Abstract: A method of generating a blood pressure estimation for a subject includes receiving real-time PPG data from a PPG sensor attached to the subject, and generating a blood pressure estimation for the subject via an adaptive predictive model using the real time PPG data. The method includes receiving a real-time measurement of blood pressure via a blood pressure monitoring device attached to the subject and, in response to receiving the real-time measurement of blood pressure, updating one or more parameters of the model in real-time to improve blood pressure estimation accuracy of the model. The method may also include detecting whether the generated blood pressure estimation is above or below a threshold and, in response to determining that the generated biometric estimation is above or below the threshold, receiving a real-time measurement of blood pressure and then updating the parameters of the model to improve blood pressure estimation accuracy of the model.

Abstract: A method of generating a blood glucose estimation for a subject includes receiving real-time PPG data from a PPG sensor attached to the subject, and generating a blood glucose estimation for the subject via an adaptive predictive model using the real time PPG data. The method includes receiving a real-time measurement of blood glucose via a blood glucose monitoring device attached to the subject and, in response to receiving the real-time measurement of blood glucose, updating one or more parameters of the model in real-time to improve blood glucose estimation accuracy of the model. The method may also include detecting whether the generated blood glucose estimation is above or below a threshold and, in response to determining that the generated blood glucose estimation is above or below the threshold, receiving a real-time measurement of blood glucose and then updating the parameters of the model to improve blood glucose estimation accuracy of the model.

Abstract: A blood pressure monitoring system includes a blood pressure monitoring device configured to obtain a blood pressure measurement from a subject, a PPG sensor configured to obtain PPG data from the subject, and at least one processor configured to generate a blood pressure estimation for the subject via an adaptive predictive model using real-time PPG data from the PPG sensor, determine whether the generated blood pressure estimation is above or below a threshold, and send an alert to a remote device in response to determining that the generated blood pressure estimation is above or below the threshold. The at least one processor may also be configured to request a blood pressure measurement from the blood pressure monitoring device in response to determining that the generated blood pressure estimation is above or below the threshold, and update the one or more parameters of the adaptive predictive model in real-time.

Abstract: A method of generating a biometric estimation for a subject includes receiving real-time PPG data from a PPG sensor attached to the subject, and generating a biometric estimation for the subject via an adaptive predictive model using the real time PPG data. The method includes receiving a measurement of the biometric via a biometric monitoring device and, in response to receiving the measurement of the biometric, updating one or more parameters of the model in real-time to improve biometric estimation accuracy of the model. The method may also include detecting whether the generated biometric estimation is above or below a threshold and, in response to determining that the generated biometric estimation is above or below the threshold, receiving a measurement of the biometric and then updating the parameters of the model to improve biometric estimation accuracy of the model.

Abstract: A wearable device includes at least one physiological sensor configured to detect and/or measure physiological information from a subject over a period of time when the wearable device is worn by the subject, and a process or coupled to the sensor. The process or is configured to detect respective peaks in a physiological waveform representing the physiological information, compute probabilities for the respective peaks based on predetermined data indicative of one or more conditions, select a subset of the respective peaks based on the probabilities thereof as representing more accurate physiological information for the subject, and generate a physiological assessment of the subject based on the subset of the respective peaks that was selected. Related signal processing devices, methods of operation, and computer program products are also discussed.

Abstract: A method of presenting data from a remote sensor that is monitoring a subject includes obtaining a data stream from the remote sensor, wherein the data stream includes a sensor metric, a metric identifier, dynamically updated integrity information about an accuracy of the sensor metric, and a diagnostic assessment of a health condition of the subject, and then displaying, via a display associated with the client device, the diagnostic assessment of the health condition of the subject, a metric statistic associated with the diagnostic assessment, and a recommendation as to an action to be taken by the subject.

Abstract: Systems and methods for presenting sensor data are provided and include collecting physiological data and meta data from a subject via a sensor system, wherein the sensor system comprises a sensor element, a signal processor, and memory in communication with the signal processor. The subject meta data is processed by the signal processor to determine metric features, the metric features are processed by the signal processor to determine a subject-specific metric statistic for the subject, and the physiological data is processed by the signal processor to generate a sensor metric. The sensor metric is then presented for display via a display.

Abstract: A biometric waveform analysis system includes a wearable device having a sensor system and that is configured to be worn on a body of a subject, a metric output generator in communication with the sensor system, a waveform analysis engine in communication with the sensor system, and a control processor configured to control the sensor system, the metric output generator, and the waveform analysis engine. The sensor system includes one or more physiological sensors and motion sensors. The metric output generator includes logic that extracts at least one physiological parameter from the physiological data signal and extracts at least one motion parameter from the motion data signal. The waveform analysis engine includes waveform capture logic, normalization logic, contextual logic, and physiological assessment logic. The waveform capture logic receives the physiological data signal from the sensor system and separates the physiological data signal into a plurality of individual physiological waveforms.

Abstract: A method of identifying a best one of a plurality of photoplethysmography channels of a PPG sensor, wherein the PPG sensor includes at least one optical detector and a plurality of optical emitters that define the plurality of PPG channels, includes sensing PPG data from each of the plurality of PPG channels; processing the PPG data from each PPG channel, via the processor, to generate a plurality of PPG parameters; processing the PPG parameters, via a probabilistic model, to identify a best one io of the plurality of PPG channels.

Abstract: Systems and methods for presenting sensor data are provided and include collecting physiological data and meta data from a subject via a sensor system, wherein the sensor system comprises a sensor element, a signal processor, and memory in communication with the signal processor. The subject meta data is processed by the signal processor to determine metric features, the metric features are processed by the signal processor to determine a subject-specific metric statistic for the subject, and the physiological data is processed by the signal processor to generate a sensor metric. The sensor metric is then presented for display via a display.

Abstract: A method of producing subject-specific metric statistics includes collecting physiological data and meta data from a subject via a sensor system. The sensor system includes at least one sensor element, at least one signal processor, and memory in communication with the at least one signal processor. The collected data is processed via the at least one signal processor to determine a plurality of metric features from the collected data. The plurality of metric features are processed using one or more data clustering techniques via the at least one signal processor to generate at least one subject-specific metric statistic and at least one sensor metric. The at least one subject-specific metric statistic and the at least one sensor metric may be displayed via a display associated with a client device.

Abstract: A wearable device includes at least one physiological sensor configured to detect and/or measure physiological information from a subject over a period of time when the wearable device is worn by the subject, and a processor coupled to the sensor. The processor is configured to detect respective peaks in a physiological waveform representing the physiological information, compute probabilities for the respective peaks based on predetermined data indicative of one or more conditions, select a subset of the respective peaks based on the probabilities thereof as representing more accurate physiological information for the subject, and generate a physiological assessment of the subject based on the subset of the respective peaks that was selected. Related signal processing devices, methods of operation, and computer program products are also discussed.

Abstract: A method of producing subject-specific metric statistics includes collecting physiological data and meta data from a subject via a sensor system. The sensor system includes at least one sensor element, at least one signal processor, and memory in communication with the at least one signal processor. The collected data is processed via the at least one signal processor to determine a plurality of metric features from the collected data. The plurality of metric features are processed using one or more data clustering techniques via the at least one signal processor to generate at least one subject-specific metric statistic and at least one sensor metric. The at least one subject-specific metric statistic and the at least one sensor metric may be displayed via a display associated with a client device.