Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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

Abstract: Embodiments are directed to age-adaptive physiologic monitoring. In one scenario, a method is provided for accessing an indication of a user's age. The method next includes determining which type of physiologic monitoring is to be performed for the user, where each type of physiologic monitoring has an associated physiologic monitoring algorithm. The method also includes modifying parameters associated with the physiologic monitoring algorithm based on the indication of the user's age, and operating a physiologic monitoring system configured to provide the specified type of physiologic monitoring using the modified parameters of the physiologic monitoring algorithm.

Abstract: 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.

Abstract: 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.

Abstract: 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.