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: The present disclosure provides computer-implemented methods, systems, and devices for estimating the oxygen saturation levels of the blood of a user. A wearable computing device emits using one or more green light sources, light towards a skin of a user at a green wavelength. The wearable computing device further emits, using one or more red light sources, light towards a skin of a user at a red wavelength. The wearable computing device further emits, using one or more infrared light sources, light towards a skin of a user at an infrared wavelength. The wearable computing device further detects an intensity of green light, red light, and infrared light emitted from the skin of a user. The wearable computing device further determines an estimated oxygen saturation level.

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: 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: 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: 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: Temperature data acquired from a wearable device, for example at a user's wrist or within the device itself, can be used as a proxy to evaluate core body temperature changes. Sensor data may be provided to determine a skin temperature of a user and also an internal device temperature. A correlation between these two temperatures may be used to monitor subsequent temperature changes, which may be indicative of changes in the user's core body temperature. Temperature changes to the proxy temperature may be evaluated against a threshold to determine whether the user's core body temperature has also increased, which may be indicative of one or more physiological symptoms or events. Furthermore, additional physiological variables such as respiration rate, nocturnal heart rate, and heart rate variability may be analyzed for early signs of impending illness. A trained machine learning classifier can output the predicted illness status of an individual based on these parameters.

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: 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: 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: 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: A particle separation device can include a fiber microfludic structure.

Abstract: A system includes a microfluidic device configured to isolate one or more particles from a mixture, a flow rate matching device configured to match flow rate of the microfluidic device with flow rate of an electrical measurement device configured to measure an electrical property of the isolated particles, and an electrical measurement device configured to measure an electrical property of the isolated particles.

Abstract: Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining that a user of the wearable device has started an exercise, activating the GPS receiver in response to determining that the user has started the exercise, and detecting a time at which the GPS receiver achieves an initial GPS fix of a location of the wearable device. The method may further involve logging, based on output of the one or more biometric sensors, a first set of user data during a first time interval between the start of the exercise and the detected time of the initial GPS fix, and back-filling an exercise route of the user during the first time interval based on the first set of user data.

Abstract: A system includes a microfluidic device configured to isolate one or more particles from a mixture, a flow rate matching device configured to match flow rate of the microfluidic device with flow rate of an electrical measurement device configured to measure an electrical property of the isolated particles, and an electrical measurement device configured to measure an electrical property of the isolated particles.

Abstract: Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining that a user of the wearable device has started an exercise, activating the GPS receiver in response to determining that the user has started the exercise, and detecting a time at which the GPS receiver achieves an initial GPS fix of a location of the wearable device. The method may further involve logging, based on output of the one or more biometric sensors, a first set of user data during a first time interval between the start of the exercise and the detected time of the initial GPS fix, and back-filling an exercise route of the user during the first time interval based on the first set of user data.