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Biomimetic and porous nanofiber-based hybrid sensor for multifunctional pressure sensing and human gesture identification via deep learning method
Nano Energy ( IF 17.6 ) Pub Date : 2020-06-15 , DOI: 10.1016/j.nanoen.2020.105029
Miao Hua Syu , Yi Jun Guan , Wei Cheng Lo , Yiin Kuen Fuh

Near-field electrospinning (NFES) is a site addressable microfabrication process and is utilized to deposit the micro/nano polyvinylidene fluoride (PVDF) fibers arrays on printed circuit board nanofiber-based based piezoelectric sensor architectures. In addition, a biomimetic and flexible hybrid self-powered sensors (BHSS) was created by hybridizing both Cu - biomimetic Polydimethylsiloxane triboelectric sensors to enhance the energy-harvesting characteristic. The optimized BHSS had open-circuit voltage (VOC) of 15 V and 115 nA of short-circuit current (ISC) and a maximum average power density is 675 μW m−2 with a load of 10 MΩ. Furthermore, an intelligent glove and the force sensor with are successively confirmed that the developed BHSS has promising applications in wearable self-power sensor technology. The machine learning algorithm of Long Short-Term Memory (LSTM) in the context of gesture recognition was used and effectively distinguish five human actions satisfactorily. LSTM based real-time electrical signals of five gestures dataset with varying duration and complexity can achieve an overall classification rate of 82.3%.



中文翻译:

基于仿生和多孔纳米纤维的混合传感器,通过深度学习方法实现多功能压力感测和手势识别

近场静电纺丝(NFES)是可现场寻址的微细加工工艺,可用于将微/纳米聚偏二氟乙烯(PVDF)纤维阵列沉积在基于印刷电路板基于纳米纤维的压电传感器架构上。此外,通过将铜-仿生聚二甲基硅氧烷摩擦电传感器混合使用来创建仿生和柔性混合自供电传感器(BHSS),以增强能量收集特性。经过优化的BHSS具有15 V的开路电压(V OC)和115 nA的短路电流(I SC),最大平均功率密度为675μWm -2负载为10MΩ。此外,智能手套和力传感器已被相继证实,所开发的BHSS在可穿戴式自功率传感器技术中具有广阔的应用前景。使用了基于手势识别的长短期记忆(LSTM)的机器学习算法,可以令人满意地有效区分五个人的动作。基于LSTM的五个手势数据集的持续时间和复杂度不同的实时电信号可以实现82.3%的总体分类率。

更新日期:2020-07-01
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