Flexible resistive sensors based on crack structures have been widely studied in the field of electronic skin and health monitoring. However, it is still a challenge to develop resistive sensors with strong interface bonding forces due to the problem that the sensitive layer of traditional cracked sensors falls off easily. This paper reports a new type of graphene-based resistive sensor, which uses modified poly(sodium 4-phenylene sulfonate) and graphene nanosheets to prepare a highly conductive, fragile, film sensitive layer. Siloxane at the end of modified poly(sodium 4-phenylene sulfonate) is used to enhance the interface between the sensitive layer and the silicone rubber layer to prepare a flexible resistive sensor with a crack structure. The sensor has a high sensitivity (gauge factor up to 21980.28) and a high response speed (∼65 ms), with an effective working range of 0–4.7 % strain. The sensor has been successfully applied to human physiological signal detection. The neural network algorithm successfully predicted the corresponding blood pressure based on the pulse wave signal detected by the sensor. The average error of systolic blood pressure and diastolic blood pressure was 0.79 and 0.42 mmHg, and the error of ± 5 mmHg accounted for 91.2 % and 94.3 % of the total, respectively. The error value passed the Association for the Advancement of Medical Instrumentation standard, and reached the Class A standard of British Hypertension Society.

基于裂纹结构的柔性电阻式传感器在电子皮肤和健康监测领域得到了广泛的研究。然而，由于传统破裂传感器的敏感层容易脱落的问题，开发具有强界面结合力的电阻式传感器仍然是一个挑战。本文报道了一种新型石墨烯基电阻传感器，它使用改性聚（4-亚苯基磺酸钠）和石墨烯纳米片制备高导电、易碎的薄膜敏感层。改性聚（4-亚苯基磺酸钠）末端的硅氧烷用于增强敏感层与硅橡胶之间的界面层制备具有裂纹结构的柔性电阻传感器。该传感器具有高灵敏度（应变系数高达 21980.28）和高响应速度（~65 ms），有效工作范围为 0–4.7% 应变。该传感器已成功应用于人体生理信号检测。神经网络算法成功地根据传感器检测到的脉搏波信号预测出相应的血压值。收缩压和舒张压的平均误差分别为0.79和0.42 mmHg，±5 mmHg的误差分别占总数的91.2%和94.3%。误差值通过医疗仪器促进协会标准，达到英国高血压学会A级标准。