A Smart Tactile Sensing System Based on Carbon Nanotube/Polypropylene Composites for Wearable Applications

被引:6
作者
Atoche-Ensenat, R. [1 ]
Perez, E. [1 ]
Hernandez-Benitez, A. [2 ]
Balam, A. [3 ]
Estrada-Lopez, Johan J. [4 ]
Vazquez-Castillo, J. [5 ]
Aviles, F. [3 ]
Castillo-Atoche, A. [2 ]
机构
[1] Tecnol Nacl Mexico, Elect Dept, Campus Merida, Merida 97118, Yucatan, Mexico
[2] Autonomous Univ Yucatan, Fac Engn, Merida 97203, Yucatan, Mexico
[3] Ctr Invest Cient Yucatan, Mat Dept, Merida 97205, Yucatan, Mexico
[4] Autonomous Univ Yucatan, Fac Math, Merida 97203, Yucatan, Mexico
[5] Univ Autonoma Estado Quintana Roo, Dept Informat & Networking, Chetmal 77019, Quintana Roo, Mexico
关键词
Sensors; Tactile sensors; Nanocomposites; Intelligent sensors; Wearable sensors; Sensor systems; Piezoresistance; Carbon nanotube (CNT)/polypropylene (PP) composite; intelligent tactile classification; piezoresistive tactile sensor; smart wearable sensor;
D O I
10.1109/JSEN.2022.3229232
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Recent advances in nanomaterials render the possibility of fabricating tactile sensors suitable for electronic skin, haptic interfaces, and biomedical applications. Furthermore, the problem of performing complex inertial measurements generated during the tactile process of polymer composites can be tackled using artificial intelligence (AI). However, the implementation of AI-based signal processing on embedded devices still represents an open area of opportunity for the design of the next generation of smart wearable sensor systems. A novel nanostructured smart tactile sensing system for wearable applications is proposed, using a 3-D-printed structure with embedded electronic devices. The highly sensitive piezoresistive tactile sensor is based on multiwall carbon nanotube/polypropylene (MWCNT/PP) composites. The integration of electronic circuits for signal processing of an artificial neural network (NN) on a digital controller unit improves the tactile interpretation in the wearable embedded device. Experiments show that the pressure classification results on MWCNT/PP composites with 98% accuracy.
引用
收藏
页码:2948 / 2955
页数:8
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