1.13 kPa-1, respectively, and the dynamic response time and recovery time are about 0.75 and 1.29 s, respectively. The pressure perception system can not only monitor the pressure distribution graph in real time, but also train its three-layer BP artificial neural network through the pressure graph dataset. The correct rate of the pressure graph recognization of a typical English letter is over 93%, which has potential applications in fields such as human-computer interaction, health monitoring, and bioengineering.
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基本信息:
DOI：10.13250/j.cnki.wndz.25120403
中图分类号:TP183;TP212
引用信息:
[1]秦瑞欣,赵全亮,温宇彤,等.基于BP人工神经网络的柔性压力感知系统[J].微纳电子技术,2025,62(12):84-90.DOI:10.13250/j.cnki.wndz.25120403.
基金信息:
国家重点研发项目(2023YFB4705701)
2025-07-23
2025
2025-08-11
2025-08-18
2025
1
2025-11-18
2025-11-18
2025-11-18