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:
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:
One or more electronic device may use motion and/or activity sensors to estimate a user's 6 minute walking distance. In particular, because users typically walk at less than their maximum output and in imperfect conditions, control circuitry within the device(s) may rely on walks of shorter distances to estimate the 6 minute walking distance. For example, the control circuitry may gather activity information for the user, such as heart rate, calories burned, and step count, and analyze a distance component and a speed component for periods in which the user has walked. Individual 6 minute walk distance estimates may be generated based on each of the activity information, distance component, and speed component. The distance and speed estimates may be corrected for walking behaviors that deviate from an ideal testing environment, and may then be fused with the activity estimate to generate a final 6 minute walk distance estimate.
Abstract:
In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.
Abstract:
Disclosed embodiments include wearable devices and techniques for detecting cardio machine activities, estimating user direction of travel, and monitoring performance during cardio machine activities. By accurately and promptly detecting cardio machine activities and automatically distinguishing between activities performed on different types of cardio machines, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording activities on a wide variety of cardio machines. In various embodiments, cardio machine activity detection techniques may use magnetic field data from a magnetic field sensor to improve the accuracy of orientation data and device heading measurements used to detect the end of a cardio machine activity.
Abstract:
Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.
Abstract:
In an example method, a mobile device obtains sample data generated by one or more sensors over a period of time, where the one or more sensors are worn by a user. The mobile device determines that the user has fallen based on the sample data, and determines, based on the sample data, a severity of an injury suffered by the user. The mobile device generates one or more notifications based on the determination that the user has fallen and the determined severity of the injury.
Abstract:
A system and method for collecting motion data using a fitness tracking device located on an arm of a user, detecting that the arm is constrained based on the motion data, estimating a stride length of the user based on the motion data and historical step cadence-to-stride length data, calculating fitness data using the estimated stride length, and outputting the fitness data to the user.
Abstract:
In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.