Neural network-based touch input classification

Examples are disclosed that relate to improving speed and accuracy of touch input classification. In one example, a touch detection device includes an array of antennas configured to measure touch input and output a touch matrix of pixels having touch values corresponding to the touch input measured...

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Main Authors Tsvetov, Anatoly, Ben-Amram, Nadav Shlomo, Hakim, Adam, Einhoren, Yoel Yehezkel, Maiberger, Roy-Gan, Zajonts, Etai
Format Patent
LanguageEnglish
Published 21.05.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Abstract Examples are disclosed that relate to improving speed and accuracy of touch input classification. In one example, a touch detection device includes an array of antennas configured to measure touch input and output a touch matrix of pixels having touch values corresponding to the touch input measured at each antenna of the array of antennas. The touch detection device further includes a neural network having an input layer including a plurality of nodes. Each node is configured to receive a touch value corresponding to a different pixel of the touch matrix. The neural network is configured to output classified touch data corresponding to the measured touch input based at least on the touch matrix.
AbstractList Examples are disclosed that relate to improving speed and accuracy of touch input classification. In one example, a touch detection device includes an array of antennas configured to measure touch input and output a touch matrix of pixels having touch values corresponding to the touch input measured at each antenna of the array of antennas. The touch detection device further includes a neural network having an input layer including a plurality of nodes. Each node is configured to receive a touch value corresponding to a different pixel of the touch matrix. The neural network is configured to output classified touch data corresponding to the measured touch input based at least on the touch matrix.
Author Hakim, Adam
Zajonts, Etai
Maiberger, Roy-Gan
Tsvetov, Anatoly
Einhoren, Yoel Yehezkel
Ben-Amram, Nadav Shlomo
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– fullname: Zajonts, Etai
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Snippet Examples are disclosed that relate to improving speed and accuracy of touch input classification. In one example, a touch detection device includes an array of...
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SubjectTerms CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
Title Neural network-based touch input classification
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