Owlet Baby Care, Inc.

Sean Kerman

Chris Hettinger

Tanner Christensen

Jeffrey Humpherys

Ajay Sundaram Iyer

Zack Bomsta

Kurt Gibbons Workman

Daniela Natalia Turner

Ali Carlile

Paul Allen

Bruce Olney

Ethan Lawrence

Ryan Workman

Daniela Turner

A technology for obtaining a respiratory rate from a photoplethysmogram (PPG) signal. In one example, an artificial neural network model can be trained to predict a respiratory rate using a training dataset containing PPG data. The artificial neural network model can include a first series of convolutional layers to remove artifacts from a PPG signal, a fast Fourier transform (FFT) layer to convert the PPG signal to PPG frequency representations, and a dense layer to decode the PPG frequency representations to respiratory rate predictions. After training the artificial neural network model, PPG data generated by a pulse oximeter monitor can be obtained, and the PPG data can be input to the artificial neural network model. The artificial neural network model outputs a respiratory rate prediction, wherein the respiratory rate prediction represents the respiratory rate obtained from the PPG signal.

Respiratory rate prediction from a photoplethysmogram

A technology for obtaining a heart rate from a photoplethysmogram (PPG) signal. In one example, an artificial neural network model can be trained to predict a heart rate using a training dataset containing PPG data. The artificial neural network model can include a series of convolutional layers to remove artifacts from a PPG signal, a fast Fourier transform (FFT) layer to convert the PPG signal to PPG frequency representations, and a dense layer to decode the PPG frequency representations to heart rate predictions. After training the artificial neural network model, PPG data generated by a pulse oximeter monitor can be obtained, and the PPG data can be input to the artificial neural network model. The artificial neural network model outputs a heart rate prediction, wherein the heart rate prediction represents the heart rate obtained from the PPG signal.

Heart rate prediction from a photoplethysmogram

A system for monitoring fetal health data and mother health data comprises a belly-covering garment that is configured to at least partially cover a belly and to hold one or more sensor modules directly adjacent to the belly. One or more sensor modules disposed within the belly-covering garment. The one or more sensor modules comprise a pulse-oximeter sensor that gathers pulse oximetry data from the mother through contact with the belly. The one or more sensor modules also comprise an accelerometer sensor that gathers movement data from the mother. Additionally, the one or more sensor modules comprise a fetal sensor that gathers health data from a fetus within the belly.

Fetal health data monitoring

Embodiments are directed to methods and systems for adaptive heart rate monitoring. In one scenario, a method for adaptive heart rate monitoring includes receiving, from a pulse-oximeter, a sensor signal, where the sensor signal is photoplethysmogram waveform. The method next includes generating a frequency-domain photoplethysmogram waveform by applying a transform algorithm to the sensor signal, and dividing the resulting frequency-domain photoplethysmogram waveform into discrete frequency regions. The method further includes identifying a fundamental heart rate harmonic within one of the discrete frequency regions by analyzing each discrete frequency region according to a specified analytic algorithm, and triggering a user interface to display a biometric measurement corresponding to the identification of the fundamental heart rate harmonic.

Heart-rate adaptive pulse oximetry

Biometric sensor systems are provided for identifying fundamental heart rate harmonics within noisy sensor signals. The system calculates a local signal-to-noise ratio for one or more identified frequency bands received in a biometric signal. The identified frequency bands are ranked based upon the calculated local signal-to-noise ratio. The fundamental heart rate is identified based upon the ranking of the identified frequency bands.

Local signal-to-noise peak detection

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Provo, UT, United States of America

Sean Kerman