Preparation of PVDF films by electrospinning and their application in a human posture recognition system

This study prepared polyvinylidene fluoride/zinc oxide (PVDF/ZnO) piezoelectric films using electrospinning. The films were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD), and their performance was tested to determine the optimal ZnO doping ratio of 15 wt%. Sensor sensitive elements were created using screen printing, and a non-array sensor layout was designed, resulting in a tactile sensor cushion. A pressure data acquisition system was developed using a microcontroller to collect sitting posture data. A posture recognition system was built using a support vector machine (SVM) classification algorithm, achieving a recognition accuracy of 88.33% with different kernel functions. Finally, the pressure distribution on the sensor array was displayed through a graphical interface.

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Publication Details

Journal
IET conference proceedings.
Published
2025-01-01
DOI
https://doi.org/10.1049/icp.2024.2748
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
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article

Preparation of PVDF films by electrospinning and their application in a human posture recognition system

Guowei Gao, Dabiao Lu, Yansheng Li, Jingfang Hu et al.
IET conference proceedings.
Advanced Sensor and Energy Harvesting Materials
article

Preparation of PVDF films by electrospinning and their application in a human posture recognition system

Guowei Gao, Dabiao Lu, Yansheng Li, Jingfang Hu, Zhongxiao Lu
article en

Abstract

This study prepared polyvinylidene fluoride/zinc oxide (PVDF/ZnO) piezoelectric films using electrospinning. The films were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD), and their performance was tested to determine the optimal ZnO doping ratio of 15 wt%. Sensor sensitive elements were created using screen printing, and a non-array sensor layout was designed, resulting in a tactile sensor cushion. A pressure data acquisition system was developed using a microcontroller to collect sitting posture data. A posture recognition system was built using a support vector machine (SVM) classification algorithm, achieving a recognition accuracy of 88.33% with different kernel functions. Finally, the pressure distribution on the sensor array was displayed through a graphical interface.

IET conference proceedings.Vol. 2024(9)
Beijing Information Science & Technology University (CN)
Gender equality
Openalex Percentile: Top 100%
Advanced Sensor and Energy Harvesting Materials
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