仿生PDMS薄膜和逐層堆疊多孔PVDF-TrFE微奈米纖維混能感測器應用於動作抽搐識別;Bionic PDMS membrane and Layer-by-Layer Stacked Porous PVDF-TrFE nano/micro fibers Hybrid Sensor for Motor Tics Recognition

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题名:	仿生PDMS薄膜和逐層堆疊多孔PVDF-TrFE微奈米纖維混能感測器應用於動作抽搐識別;Bionic PDMS membrane and Layer-by-Layer Stacked Porous PVDF-TrFE nano/micro fibers Hybrid Sensor for Motor Tics Recognition
作者:	王傑;Wang, Jie
贡献者:	機械工程學系
关键词:	植物仿生混能自供電感測器（PBHS）;近場電紡織技術;逐層堆疊多孔PVDF-TrFE微奈米纖維;深度學習;動作抽搐識別;Plant bionic hybrid self-powered sensor (PBHS);Near field electrospinning (NFES);Layer-by-layer staked Porous PVDF-TrFE nano/micro fibers;Deep learning LSTM model;Motor Tics Recognition
日期:	2021-09-02
上传时间:	2021-12-07 13:36:52 (UTC+8)
出版者:	國立中央大學
摘要:	隨著各種人造電子皮膚和智能貼片等可穿戴電子產品的逐步發展，收集生物力學能量以實現自供電傳感對於實現系統的高效功能和可持續性至關重要。在本文中，報告了一種植物仿生和柔性混能自供電傳感器（PBHS），將睡蓮微奈米結構圖案轉移到 PDMS 膜表面，獲得具仿生睡蓮表面PDMS薄膜作為摩擦電層，並與逐層堆疊多孔微奈米纖維壓電奈米發電機混能，使感測器能夠增強能量收集特性。與原來的PVDF-TrFE奈米纖維壓電奈米發電機相比，油改性後的多孔奈米纖維壓電奈米發電機電壓輸出顯著提高了5.7倍，與具有睡蓮微納米結構的PDMS薄膜混能後，電壓輸出又增加了將近2倍。另外，還開發了自供電抽動識別系統，讓醫生或妥瑞兒照顧者可以觀察妥瑞氏症動作抽搐患者的狀態。通過長短期記憶（LSTM）的深度學習模型，整體序列混合訊號識別率達到了88.1%。本研究展示了PBHS的應用，有望為自供電可穿戴電子系統開闢新途徑，為醫療大數據分析帶來巨大機遇。;With the gradual development of various artificial electronic skins and smart patches and other wearable electronic products, the collection of biomechanical energy to achieve self-powered sensing is critical to achieving the efficient function and sustainability of the system. In this work, a study of a novel hybrid sensor fabricated based on piezoelectric and triboelectric design for motor tics recognition will be presented. A plant bionic and flexible hybrid self-powered sensor (PBHS) for motor tics recognition is reported. By combining a bionic polydimethylsiloxane (PDMS) triboelectric nanogenerator and a layered stacked porous polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) nanofiber piezoelectric nanogenerator in mixing through near-field electrospinning (NFES) process on the flexible printed circuit board (FPCB) substrate, this enables the sensor to enhance energy harvesting characteristics. Compared with the original PVDF-TrFE nanogenerator, the voltage output performance is improved by nearly 200%. Furthermore, a self-powered tics recognition system has been developed through deep learning to provide doctors to observe the status of patients with motor tics of Tourette syndrome. By using the deep learning model of long short-term memory (LSTM) of a type of recurrent neural network (RNN), the overall sequences hybrid signal recognition rate for tic recognition has been achieved to 88.1%.
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