Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the machine data; determining a heart rate caloric expenditure based on the heart rate data and the calibrated maximal oxygen consumption of the user; and providing information corresponding to the heart rate caloric expenditure for presentation.

Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Abstract: Embodiments are disclosed for estimating caloric expenditure using a heart rate model specific to a motion class. In an embodiment, a method comprises: obtaining acceleration and rotation rate from motion sensors of a wearable device; determining a vertical component of inertial acceleration and a vertical component of rotational acceleration from the acceleration and rotation rate, respectively; determining a magnitude of the rotation rate; determining a correlation between the inertial vertical acceleration component and rotational acceleration; determining a percentage of motion outside a dominant plane of motion; predicting a motion class based on a motion classification model that takes as input the motions, correlation and percentage; determining a likelihood the user is walking; in accordance with determining that the user is likely not walking, configuring a heart rate model based on the predicted motion class; and estimating, using the configured heart rate model, a caloric expenditure of the user.

Abstract: Embodiments of the present disclosure provide systems and methods for monitoring a patient to produce a signal representing a blood oxygen concentration. The signal may be analyzed to determine the presence of one or more sleep apnea events, and an integral of the signal may be calculated if the signal is outside of a set range or threshold. A practitioner may choose to be informed of the presence of sleep apnea events if the blood oxygen concentration is less then a preset limit, if an upper limit has been reached for an integral representing the severity of the oxygen deprivation over time, or anytime sleep apnea events may be present in the signal.

Abstract: Embodiments are disclosed for estimating caloric expenditure based on center of mass motion and heart rate. In an embodiment, a method comprises: obtaining acceleration and rotation rate of a wearable device worn on a limb of a user; transforming the acceleration and rotation rate into an inertial frame; determining a vertical component of acceleration, rotation rate magnitude and vertical component of rotational acceleration due to limb rotation; determining a work rate (WR) based caloric expenditure based on the vertical component of acceleration, rotation rate magnitude and a correlation coefficient that measures a correlation between the vertical component of acceleration and the vertical component of rotational acceleration; obtaining heart rate (HR) data from a heart rate sensor of the wearable device; determining an HR based caloric expenditure based on the HR data; and fusing, the WR based caloric expenditure with the HR based caloric expenditure to get a fused caloric expenditure.

Abstract: Embodiments are disclosed for estimating caloric expenditure using a heart rate model specific to a motion class. In an embodiment, a method comprises: obtaining acceleration and rotation rate from motion sensors of a wearable device; determining a vertical component of inertial acceleration and a vertical component of rotational acceleration from the acceleration and rotation rate, respectively; determining a magnitude of the rotation rate; determining a correlation between the inertial vertical acceleration component and rotational acceleration; determining a percentage of motion outside a dominant plane of motion; predicting a motion class based on a motion classification model that takes as input the motions, correlation and percentage; determining a likelihood the user is walking; in accordance with determining that the user is likely not walking, configuring a heart rate model based on the predicted motion class; and estimating, using the configured heart rate model, a caloric expenditure of the user.

Abstract: In an embodiment, a method comprises: establishing, by a wireless wearable computer worn by a user, a wireless communication connection with a fitness machine; obtaining machine data from the fitness machine while the user is engaged in a workout session on the fitness machine; obtaining, from a heart rate sensor of the wireless device, heart rate data of the user; determining a work rate caloric expenditure by applying a work rate calorie model to the machine data; determining a calibrated maximal oxygen consumption of the user based on the heart rate data and the work rate caloric expenditure; determining a heart rate caloric expenditure by applying a heart rate calorie model to the heart rate data and the calibrated maximal oxygen consumption of the user; and sending to the fitness machine via the communication connection, at least one of the work rate caloric expenditure or the heart rate caloric expenditure.

Abstract: Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models.

Abstract: Systems and methods are provided for determining respiration information from physiological signals such as PPG signals. A physiological signal is processed to generate at least one respiration information signal and an autocorrelation sequence is generated based on the at least one respiration information signal. In some embodiments, a respiration peak is identified based on the autocorrelation sequence and a composite peak is generated based on the identified peak and at least one previous respiration peak. Respiration information is calculated based on the composite peak. In some embodiments, a determination is made whether the autocorrelation sequence includes an undesired harmonic. When the autocorrelation sequence includes an undesired harmonic, the autocorrelation sequence may not be used in the calculation of respiration information.

Abstract: A patient monitoring system may receive a physiological signal such as a photoplethysmograph (PPG) signal that exhibits frequency and amplitude modulation based on respiration. A phase locked loop may generate a frequency demodulated signal and an amplitude demodulated signal from the PPG signal. An autocorrelation sequence may be generated for each of the frequency demodulated signal and the amplitude demodulated signal. The autocorrelation sequences may be combined and respiration information may be determined based on the combined autocorrelation sequence.

Abstract: Embodiments are disclosed for a wireless wearable computer with fitness machine connectivity for improved activity monitoring using caloric expenditure models.

Abstract: Embodiments of the present disclosure provide systems and methods for monitoring a patient to produce a signal representing a blood oxygen concentration. The signal may be analyzed to determine the presence of one or more sleep apnea events, and an integral of the signal may be calculated if the signal is outside of a set range or threshold. A practitioner may choose to be informed of the presence of sleep apnea events if the blood oxygen concentration is less then a preset limit, if an upper limit has been reached for an integral representing the severity of the oxygen deprivation over time, or anytime sleep apnea events may be present in the signal.

Abstract: Embodiments of the present disclosure provide systems and methods for monitoring a patient to produce a signal representing a blood oxygen concentration. The signal may be analyzed to determine the presence of one or more sleep apnea events, and an integral of the signal may be calculated if the signal is outside of a set range or threshold. A practitioner may choose to be informed of the presence of sleep apnea events if the blood oxygen concentration is less then a preset limit, if an upper limit has been reached for an integral representing the severity of the oxygen deprivation over time, or anytime sleep apnea events may be present in the signal.

Abstract: Systems and methods are provided for detecting held breath events. A physiological signal, such as a photoplethysmograph (PPG) signal, is processed to extract respiration-related morphology metric signals. The morphology signals are analyzed to determine when a patient's breath is being held.

Abstract: Systems and methods are provided for determining respiration information from physiological signals such as PPG signals. A physiological signal is processed to generate at least one respiration information signal and an autocorrelation sequence is generated based on the at least one respiration information signal. In some embodiments, a respiration peak is identified based on the autocorrelation sequence and a composite peak is generated based on the identified peak and at least one previous respiration peak. Respiration information is calculated based on the composite peak. In some embodiments, a determination is made whether the autocorrelation sequence includes an undesired harmonic. When the autocorrelation sequence includes an undesired harmonic, the autocorrelation sequence may not be used in the calculation of respiration information.

Abstract: Systems and methods are provided for determining respiration information from physiological signals such as PPG signals. A physiological signal is processed to generate at least one respiration information signal and an autocorrelation sequence is generated based on the at least one respiration information signal. In some embodiments, a respiration peak is identified based on the autocorrelation sequence and a composite peak is generated based on the identified peak and at least one previous respiration peak. Respiration information is calculated based on the composite peak. In some embodiments, a determination is made whether the autocorrelation sequence includes an undesired harmonic. When the autocorrelation sequence includes an undesired harmonic, the autocorrelation sequence may not be used in the calculation of respiration information.

Abstract: Methods and systems are presented for determining regional blood oxygen saturation (rSO2) of a subject. Differential absorption values may be determined based on received first and second light signals corresponding to light attenuated by respective first and second regions of the subject. Reference absorption curves are received, each associated with an rSO2 value and containing expected absorption values. The differential absorption values are compared with the expected absorption values to determine a best fit reference absorption curve. An rSO2 value is determined based on the best fit reference absorption curve. An rSO2 value may also be determined based on a combination of two or more rSO2 estimates. The rSO2 estimates are determined based on two or more pairs of the differential absorption values. The differential absorption values are determined based on received first and second lights signals corresponding to three or more wavelengths of light.

Abstract: A signal representing physiological information may include information related to respiration. A patient monitoring system may generate a plurality of autocorrelation sequences from the signal and combine the autocorrelation sequences to generate a combined autocorrelation sequence. The combined autocorrelation sequence may be analyzed to identify one or more peaks that may correspond to respiration information. Respiration information such as respiration rate may be determined based on the one or more peaks.

Abstract: A patient monitoring system may generate an autocorrelation sequence for a physiological signal such as a photoplethysmograph signal. A series of peak values may be identified for the autocorrelation sequence. The peak values may be modified based on a historical distribution of a physiological parameter. A physiological parameter such as respiration rate may be determined based on the modified peak values.

Abstract: A patient monitoring system may generate a derivative signal from a physiological signal. The derivative signal may be filtered based on a pulse rate estimate associated with the physiological signal. A plurality of crossing points may be determined for the filtered derivative signal and translated to the derivative signal. A plurality of fiducial points may be determined for the derivative signal based on the plurality of crossing points. The plurality of fiducial points may be utilized to determine physiological information from the physiological signal.