Journal ArticleDOI
Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors
Ming Wang,Zheng Yan,Ting Wang,Pingqiang Cai,Siyu Gao,Yi Zeng,Changjin Wan,Hong Wang,Liang Pan,Jiancan Yu,Shaowu Pan,Ke He,Jie Lu,Xiaodong Chen +13 more
- Vol. 3, Iss: 9, pp 563-570
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TLDR
A bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes is reported.Abstract:
Gesture recognition using machine-learning methods is valuable in the development of advanced cybernetics, robotics and healthcare systems, and typically relies on images or videos. To improve recognition accuracy, such visual data can be combined with data from other sensors, but this approach, which is termed data fusion, is limited by the quality of the sensor data and the incompatibility of the datasets. Here, we report a bioinspired data fusion architecture that can perform human gesture recognition by integrating visual data with somatosensory data from skin-like stretchable strain sensors made from single-walled carbon nanotubes. The learning architecture uses a convolutional neural network for visual processing and then implements a sparse neural network for sensor data fusion and recognition at the feature level. Our approach can achieve a recognition accuracy of 100% and maintain recognition accuracy in non-ideal conditions where images are noisy and under- or over-exposed. We also show that our architecture can be used for robot navigation via hand gestures, with an error of 1.7% under normal illumination and 3.3% in the dark. A bioinspired machine-learning architecture can combine visual data with data from stretchable strain sensors to achieve human gesture recognition with high accuracy in complex environments.read more
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Journal ArticleDOI
Near-sensor and in-sensor computing
Feichi Zhou,Yang Chai +1 more
TL;DR: In this paper, the authors examine the concept of near-senor and in-sensor computing in which computation tasks are moved partly to the sensory terminals, exploring the challenges facing the field and providing possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies.
Journal ArticleDOI
Wearable Pressure Sensors for Pulse Wave Monitoring
TL;DR: This review presents an overview of wearable pressure sensors for human pulse wave monitoring, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave Monitoring and cardiovascular condition assessment.
Journal ArticleDOI
Mixed-dimensional MXene-hydrogel heterostructures for electronic skin sensors with ultrabroad working range
Yichen Cai,Jie Shen,Chi-Wen Yang,Yi Wan,Hao-Ling Tang,Areej Aljarb,Cailing Chen,Jui-Han Fu,Xuan Wei,Kuo-Wei Huang,Yu Han,Steven J. Jonas,Xiaochen Dong,Vincent Tung +13 more
TL;DR: A multifunctional e-skin system with a heterostructured configuration that couples vinyl-hybrid-silica nanoparticle–modified polyacrylamide hydrogel with two-dimensional MXene through nano-bridging layers of polypyrrole nanowires at the interfaces, featuring high toughness and low hysteresis, is presented.
Journal ArticleDOI
Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure.
Jinran Yu,Xixi Yang,Guoyun Gao,Yao Xiong,Yifei Wang,Jing Han,Youhui Chen,Huai Zhang,Qijun Sun,Qijun Sun,Zhong Lin Wang,Zhong Lin Wang +11 more
TL;DR: In this article, a bio-inspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity is presented, which is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator.
Journal ArticleDOI
AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.
TL;DR: In this article, an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface was proposed for barrier-free communication between signers and non-signers.
References
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