Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for driver detection are described. One example method includes monitoring data provided by an accelerometer associated with a client device. It is determined whether the data indicates a particular event associated with a vehicle. In response to determining that the data indicates the particular event associated with the vehicle, one or more micro-activities associated with the vehicle are determined based on data from at least one of the accelerometer and one or more other sensors associated with the client device. Results from each of the one or more micro-activities are provided to a trained model associated with the client device. In response to providing the results to the trained model, one or more vehicular events in a time sequence are identified based on the one or more micro-activities associated with the vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for driver detection are described. One example method includes monitoring data provided by an accelerometer associated with a client device. It is determined whether the data indicates a particular event associated with a vehicle. In response to determining that the data indicates the particular event associated with the vehicle, one or more micro-activities associated with the vehicle are determined based on data from at least one of the accelerometer and one or more other sensors associated with the client device. Results from each of the one or more micro-activities are provided to a trained model associated with the client device. In response to providing the results to the trained model, one or more vehicular events in a time sequence are identified based on the one or more micro-activities associated with the vehicle.

Abstract: Method, apparatus and system for determining a wearing position of a wearable device. The method includes receiving motion data from a wearable device associated with an individual, wherein the motion data comprises a first data segment and a second data segment, the first data segment associated with walking, and the second data segment associated with non-walking. The method further includes determining first characteristic data associated with the first data segment and second characteristic data associated with the second data segment. The method further includes determining, by a computing device, a wearing position of the wearable device associated with the individual based on the first characteristic data and a match between the second characteristic data and an activity signature associated with a time of day.

Abstract: A method for measuring body movement with a wearable device. The method includes receiving, from a magnetic sensor of the wearable device, first signal data indicative of a magnetic field of a wearable magnet, wherein the wearable device is placed on a first body segment and the wearable magnet is placed on a second body segment; determining, by a computing device based on the first signal data, a distance metric indicative of a relative distance between the wearable magnet and the wearable device, and an orientation metric indicative of an orientation relative to the wearable magnet; and determining a biomechanical model based on the type of body segment for the first body segment and the second body segment, wherein the biomechanical model includes a relationship constraint between the distance metric and the orientation metric.

Abstract: Method, apparatus and system for estimating heart rate with a wearable device. The method includes receiving movement data and heart rate data, the movement data indicative of physical exertion of an individual associated with the wearable device, and the heart rate data measured for the individual during the same period; determining an estimated human power output based on the movement data indicative of physical exertion of the individual; determining a heart rate demand value for improving a heart rate estimate based on the estimated human power output and at least one adaptive parameter, wherein the heart rate estimate corresponds to the heart rate data, and the at least one adaptive parameter is adjustable based on the heart rate demand value and the heart rate estimate; and determining an improved heart rate estimate for the individual based on the heart rate demand value and the heart rate estimate.

Abstract: Method, apparatus and system for determining a wearing position of a wearable device. The method includes receiving motion data from a wearable device associated with an individual, wherein the motion data comprises a first data segment and a second data segment, the first data segment associated with walking, and the second data segment associated with non-walking. The method further includes determining first characteristic data associated with the first data segment and second characteristic data associated with the second data segment. The method further includes determining, by a computing device, a wearing position of the wearable device associated with the individual based on the first characteristic data and a match between the second characteristic data and an activity signature associated with a time of day.

Abstract: A method for measuring body movement with a wearable device. The method includes receiving, from a magnetic sensor of the wearable device, first signal data indicative of a magnetic field of a wearable magnet, wherein the wearable device is placed on a first body segment and the wearable magnet is placed on a second body segment; determining, by a computing device based on the first signal data, a distance metric indicative of a relative distance between the wearable magnet and the wearable device, and an orientation metric indicative of an orientation relative to the wearable magnet; and determining a biomechanical model based on the type of body segment for the first body segment and the second body segment, wherein the biomechanical model includes a relationship constraint between the distance metric and the orientation metric.

Abstract: Systems and methods determining measurement confidence for data collected from sensors of a wearable device are herein disclosed. In an implementation, a confidence measurement that a wearable device is worn by a user can be determined by determining that the wearable device is not in motion, comparing sample voltages collected using a light emitter to thresholds indicative of a surface on which the light is being reflected, calculating a signal quality metric using data collected using a pulse oximeter, and comparing the signal quality metric to thresholds indicative of typical biometric data measurements. Other implementations for confidence measurement can include frequency transforming signal data stored in a buffer, performing probabilistic modelling on the frequency transformed data, and determining a confidence measurement using a signal quality estimation based on the modelled data.