Abstract: A computer-implemented method for upmixing audiovisual data can include obtaining audiovisual data including input audio data and video data accompanying the input audio data. Each frame of the video data can depict only a portion of a larger scene. The input audio data can have a first number of audio channels. The computer-implemented method can include providing the audiovisual data as input to a machine-learned audiovisual upmixing model. The audiovisual upmixing model can include a sequence-to-sequence model configured to model a respective location of one or more audio sources within the larger scene over multiple frames of the video data. The computer-implemented method can include receiving upmixed audio data from the audiovisual upmixing model. The upmixed audio data can have a second number of audio channels. The second number of audio channels can be greater than the first number of audio channels.

Abstract: Improved techniques and systems are disclosed for determining the components of resistance experienced by a wearer of a wearable device engaged in an activity such as bicycling or running. By monitoring data using the wearable device, improved estimates can be derived for various factors contributing to the resistance experienced by the user in the course of the activity. Using these improved estimates, data sampling rates may be reduced for some or all of the monitored data.

Abstract: Improved techniques and systems are disclosed for determining the components of resistance experienced by a wearer of a wearable device engaged in an activity such as bicycling or running. By monitoring data using the wearable device, improved estimates can be derived for various factors contributing to the resistance experienced by the user in the course of the activity. Using these improved estimates, data sampling rates may be reduced for some or all of the monitored data.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by a fitness tracking device, a plurality of heart rate measurements of the user over a period of time, wherein the plurality of heart rate measurements can include heart rate data from a heart rate sensor of the fitness tracking device; analyzing, by the fitness tracking device, the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time; determining, by the fitness tracking device, that the user is experiencing an onset phase if the rate of change of the heart rate during the period of time is greater than zero; determining, by the fitness tracking device, that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero; estimating, by the fitness tracking device, a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and estimating

Abstract: Systems and methods are disclosed for tracking physiological states and parameters for calorie estimation. A start of an exercise session associated with a user of a wearable computing device is determined. Heart rate data is measured for a first period of time. An onset heart rate value of the user is determined based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time. A resting heart rate parameter (RHR) of a calorimetry model is associated with at least one of the onset heart rate value, a preset RHR, and an RHR based on user biometric data. Energy expenditure of the user during a second period of time is estimated based on the calorimetry model and a plurality of heart rate measurements obtained by the wearable computing device during the second period of time.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device; obtaining, by at least one motion sensor, motion data of the user; analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user; estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the load and at least one of the heart rate measurement and the step rate.

Abstract: A fitness tracking device configured to be worn by a user obtains a plurality of physical characteristics of the user including a first age and a sex of the user. The fitness tracking device maps each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of a plurality of age ranges, and wherein the sex of the user is mapped to a first sex index. The fitness tracking device selects, from a memory of the fitness tracking device, a first calorimetry model of a plurality of calorimetry models, wherein the first calorimetry model is associated with each corresponding index, including the first age index and the first sex index of the user. The fitness tracking device estimates an energy expenditure rate using the first calorimetry model.

Abstract: In one aspect, the present disclosure relates to a method including obtaining a plurality of heart rate measurements of the user over a period of time; obtaining motion data of the user over the period of time; analyzing the motion data of the user to determine for each of the plurality of heart rate measurements, a corresponding work rate measurement; determining, for each of the plurality of heart rate measurements, a first confidence level; determining, for each corresponding work rate measurement, a second confidence level; and estimating a first energy expenditure rate using the plurality of heart rate measurements; estimating a second energy expenditure rate using the plurality of work rate measurements; and estimating a weighted energy expenditure rate of the user by combining the first energy expenditure rate weighted by the first confidence level and the second energy expenditure rate weighted by the second confidence level.

Abstract: Improved techniques and systems are disclosed for determining the components of resistance experienced by a wearer of a wearable device engaged in an activity such as bicycling or running. By monitoring data using the wearable device, improved estimates can be derived for various factors contributing to the resistance experienced by the user in the course of the activity. Using these improved estimates, data sampling rates may be reduced for some or all of the monitored data.

Abstract: In one aspect, the present disclosure relates to a method, including obtaining, by the fitness tracking device, motion data of the user over a period of time, wherein the motion data can include a first plurality motion measurements from a first motion sensor of the fitness tracking device; determining, by the fitness tracking device, using the motion data an angle of the fitness tracking device relative to a plane during the period of time; estimating by the fitness tracking device, using the motion data, a range of linear motion of the fitness tracking device through space during the period of time; and comparing, by the fitness tracking device, the angle of the fitness tracking device to a threshold angle and comparing the range of linear motion of the fitness tracking device to a threshold range of linear motion to determine whether the user is sitting or standing.

Abstract: Systems and methods are disclosed for tracking physiological states and parameters for calorie estimation. A start of an exercise session associated with a user of a wearable computing device is determined. Heart rate data is measured for a first period of time. An onset heart rate value of the user is determined based on the measured heart rate data, the onset heart rate value associated with a lowest valid heart rate measured during the first period of time. A resting heart rate parameter (RHR) of a calorimetry model is associated with at least one of the onset heart rate value, a preset RHR, and an RHR based on user biometric data. Energy expenditure of the user during a second period of time is estimated based on the calorimetry model and a plurality of heart rate measurements obtained by the wearable computing device during the second period of time.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by at least one sensor of a fitness tracking device, motion data of a user of the fitness tracking device; separating, by the fitness tracking device, the motion data into at least a first frequency signature attributable to movement by the user and a second frequency signature attributable to a type of a terrain on which the user is moving; determining, by the fitness tracking device, the type of the terrain on which the user is moving by analyzing the first frequency signature and the second frequency signature; and estimating, by the fitness tracking device, a rate of energy expenditure of the user by applying a calorimetry model including a coefficient or a parameter associated with the type of the terrain.

Abstract: In one aspect, the present disclosure relates to a method including obtaining a plurality of heart rate measurements of the user over a period of time; obtaining motion data of the user over the period of time; analyzing the motion data of the user to determine for each of the plurality of heart rate measurements, a corresponding work rate measurement; determining, for each of the plurality of heart rate measurements, a first confidence level; determining, for each corresponding work rate measurement, a second confidence level; and estimating a first energy expenditure rate using the plurality of heart rate measurements; estimating a second energy expenditure rate using the plurality of work rate measurements; and estimating a weighted energy expenditure rate of the user by combining the first energy expenditure rate weighted by the first confidence level and the second energy expenditure rate weighted by the second confidence level.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by a fitness tracking device, a plurality of heart rate measurements of the user over a period of time, wherein the plurality of heart rate measurements can include heart rate data from a heart rate sensor of the fitness tracking device; analyzing, by the fitness tracking device, the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time; determining, by the fitness tracking device, that the user is experiencing an onset phase if the rate of change of the heart rate during the period of time is greater than zero; determining, by the fitness tracking device, that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero; estimating, by the fitness tracking device, a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and estimating

Abstract: In one aspect, the present disclosure relates to a method, including obtaining, by the fitness tracking device, motion data of the user over a period of time, wherein the motion data can include a first plurality motion measurements from a first motion sensor of the fitness tracking device; determining, by the fitness tracking device, using the motion data an angle of the fitness tracking device relative to a plane during the period of time; estimating by the fitness tracking device, using the motion data, a range of linear motion of the fitness tracking device through space during the period of time; and comparing, by the fitness tracking device, the angle of the fitness tracking device to a threshold angle and comparing the range of linear motion of the fitness tracking device to a threshold range of linear motion to determine whether the user is sitting or standing.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by a fitness tracking device configured to be worn by a user, a plurality of physical characteristics of the user, wherein the plurality of physical characteristics includes a first age and a sex of the user; mapping, by the fitness tracking device, each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of a plurality of age ranges, and wherein the sex of the user is mapped to a first sex index; selecting, from a memory of the fitness tracking device, a first calorimetry model of a plurality of calorimetry models, wherein the first calorimetry model is associated with each corresponding index, including the first age index and the first sex index of the user; and estimating, by the fitness tracking device, an energy expenditure rate using the first calorimetry model, wherein the fitness tracking device can include constrained resources

Abstract: The present disclosure relates generally to improving calorie expenditure prediction and tracking and, more particularly, to techniques for calibration and calorimetry using data from motions sensors and heart rate sensors. Embodiments of the present disclosure include a fitness tracking device and techniques for accurately tracking an individual's energy expenditure over time and over a variety of activities while wearing the fitness tracking device. In some embodiments, the fitness tracking device may be a wearable device. The wearable device may be worn on a wrist, such as a watch, and it may include one or more microprocessors, a display, and a variety of sensors, including a heart rate sensor and one or more motion sensors.

Abstract: In one aspect, the present disclosure relates to a method including obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device; obtaining, by at least one motion sensor, motion data of the user; analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user; estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the load and at least one of the heart rate measurement and the step rate.