物理学报2026，Vol.75Issue(10)：25-37,13.DOI:10.7498/aps.75.20251798
基于CTS/CNTs-OH的高性能织物压力传感器及其人体动作的深度学习识别
A high-performance CTS/CNTs-OH fabric pressure sensor and its deep learning-based recognition of human motions
摘要
Abstract
Flexible piezoresistive pressure sensors integrating high sensitivity,broad linear detection range,and excellent stability are crucial for wearable electronics and human-machine interfaces.However,achieving a balanced improvement in these performance metrics remains a challenge.Herein,we propose and fabricate a high-performance flexible pressure sensor based on a chitosan/hydroxylated carbon nanotubes(CTS/CNTs-OH)composite fabric.Benefiting from the inherent antibacterial properties of chitosan and the synergistic effect with hydroxylated carbon nanotubes,the composite fabric and the sensor exhibit excellent antibacterial performance,which can effectively avoid sensor performance degradation and human skin discomfort caused by microbial growth in wearable scenarios.In addition,the chitosan-based fibrous network endows the sensor with good biocompatibility,breathability,and mechanical compliance,making it suitable for long-term skin-contact wearable applications.By optimizing the electrophoretic deposition process and the fabric layering structure,the sensor demonstrates outstanding overall performance:a broad detection range of up to 100 kPa,a maximum sensitivity of 0.151 kPa-1,and excellent linearity(R2>0.999)across a wide pressure range.Notably,the multilayer fabric architecture enables a continuous evolution of interfacial contact under external pressure,which contributes to the simultaneous achievement of high sensitivity and a wide linear sensing range.Under dynamic pressure testing,the sensor exhibits fast response(6 ms)and recovery(46 ms)times,and it is capable of detecting subtle pressures as low as 0.98 Pa.Such rapid response characteristics allow the sensor to accurately capture both static and dynamic mechanical stimuli associated with human motions.The sensor maintains highly stable electrical output after more than 8000 loading-unloading cycles at 100 kPa,confirming its remarkable mechanical durability.For practical demonstrations,relying on its excellent antibacterial and sensing properties,the sensor was successfully employed for real-time monitoring of various human motions.Combined with a deep learning model based on a convolutional neural network(CNN),it achieved high-accuracy classification of eight types of human activities,with an overall recognition accuracy exceeding 99%.These results highlight the strong compatibility between the proposed sensor and data-driven intelligent recognition algorithms.This work provides an effective material and structural design strategy for developing high-performance,wearable flexible sensing systems,especially suitable for the wearable electronics field requiring biocompatibility and antibacterial properties.关键词
柔性压阻式压力传感器/壳聚糖/羟基化碳纳米管/织物传感器/人体动作识别/卷积神经网络Key words
flexible piezoresistive pressure sensor/chitosan/hydroxylated carbon nanotubes/textile sensor/human motion recognition/convolutional neural network引用本文复制引用
胡熙,赵彬喆,唐子渊,王磊,杨春雷,陈明..基于CTS/CNTs-OH的高性能织物压力传感器及其人体动作的深度学习识别[J].物理学报,2026,75(10):25-37,13.基金项目
国家自然科学基金(批准号:52573287)、深圳市科技计划(批准号:GJHZ20240218112501002,RCYX20231211090209016,JCYJ20241202125007009)、广东省青年英才计划(批准号:2023TQ07A142)、中国科学院青年创新促进会(批准号:2023375)和中国科学院国际伙伴计划(批准号:321GJHZ2024091FN)资助的课题. Project supported by the National Natural Science Foundation of China(Grant No.52573287),the Science and Technology Program of Shenzhen,China(Grant Nos.GJHZ20240218112501002,RCYX20231211090209016,JCYJ20241202125007009),the Youth Talent Program of Guangdong Province,China(Grant No.2023TQ07A142),the Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.2023375),and the International Partnership Program of the Chinese Academy of Sciences(Grant No.321GJHZ2024091FN). （批准号:52573287）
