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:
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, by a fitness tracking device, an average step count of a user of the fitness tracking device; mapping, by the fitness tracking device, the average step count to a first corresponding physical activity level of a plurality of physical activity levels; storing, in a memory of the fitness tracking device, the first corresponding physical activity level; estimating, by the fitness tracking device, an aerobic capacity of the user using the first corresponding physical activity level of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the aerobic capacity of the user.
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, by the fitness tracking device, a second rate of energy expenditure of the user if the user is experiencing a cool-down phase using a cool-down calorimetry model.
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 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:
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:
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 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.