Enhancement for the nano-sensors by means of a creative adjustment on the underlying and actual attributes for intelligent artificial hand

Gongxing Yan, Jialing Li, Rania M. Ghoniem, Liang Yin, Abdullah Alnutayfat, Riadh Marzouki, Hamid Assilzadeh and José Escorcia-Gutierrez

Abstract

Pressure nanosensors are widely used in industry today. Cheap price, simple measurement circuit, and low energy consumption are the reasons for the widespread use of these sensors. The structure of these systems includes membranes, Wheatstone bridge circuits for measurement, and piezoresistor elements for use as the resistance, respectively. The development of intelligent artificial hands relies heavily on nano-sensor technology to provide precise sensory feedback and enhance user control. However, existing nano-sensors often face limitations in sensitivity, durability, and seamless integration with neural control systems, creating a gap in achieving lifelike prosthetic functionality. This study aims to creatively adjust both the underlying attributes (material composition, sensor architecture, signal processing) and the actual attributes (durability, real-world performance, compatibility) of nano-sensors to improve their efficiency in intelligent prosthetics significantly. The novelty lies in combining advanced nano-materials, structural optimization, and Artificial Intelligence (AI)-driven signal processing for multi-sensor fusion, an approach not fully explored in previous research. The study identifies key sensor limitations and enhances performance through graphene-based materials, structural redesign, and AI-driven signal optimization. Simulations and performance modeling assess expected gains in response time, sensitivity, and integration efficiency for next-generation artificial hands. Experimental and simulation results demonstrate a gauge factor improvement to 11.94, representing a 73.8 % increase over Carbon Nano Tube (CNT)-only films, with linearity maintained at Coefficient of Determination (R2) = 0.996. Electrical noise was reduced by 34 %, conductivity improved from 2.31

Key Words

AI-driven signal processing; Artificial Intelligence (AI); Artificial Neural Network (ANN); durability improvement; Genetic Algorithm (GA); Graphene–Carbon Nano Tube (G-CNT) Sensors; intelligent prosthetics; nano-sensor optimization; sensitivity enhancement

Address

Gongxing Yan: School of Architecture and Engineeringn, Xinjiang Applied Vocational and Technical College, Yili 8333200, Xinjiang, China Jialing Li: School of Artificial intellegence, Chongqing Youth Vocational & Technical College, Chongqing 401320, China Rania M. Ghoniem: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia Liang Yin: Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia Abdullah Alnutayfat: Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia Riadh Marzouki: Department of Chemistry, College of Science, King Khalid University, P.O. Box 9004, 61413 Abha, Saudi Arabia Hamid Assilzadeh: Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam/ School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam José Escorcia-Gutierrez: Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia

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