Abstract: Embodiments are directed to systems and methods for determining blood pressure (e.g., diastolic and systolic pressure) of a user. A pressure applied by a skin region of a user and an optical cardiac signal are simultaneously measured. The optical cardiac signal, which may correspond to a measured skin region blood volume, is analyzed to identify a series of pulse waves, which in some instances may be normalized. A series of values of a metric associated with the morphology of the pulse waves is determined. The metric values are associated with corresponding pressure values. The metric values and pressure values are used to determine diastolic pressure and systolic pressure.

Abstract: A characteristic (e.g., SpO2) of a user's physiological signals can be estimated using a pulse oximeter. In some examples, inconsistent measurement of the physiological characteristic may occur despite the underlying physiological signals having quality characteristics consistent with physiologically valid signals showing a consistent cardiac signal indicative of accurate measurement of the physiological characteristic. In particular, the measurement inconsistency may be associated with a spatially localized region. Such measurement inconsistency may result in an incorrect, low estimate of the physiological characteristic relative to the true characteristic (e.g., the SpO2 estimate may skew lower than the true SpO2). An algorithm may be used to detect spatially localized measurement inconsistency and to mitigate or reduce its effect to improve the accuracy of the estimate of the physiological characteristic.

Abstract: A characteristic (e.g., SpO2) of a user's physiological signals can be estimated using a pulse oximeter. In some examples, inconsistent measurement of the physiological characteristic may occur despite the underlying physiological signals having quality characteristics consistent with physiologically valid signals showing a consistent cardiac signal indicative of accurate measurement of the physiological characteristic. In particular, the measurement inconsistency may be associated with a spatially localized region. Such measurement inconsistency may result in an incorrect, low estimate of the physiological characteristic relative to the true characteristic (e.g., the SpO2 estimate may skew lower than the true SpO2). An algorithm may be used to detect spatially localized measurement inconsistency and to mitigate or reduce its effect to improve the accuracy of the estimate of the physiological characteristic.

Abstract: Robust estimation of a characteristic of a user's physiological signals can be achieved by filtering or classifying samples. Rather than estimating the characteristic of the user's physiological signals based on each sample at a first wavelength and a second wavelength, a robust system and method can, in some examples, estimate the characteristic using samples at the first wavelength and the second wavelength that meet one or more criteria and filter out samples that fail to meet the one or more criteria. In some examples, the system and method can weight samples based on the one or more criteria, and estimate the characteristic using the weighted samples. Samples failing to meet the one or more criteria can be given less weight or no weight in the estimation. The one or more criteria can include a criterion based on at least the physiological signal at a third wavelength.

Abstract: Estimation of a characteristic of a user's physiological signals can be improved using one or more calibration relationships that may be dependent on a characteristic of the optical sensor. For example, different calibration relationships can be used that are dependent on a spatial characteristic and/or that are dependent on a wavelength characteristic of the light emitting component(s) of the respective emitter of a channel. In some examples, a unique calibration relationship can be used for each channel. In some examples, a common calibration relationship can be used for multiple channels with shared distance and/or wavelength characteristics. Utilizing distance-dependent and/or wavelength-dependent calibration relationships can improve robustness of pulse oximetry measurements.

Abstract: A wearable device is described. The wearable device includes a housing having a back cover, and an optical mask on first portions of the back cover. The back cover includes a set of windows, with a first subset of windows in the set of windows being defined by an absence of the optical mask on second portions of the back cover, and a second subset of windows in the set of windows being inset in a set of openings in the back cover. An optical barrier surrounds each window in the second subset of windows. A set of light emitters is configured to emit light through at least some of the windows in the set of windows. A set of light detectors is configured to receive light through at least some of the windows in the set of windows.

Abstract: A wearable electronic device (e.g., an electronic watch) may detect and analyze one or more sensing signals corresponding to light detected by the device to determine whether the device is in a sensing state (e.g., the device is within a maximum sensing distance of a user). If the wearable electronic device is in the sensing state, the device may determine one or more physiological parameters from the same signals used to determine whether the device is in the sensing state.

Abstract: A wearable device is described. The wearable device includes a housing having a back cover, and an optical mask on first portions of the back cover. The back cover includes a set of windows, with a first subset of windows in the set of windows being defined by an absence of the optical mask on second portions of the back cover, and a second subset of windows in the set of windows being inset in a set of openings in the back cover. An optical barrier surrounds each window in the second subset of windows. A set of light emitters is configured to emit light through at least some of the windows in the set of windows. A set of light detectors is configured to receive light through at least some of the windows in the set of windows.

Abstract: Robust estimation of a characteristic of a user's physiological signals can be achieved by filtering or classifying samples. Rather than estimating the characteristic of the user's physiological signals based on each sample at a first wavelength and a second wavelength, a robust system and method can, in some examples, estimate the characteristic using samples at the first wavelength and the second wavelength that meet one or more criteria and filter out samples that fail to meet the one or more criteria. In some examples, the system and method can weight samples based on the one or more criteria, and estimate the characteristic using the weighted samples. Samples failing to meet the one or more criteria can be given less weight or no weight in the estimation. The one or more criteria can include a criterion based on at least the physiological signal at a third wavelength.

Abstract: A wearable device includes a housing; a wrist band attached to the housing; first and second emitters positioned within the housing and configured to respectively emit, through a back of the housing, a first beam of electromagnetic radiation having a first infrared (IR) wavelength and a second beam of electromagnetic radiation having a second IR wavelength. The second IR wavelength is different from the first IR wavelength. The wearable device also includes a photodetector positioned within the housing and filtered to detect a set of electromagnetic radiation wavelengths including the first IR wavelength and the second IR wavelength; and a matter differentiation circuit configured to indicate, at least partly in response to signals indicating amounts of the first IR wavelength and the second IR wavelength received by the photodetector, whether the back of the housing is likely proximate to human tissue.

Abstract: Estimation of a characteristic of a user's physiological signals can be improved using one or more calibration relationships that may be dependent on a characteristic of the optical sensor. For example, different calibration relationships can be used that are dependent on a spatial characteristic and/or that are dependent on a wavelength characteristic of the light emitting component(s) of the respective emitter of a channel. In some examples, a unique calibration relationship can be used for each channel. In some examples, a common calibration relationship can be used for multiple channels with shared distance and/or wavelength characteristics. Utilizing distance-dependent and/or wavelength-dependent calibration relationships can improve robustness of pulse oximetry measurements.

Abstract: Estimation of a characteristic of a user's physiological signals can be improved using one or more calibration relationships that may be dependent on a characteristic of the optical sensor. For example, different calibration relationships can be used that are dependent on a spatial characteristic and/or that are dependent on a wavelength characteristic of the light emitting component(s) of the respective emitter of a channel. In some examples, a unique calibration relationship can be used for each channel. In some examples, a common calibration relationship can be used for multiple channels with shared distance and/or wavelength characteristics. Utilizing distance-dependent and/or wavelength-dependent calibration relationships can improve robustness of pulse oximetry measurements.

Abstract: Robust estimation of a characteristic of a user's physiological signals can be achieved by filtering or classifying samples. Rather than estimating the characteristic of the user's physiological signals based on each sample at a first wavelength and a second wavelength, a robust system and method can, in some examples, estimate the characteristic using samples at the first wavelength and the second wavelength that meet one or more criteria and filter out samples that fail to meet the one or more criteria. In some examples, the system and method can weight samples based on the one or more criteria, and estimate the characteristic using the weighted samples. Samples failing to meet the one or more criteria can be given less weight or no weight in the estimation. The one or more criteria can include a criterion based on at least the physiological signal at a third wavelength.

Abstract: A wearable device includes a housing; a wrist band attached to the housing; first and second emitters positioned within the housing and configured to respectively emit, through a back of the housing, a first beam of electromagnetic radiation having a first infrared (IR) wavelength and a second beam of electromagnetic radiation having a second IR wavelength. The second IR wavelength is different from the first IR wavelength. The wearable device also includes a photodetector positioned within the housing and filtered to detect a set of electromagnetic radiation wavelengths including the first IR wavelength and the second IR wavelength; and a matter differentiation circuit configured to indicate, at least partly in response to signals indicating amounts of the first IR wavelength and the second IR wavelength received by the photodetector, whether the back of the housing is likely proximate to human tissue.

Abstract: A wearable device includes a housing; a wrist band attached to the housing; first and second emitters positioned within the housing and configured to respectively emit, through a back of the housing, a first beam of electromagnetic radiation having a first infrared (IR) wavelength and a second beam of electromagnetic radiation having a second IR wavelength. The second IR wavelength is different from the first IR wavelength. The wearable device also includes a photodetector positioned within the housing and filtered to detect a set of electromagnetic radiation wavelengths including the first IR wavelength and the second IR wavelength; and a matter differentiation circuit configured to indicate, at least partly in response to signals indicating amounts of the first IR wavelength and the second IR wavelength received by the photodetector, whether the back of the housing is likely proximate to human tissue.

Abstract: A wearable device is described. The wearable device includes a housing having a back cover, and an optical mask on first portions of the back cover. The back cover includes a set of windows, with a first subset of windows in the set of windows being defined by an absence of the optical mask on second portions of the back cover, and a second subset of windows in the set of windows being inset in a set of openings in the back cover. An optical barrier surrounds each window in the second subset of windows. A set of light emitters is configured to emit light through at least some of the windows in the set of windows. A set of light detectors is configured to receive light through at least some of the windows in the set of windows.

Abstract: Estimation of a characteristic of a user's physiological signals can be improved using one or more calibration relationships that may be dependent on a characteristic of the optical sensor. For example, different calibration relationships can be used that are dependent on a spatial characteristic and/or that are dependent on a wavelength characteristic of the light emitting component(s) of the respective emitter of a channel. In some examples, a unique calibration relationship can be used for each channel. In some examples, a common calibration relationship can be used for multiple channels with shared distance and/or wavelength characteristics. Utilizing distance-dependent and/or wavelength-dependent calibration relationships can improve robustness of pulse oximetry measurements.

Abstract: A wearable device is described. The wearable device includes a housing having a back cover, and an optical mask on first portions of the back cover. The back cover includes a set of windows, with a first subset of windows in the set of windows being defined by an absence of the optical mask on second portions of the back cover, and a second subset of windows in the set of windows being inset in a set of openings in the back cover. An optical barrier surrounds each window in the second subset of windows. A set of light emitters is configured to emit light through at least some of the windows in the set of windows. A set of light detectors is configured to receive light through at least some of the windows in the set of windows.

Abstract: Robust estimation of a characteristic of a user's physiological signals can be achieved by filtering or classifying samples. Rather than estimating the characteristic of the user's physiological signals based on each sample at a first wavelength and a second wavelength, a robust system and method can, in some examples, estimate the characteristic using samples at the first wavelength and the second wavelength that meet one or more criteria and filter out samples that fail to meet the one or more criteria. In some examples, the system and method can weight samples based on the one or more criteria, and estimate the characteristic using the weighted samples. Samples failing to meet the one or more criteria can be given less weight or no weight in the estimation. The one or more criteria can include a criterion based on at least the physiological signal at a third wavelength.

Abstract: Robust estimation of a characteristic of a user's physiological signals can be achieved by filtering or classifying samples. Rather than estimating the characteristic of the user's physiological signals based on each sample at a first wavelength and a second wavelength, a robust system and method can, in some examples, estimate the characteristic using samples at the first wavelength and the second wavelength that meet one or more criteria and filter out samples that fail to meet the one or more criteria. In some examples, the system and method can weight samples based on the one or more criteria, and estimate the characteristic using the weighted samples. Samples failing to meet the one or more criteria can be given less weight or no weight in the estimation. The one or more criteria can include a criterion based on at least the physiological signal at a third wavelength.