Design and application research of a flexible array plantar sensor based on P(VDF-TrFE)/SnO2NPS/GR for Parkinson’s disease diagnosis - Luo, Yi; Su, Peinan; Wu, Ying; Zhao, Zhidong (Polymer-plastics technology and engineering, 2024)
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.