近場電紡織堆疊3D鰭式多孔隙改質PVDF-TrFE壓電奈米感測器應用於智能聲場與步態足壓感測大數據分析;Three-Dimensional Stacked Near-Field Electrospun NanoPorous PVDF-TrFE Nanofibers as Self-Powered Smart Sensing in Acoustic and Gait Big Data Analytics

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题名:	近場電紡織堆疊3D鰭式多孔隙改質PVDF-TrFE壓電奈米感測器應用於智能聲場與步態足壓感測大數據分析;Three-Dimensional Stacked Near-Field Electrospun NanoPorous PVDF-TrFE Nanofibers as Self-Powered Smart Sensing in Acoustic and Gait Big Data Analytics
作者:	羅威丞;Lo, Wei-Cheng
贡献者:	光機電工程研究所
关键词:	近場電紡織技術;積層製造;智能步態感測;深度學習;動作辨識;生物識別;Near field electrospinning;Additive Manufacturing;Smart mat;Deep learning;Motion recognition;biometric
日期:	2020-07-29
上传时间:	2020-09-02 15:37:03 (UTC+8)
出版者:	國立中央大學
摘要:	本研究通過近場靜電紡絲描述一種具有多孔隙奈米纖維壓電奈米發電機的新型堆疊結構。3D堆疊的多孔奈米纖維結構增強應力集中效應，因此PVDF-TrFE奈米纖維可以促進更高的電輸出性能。通過近場靜電紡絲設置模仿積層製造，結構簡單且壓電強化的製造工藝能夠將堆疊的多孔隙PVDF-TrFE奈米纖維轉變為高性能傳感器。與原始PVDF-TrFE奈米纖維相比，電壓輸出性能提高2.7倍以上。此外，還開發自供電的步態足壓識別統計系統和個人步態生物特徵識別系統，以提供步態識別和一種新的生物特徵識別技術。通過深度學習BiLSTM模型，個人步態壓電訊號識別率達到86％。此研究將自供電系統的應用領域擴展到智能穿戴設備監控外，也刺激智能醫療行業中大數據分析的發展。其二設計多孔隙PVDF-TrFE壓電奈米單層纖維膜收集聲能振動訊號，輕薄平整的PVDF-TrFE纖維膜直寫於柵式柔性印刷電路基板上具有高的性能輸出，高靈敏度的可撓自供電聲能感測器被應用於收集人體聲能振動訊號，智慧聲場感測器結合深度學習模型設計人體動作辨識系統，透過演算法訓練後，收集喉部及口罩之振動訊號足以辨識多種運動型態，此研究為智能人機介面應用提出一種新穎的辨識系統。 ;This study describes a new stacked structure with porous nanofiber-based piezoelectric nanogenerator by near field electrospinning. The 3D-stacked porous nanofibers structure enhanced the stress concentration effect such that the PVDF-TrFE nanofibers can promote higher performance in electrical output. By mimicking the additive manufacturing in near field electrospinning (NFES) setup, structurally simple and piezoelectrically effective fabrication is capable of converting stacked porous PVDF-TrFE nanofiber into a high-performance sensor. The electrical voltage output performance is enhanced more than 2.7 times compared with primitive PVDF-TrFE nanofiber. Furthermore, a self-powered foot pressure recognition statistical system and an individual gait biometrics system are developed to provide gait recognition and a new biometrics technology. The personal sequence gait piezoelectric signal recognition rate have achieved to 86% by deep learning BiLSTM model. Furthermore, besides expending the application area of self-powered system to smart wearable device monitoring, this work also stimulates the evolution of big data analytics in intelligent medical industry. The second design is a single-layer porous PVDF-TrFE nanofiber film to collect acoustic signals. The thin and flat PVDF-TrFE fiber film is directly written on the grating FPCB with high performance and sensitivity. The self-powered smart acoustic sensing is used to collect human acoustic vibration signals, combined with the deep learning model as the human motion recognition system. The throat and mask vibration signals identify the human motion. This study presents a novel identification system for intelligent human-machine interface applications.
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