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: A system and method for improving the accuracy of a user device when generating map/navigation information for display to a user, comprising: obtaining compass heading from a magnetometer of the user device located within a vehicle; adjusting the compass heading based on a mount angle of the user device within the vehicle; obtaining location data from a location sensor of the user device; determining if a course of the vehicle can be reliably determined from the location data; if the course of the vehicle cannot be reliably determined from the location data, determining the orientation of the vehicle using the compass heading but not the course; if the course of the vehicle can be reliably determined from the location data, calculating a course of the vehicle based on the location data and determining the orientation of the vehicle using the course; generating, by a processor, first map/navigation information using the orientation of the vehicle; and displaying, by a processor, the first map/navigation inform

Abstract: Aspects of the subject technology relate to electronic devices with pressure sensors. Pressure sensor occlusion may be detected based on a comparison of a variance of pressure data from the pressure sensor with a variance of acceleration data from an accelerometer of the device. If a ratio of the pressure data variance to the acceleration data variance is above a threshold, occlusion may be identified. Data from other sensors in the device or in an external device, or other features of the pressure data, may be used to identify a type of occlusion.

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: Ear buds may have optical proximity sensors and accelerometers. Control circuitry may analyze output from the optical proximity sensors and the accelerometers to identify a current operational state for the ear buds. The control circuitry may also analyze the accelerometer output to identify tap input such as double taps made by a user on ear bud housings. Samples in the accelerometer output may be analyzed to determine whether the samples associated with a tap have been clipped. If the samples have been clipped, a curve may be fit to the samples. Optical sensor data may be analyzed in conjunction with potential tap input data from the accelerometer. If the optical sensor data is ordered, a tap input may be confirmed. If the optical sensor data is disordered, the control circuitry can conclude that accelerometer data corresponds to false tap input associated with unintentional contact with the housing.

Abstract: The present disclosure relates to systems and methods of estimating energy expenditure of a user while swimming. A processor circuit of a user device can estimate a speed of the user based on a stroke rate and a stroke length. The processor circuit can estimate an efficiency of the user. The processor circuit can classify a swimming style of the user. The processor circuit can determine energy expenditure of the user based on the speed, the efficiency, and the style. The processor circuit can also detect glides of the user and adjust the energy expenditure.

Abstract: Systems and methods of analyzing a user's motion during a swimming session are described. One or more motions sensors can collect motion data of the user. A processor circuit can make motion analysis based on the motion data. The processor circuit can determine if the user's arm swing is a genuine swim stroke. The processor circuit can also determine whether the user is swimming or turning. The processor circuit can also classify the user's swim stroke style. The processor circuit can also determine the user's swim stroke phase. The processor circuit can also determine the user's stroke orbit consistency.

Abstract: The present disclosure relates to methods and systems of determining swimming metrics of a user during a swimming session. The method can include receiving, by a processor circuit of a user device, motion information from one or more motion sensors of the user device; determining, by the processor circuit using the motion information, a first set of rotational data of the user device, wherein the first set of rotational data is expressed in a first frame of reference; converting, by the processor circuit, the first set of rotational data into a second set of rotational data, wherein the second set of rotational data is expressed in a second frame of reference; determining, by the processor circuit, one or more swimming metrics of the user; and outputting the one or more swimming metrics.

Abstract: A method and a system for determining an energy expenditure of a user while practicing yoga are described. A heart rate sensing module can measure the user's heart rate. A temperature sensing module can measure ambient temperature. A motion sensing module can collect user's motion data. In some embodiments, a hot yoga session can be detected based on measured ambient temperature. In some embodiments, a yoga type can be detected based on the motion data. In some embodiments, an energy expenditure model can be applied based on the determined yoga type.

Abstract: A method and a system for determining an individual energy expenditure are described. In some embodiments, an energy expenditure can be calculated based on a combination of biometrics, heart rate and work rate. In some embodiments, a relative drag associated with the user can be calculated based on a group formation size, a group formation shape, participant velocities, weather, air density, and participant body surface areas. In some embodiments, a load adjustment factor can be determined based on the relative drag. In some embodiments, an adjusted energy expenditure can be determined based on the load adjustment factor.

Abstract: The present disclosure relates to a system and method of detecting activity by a wheelchair user. In one aspect, a method comprises collecting motion data of a user device located on an appendage of the user; detecting, by a processor circuit, that one or more activities by the wheelchair user occurred based on the motion data; calculating, by a processor circuit, an energy expenditure by the user based the one or more activities by the wheelchair user occurred; and outputting, by a processor circuit, the energy expenditure estimation.

Abstract: A relationship relating a load of exercise and a user's aerobic capacity may be determined as follows. A processor circuit of a device may retrieve, from a memory, a prior probability distribution of the load of exercise and a prior probability distribution of the user's aerobic capacity. The processor circuit may compute a joint prior probability of the load of exercise and the user's aerobic capacity. The processor circuit may compute a joint likelihood of the load of exercise and the user's aerobic capacity based on data indicative of a measured time-stamped work rate and a measured time-stamped heart rate. The processor circuit may combine the joint prior probability and the joint likelihood to produce a joint posterior probability. The processor circuit may use the joint posterior probability to determine a relationship relating the load of exercise and the user's aerobic capacity and output a calorie calculation.

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 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 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, 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