Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors

Wang, Ming; Yan, Zheng; Wang, Ting; Cai, Pingqiang; Gao, Siyu; Zeng, Yi; Wan, Changjin; Wang, Hong; Pan, Liang; Yu, Jiancan; Pan, Shaowu; He, Ke; Lu, Jie; Chen, Xiao

doi:10.1038/s41928-020-0422-z

Cited by 392 publications

(322 citation statements)

References 40 publications

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“…S27). The identification accuracy of our GA tactile sensor has reached a leading level of wearable sensors in recognition of materials species and pattern (36,37).…”

Section: Ai Microarray Sensor Of Gasmentioning

confidence: 98%

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

et al. 2020

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Direct foaming from solids is the most efficient method to fabricate porous materials. However, the ideal foaming fails to prepare aerogel of nanoparticles because the plasticity of their solids is denied by the overwhelming interface interactions. Here, we invent a hydroplastic foaming method to directly convert graphene oxide solids into aerogel bulks and microarrays, replacing the prevalent freezing method. The water intercalation plasticizes graphene oxide solids and enables direct foaming instead of catastrophic fragmentation. The bubble formation follows a general crystallization rule and allows nanometer-precision control of cellular wall thickness down to 8 nm. Bubble clustering generates hyperboloid structures with seamless basal connection and renders graphene aerogels with ultrarobust mechanical stability against extreme deformations. We exploit graphene aerogel to fabricate tactile microarray sensors with ultrasensitivity and ultrastability, achieving a high accuracy (80%) in artificially intelligent touch identification that outperforms human fingers (30%).

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“…S27). The identification accuracy of our GA tactile sensor has reached a leading level of wearable sensors in recognition of materials species and pattern (36,37).…”

Section: Ai Microarray Sensor Of Gasmentioning

confidence: 98%

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

et al. 2020

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“…DCNN architecture, which is an end‐to‐end network with several nonlinear activation functions, [ 33–35 ] is suitable for non‐linear multidimension data analysis. [ 36–39 ]…”

Section: Figurementioning

confidence: 99%

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

et al. 2020

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Artificial scent screening systems (known as electronic noses, E‐noses) have been researched extensively. A portable, automatic, and accurate, real‐time E‐nose requires both robust cross‐reactive sensing and fingerprint pattern recognition. Few E‐noses have been commercialized because they suffer from either sensing or pattern‐recognition issues. Here, cross‐reactive colorimetric barcode combinatorics and deep convolutional neural networks (DCNNs) are combined to form a system for monitoring meat freshness that concurrently provides scent fingerprint and fingerprint recognition. The barcodes—comprising 20 different types of porous nanocomposites of chitosan, dye, and cellulose acetate—form scent fingerprints that are identifiable by DCNN. A fully supervised DCNN trained using 3475 labeled barcode images predicts meat freshness with an overall accuracy of 98.5%. Incorporating DCNN into a smartphone application forms a simple platform for rapid barcode scanning and identification of food freshness in real time. The system is fast, accurate, and non‐destructive, enabling consumers and all stakeholders in the food supply chain to monitor food freshness.

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“…In particular, the persistent photoconduction in photonic synapse based on metal oxides ( 9 , 11 ), perovskites ( 12 ), carbon nanotubes ( 13 ), and two-dimensional (2D) materials ( 14 , 15 ) are favorable to emulate typical synaptic behaviors, such as spike timing–dependent plasticity, neural facilitation/depression, and conversion from short-term to long-term memory. To emulate a more practical nervous system, it is preferred to update the connection weight in multistep or multimodal plastic strategies, which convey more flexible and dexterous synaptic plasticity ( 16 ). Sequential (or superimposed) multimodal modulations in synaptic devices are also the fundamental of implementing complex neural behaviors and activities, which are still a significant challenge to conventional artificial synapses.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the optoelectronic neuromorphic devices have exhibited important applications in selective ultraviolet light detection ( 22 ), ultrafast machine vision sensors ( 23 ), on-chip photonic synapse ( 24 ), optical spiking afferent nerve ( 25 ), stretchable sensorimotor synapses ( 26 ), etc. The associative analysis of biomechanical and visual information is the basis of perception and cognition ability of human brain, which is of great significance for acquisition of somatosensory data and emulating artificial intelligence ( 16 , 27 ). How to synergize mechanical and optical strategies to update the synaptic weight is essential to realize multimodal plasticity for preferential interactive neuromorphic computation.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired mechano-photonic artificial synapse based on graphene/MoS ₂ heterostructure

et al. 2021

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Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS2 heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

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Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors

Abstract: X. (2020). Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors.

Cited by 392 publications

References 40 publications

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

Bioinspired mechano-photonic artificial synapse based on graphene/MoS ₂ heterostructure

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Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors

Abstract: X. (2020). Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors.

Cited by 392 publications

References 40 publications

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

Hydroplastic foaming of graphene aerogels and artificially intelligent tactile sensors

Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks

Bioinspired mechano-photonic artificial synapse based on graphene/MoS 2 heterostructure

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Bioinspired mechano-photonic artificial synapse based on graphene/MoS ₂ heterostructure