Abstract: Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.

Abstract: A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

Abstract: The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type. The device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process or the BMD applies a time domain analysis on data provided by a sensor of the BMD for a first activity, and applies a frequency domain analysis on the data for a second activity, which contributes to improved accuracy and speed of biometric data.

Abstract: A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

Abstract: Approaches described herein can capture an audio signal using at least one microphone while a user of an electronic device is determined to be asleep. At least one audio frame can be determined from the audio signal. The at least one audio frame represents a spectrum of frequencies detected by the at least one microphone over some period of time. One or more sounds associated with the at least one audio frame can be determined. Sleep-related information can be generated. The information identifies the one or more sounds as potential sources of sleep disruption.

Abstract: A wearable computing device includes an electronic display with a configurable brightness level setting, a physiological metric sensor system including a light source configured to direct light into tissue of a user wearing the wearable computing device and a light detector configured to detect light from the light source that reflects back from the user. The device may further include control circuitry configured to activate the light source during a first period, generate a first light detector signal indicating a first amount of light detected by the light detector during the first period, deactivate the light source during a second period, generate a second light detector signal indicating a second amount of light detected by the light detector during the second period, generate a physiological metric based at least in part on the first light detector signal and the second light detector signal, and modify the configurable brightness level setting based on the second light detector signal.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: According to one embodiment, an apparatus comprising a portable monitoring device to be affixed to a user. The portable monitoring device including: 1) a set of one or more sensors to generate sensor data indicative of physical activity of a user when the portable monitoring device is affixed to the user; and 2) processing circuitry coupled with the set of sensors, to detect that the user has been sedentary for a period of time, and cause the portable monitoring device to alert the user responsive to the detection to encourage the user to move.

Abstract: Techniques for music selection based on exercise detection are disclosed. In one aspect, a method of operating a wearable device may involve determining, based on output of one or more biometric sensors, that a user of the wearable device has started an exercise and playing music for the user of the wearable device in response to determining the start of the exercise. For example, playing the music may involve turning on a music player based on the start of the exercise. In another example, the wearable device includes the music player.

Abstract: In one aspect of the disclosed implementations, a device includes one or more motion sensors for sensing motion of the device and providing activity data indicative of the sensed motion. The device also includes one or more processors for monitoring the activity data, and receiving or generating annotation data for annotating the activity data with one or more markers or indicators to define one or more characteristics of an activity session. The device also includes one or more feedback devices for providing feedback, a notice, or an indication to a user based on the monitoring. The device further includes a portable housing that encloses at least portions of the motion sensors, the processors and the feedback devices.

Abstract: An aspect of the disclosure pertains to a wrist-worn device that may be characterized by the following features: an external surface that is not in contact with the user when the wrist-worn device is worn; a force sensor; a PPG sensor disposed on the wrist-worn device; and control logic configured to: (i) generate one or more sensor data samples, each sensor data sample including data that links force data generated by the force sensor when a user presses a against the external surface at a given time with heart rate data obtained from the PPG sensor at the given time; and (ii) calculate an estimate of blood pressure from the one or more sensor data samples. As examples, the force sensor may be a force sensitive touch screen or film, a strain gauge integrating into the device, or a calibrated spring element configured to be pressed by the user.

Abstract: An aspect of the disclosure pertains to a wrist-worn device that may be characterized by the following features: an external surface that is not in contact with the user when the wrist-worn device is worn; a force sensor; a PPG sensor disposed on the wrist-worn device; and control logic configured to: (i) generate one or more sensor data samples, each sensor data sample including data that links force data generated by the force sensor when a user presses a against the external surface at a given time with heart rate data obtained from the PPG sensor at the given time; and (ii) calculate an estimate of blood pressure from the one or more sensor data samples. As examples, the force sensor may be a force sensitive touch screen or film, a strain gauge integrating into the device, or a calibrated spring element configured to be pressed by the user.

Abstract: In one embodiment, a method for creating a blood oxygen saturation (SpO2) value, the method comprises receiving one or more photoplethysmography (PPG) signals for SpO2 detection from one or more PPG sensors; receiving one or more PPG signals for characterizing a heart rate from the one or more PPG sensors; using the one or more PPG signals for SpO2 detection, forming one or more SpO2 datasets wherein the SpO2 datasets respectively comprise one or more noise components; removing the one or more noise components from the one or more SpO2 datasets that are inconsistent with a feature of the one or more PPG signals characterizing the heart rate to produce one or more filtered SpO2 datasets; and using the one or more filtered SpO2 datasets, creating and storing the SpO2.

Abstract: In one aspect of the disclosed implementations, a device includes one or more motion sensors for sensing motion of the device and providing activity data indicative of the sensed motion. The device also includes one or more processors for monitoring the activity data, and receiving or generating annotation data for annotating the activity data with one or more markers or indicators to define one or more characteristics of an activity session. The device also includes one or more feedback devices for providing feedback, a notice, or an indication to a user based on the monitoring. The device further includes a portable housing that encloses at least portions of the motion sensors, the processors and the feedback devices.

Abstract: Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.

Abstract: An activity monitoring device, methods and computer readable media are provided. The activity monitoring device includes a housing configured for attachment to a body part of a user and a display screen attached to the housing. Further included is a first sensor disposed in the housing for capturing motion of the activity monitoring device when attached to the body part of the user and a second sensor disposed in the housing for sampling a heart rate of the user. Memory is disposed in the housing for storing the motion captured by the first sensor and the heart rate sampled by the second sensor. A processor is disposed in the housing and is configured to determine a physical state of the user during a period of time. For motion that is below a threshold the processor identifies the physical state to be a sedentary state and for motion that is at or above the threshold the processor identifies the physical state to be an active state.

Abstract: In one embodiment, a method for creating a blood oxygen saturation (SpO2) value, the method comprises receiving one or more photoplethysmography (PPG) signals for SpO2 detection from one or more PPG sensors; receiving one or more PPG signals for characterizing a heart rate from the one or more PPG sensors; using the one or more PPG signals for SpO2 detection, forming one or more SpO2 datasets wherein the SpO2 datasets respectively comprise one or more noise components; removing the one or more noise components from the one or more SpO2 datasets that are inconsistent with a feature of the one or more PPG signals characterizing the heart rate to produce one or more filtered SpO2 datasets; and using the one or more filtered SpO2 datasets, creating and storing the SpO2.

Abstract: Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

Abstract: Approaches described herein can determine one or more breathing phase patterns over a period of time using audio data captured by at least one microphone. The audio data can include one or more snores. A breathing phase pattern included within the period of time can be determined based at least in part on sensor data captured by one or more sensors in the electronic device. A determination can be made that a first breathing phase pattern represented by the audio data and a second breathing phase pattern represented by the sensor data are correlated. A determination can be made that the first breathing phase pattern represented by the audio data and the second breathing phase pattern represented by the sensor data both correspond to a user wearing the electronic device.