Design and application research of a flexible array plantar sensor based on P(VDF-TrFE)/SnO₂NP_S/GR for Parkinson’s disease diagnosis

ABSTRACT

This paper introduces a flexible array Plantar sensor fabricated through the high-voltage electrospinning process of poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) ，incorporating stannic oxide nanoparticles （SnO2NPS）and graphene (GR) composite nanofilm. The surface morphology, β-phase crystal content, piezoelectric performance, composition, and structure of the composite piezoelectric films were evaluated using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, and a vibration platform. Experimental findings reveal that P(VDF-TrFE)/SnO2NPS/GR composite films containing 5% SnO2NPS and 0.1% GR exhibit superior open-circuit voltage and short-circuit current, measuring 22.43 V and 12.95 uA, respectively. These values are approximately 1.59 times and 1.34 times higher than those of the 5% P(VDF-TrFE) composite film and about 2.37 and 2.16 times higher than those of pure P(VDF-TrFE). A flexible piezoelectric sensor was fabricated using this composite film, and the mechanical properties and electrical impedance behavior of the sensor were investigated and analyzed.A multi-channel foot pressure collection and classification system was established based on this sensor, and a Parkinson’s disease machine learning model for multi-channel foot pressure collection was investigated. Various machine learning models for Parkinson’s disease were compared, and a fine K Nearest Neighbors（KNN） Parkinson’s disease classification model with an accuracy of 97.1% was proposed. This offers a novel solution for Parkinson’s disease diagnosis and holds significant reference value.

Graphical abstract

KEYWORDS:

• Flexible array sensor
• high-voltage electrospinning
• KNN algorithm
• nanoelectrospun piezoelectric film
• P(VDF-TrFE)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgments

The experimental procedures involving human subjects in the manuscript were conducted in strict accordance with the ethical principles outlined in the 1964 Helsinki Declaration (revised in 2013). Prior to their involvement, each participant was fully informed about the study and provided their informed consent.The authors would like to express their sincere appreciation for the invaluable participation of every individual who contributed to the research presented in this article.

Additional information

Funding

The authors would like to express their gratitude to the Department of Key Laboratory of Electronic Information Materials and Devices at Hangzhou DIANZI University for providing the scanning electron microscope and X-ray diffractometer equipment used in this study.

Notes on contributors

Yi Luo

Yi Luo (1976), Male, Master’s supervisor at the School of Electronics and Information Engineering, Hangzhou DIANZI University. Currently pursuing a Ph.D., specializing in the application of sensors and embedded systems, [email protected]

Peinan Su

Peinan Su (2001), Male, currently a master’s graduate student in the School of Communication Engineering, Hangzhou DIANZI University, specializing in piezoelectric sensors and their applications,[email protected]

Ying Wu

Ying Wu (1978), Female, Master’s degree in Computer Science and Technology, currently holding the position of Deputy Dean in the Academic Affairs Office at Hangzhou DIANZI University, [email protected]

Zhidong Zhao

Zhidong Zhao (1976), Male, Professor, Ph.D. supervisor at Hangzhou DIANZI University. Obtained a Ph.D. in Biomedical Engineering from Zhejiang University in 2004. Main research areas include biomedical signal processing and medical artificial intelligence technologies, [email protected]