Patent application number | Description | Published |
20140074424 | USING MAGNETOMETER DATA TO CALCULATE ANGULAR RATE MEASUREMENT BIAS - Implementations are disclosed for using magnetometer measurements to estimate bias for angular rate measurements provided by an angular rate sensor (e.g., a gyro sensor). In some implementations, a bias estimator running on a device is configured to determine if the device is rotating based on the magnetometer measurements. If the device is not rotating, a dynamic bias is calculated and added to a temperature compensated static bias to provide a total angular rate measurement bias. The total angular rate measurement bias can be provided to an attitude estimation system where it is used to update an attitude (orientation) of the device. In some implementations, the angular rate measurements are used to determine if the device is oscillating according to a threshold value. If the device is not rotating and the device is oscillating according to a threshold value, the static bias is updated in a calibration table. | 03-13-2014 |
20140364102 | Determination of Device Body Location - In some implementations, a mobile device can analyze motion sensor data during a voice call to determine whether the mobile device is on a stationary object or worn on a user's body (e.g., in the lap or pocket of a user of the mobile device). The mobile device can adjust the transmit power level of the telephony transceiver during the voice call based on the determination | 12-11-2014 |
20140364162 | Determination of Device Body Location - In some implementations, a mobile device can analyze motion sensor data and proximity sensor data during a voice call to determine whether the mobile device is on a stationary object or worn on a user's body (e.g., in the lap or pocket of a user of the mobile device). The mobile device can adjust the transmit power level of the telephony transceiver during the voice call based on the determination. | 12-11-2014 |
20140365169 | Adjusting Step Count to Compensate for Arm Swing - In some implementations, a mobile device can receive a motion signal from a motion sensor on the mobile device. The mobile device can determine a step count based on the motion signal. The mobile device can transform the motion signal from a time domain signal into a frequency domain signal. The mobile device can determine a dominant peak and harmonic peaks of the motion signal within a pedestrian frequency band. The mobile device can determine that the dominant peak corresponds to an arm swing of a user and adjust the step count to compensate for the arm swing. | 12-11-2014 |
20140365803 | Motion Fencing - In some implementations, a mobile device can be configured with virtual motion fences that delineate domains of motion detectable by the mobile device. In some implementations, the mobile device can be configured to invoke an application or function when the mobile device enters or exits a motion domain (by crossing a motion fence). In some implementations, entering or exiting a motion domain can cause components of the mobile device to power on or off (or awaken or sleep) in an incremental manner. | 12-11-2014 |
20150285659 | AUTOMATIC TRACK SELECTION FOR CALIBRATION OF PEDOMETER DEVICES - A calibration track to use for pedometer calibration can be automatically selected based on detecting sustained locomotion activity and an ability to obtain and maintain a reliable location fix over a calibration period. Calibration tracks can be generated, rated for quality, and used to compute calibration parameters to convert accelerometer data to stride length and/or distance traveled. Quality of a calibration can be assessed, and old and new calibration parameter sets can be combined based on quality weights assigned to each. Calibration parameters can be separately maintained for different locomotion activities and/or different on-body locations of the pedometers. Pedometer devices can also cooperatively calibrate each other. | 10-08-2015 |
20150350822 | Electronic Devices with Motion Characterization Circuitry - An electronic device may include a motion sensor for detecting movement of the electronic device. Applications that run on the electronic device such as fitness applications or activity logging applications may use motion sensor data to track a user's physical activity. To avoid mischaracterizing a user's movement, motion sensor circuitry in the electronic device may supplement motion sensor data with additional information in instances where motion sensor data alone may be insufficient to distinguish between different types of physical activity. For example, information on a user's speed may be synthesized with motion sensor data to help characterize a user's movement. Information on a user's speed may be determined based on location information. The location information may, for example, be gathered using IEEE 802.11 transceiver circuitry or, in more rural areas, may be gathered using Global Positioning System receiver circuitry. | 12-03-2015 |
20160058302 | LATENT LOAD CALIBRATION FOR CALORIMETRY USING SENSOR FUSION - 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. | 03-03-2016 |
20160058329 | METHOD AND SYSTEM TO ESTIMATE DAY-LONG CALORIE EXPENDITURE BASED ON POSTURE - 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. | 03-03-2016 |
20160058332 | LOCAL MODEL FOR CALORIMETRY - 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 for at least one of battery power, processor speed, and memory capacity. | 03-03-2016 |
20160058356 | METHOD AND SYSTEM TO CALIBRATE FITNESS LEVEL AND DIRECT CALORIE BURN USING MOTION, LOCATION SENSING, AND HEART RATE - 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. | 03-03-2016 |
20160058370 | ACCURATE CALORIMETRY FOR INTERMITTENT EXERCISES - 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. | 03-03-2016 |
20160058371 | SENSOR FUSION APPROACH TO ENERGY EXPENDITURE ESTIMATION - 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. | 03-03-2016 |
20160058372 | TERRAIN TYPE INFERENCE FROM WEARABLE WITH MOTION SENSING - 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. | 03-03-2016 |
20160069679 | Electronic Devices with Pressure Sensors for Characterizing Motion - An electronic device may include a motion sensor for detecting movement of the electronic device and a pressure sensor for detecting changes in elevation of the electronic device. Applications that run on the electronic device such as health and fitness applications may use motion sensor and pressure sensor data to track a user's physical activity. For example, processing circuitry in the electronic device may use the motion sensor to track a user's steps and the pressure sensor to track changes in the user's elevation. The processing circuitry may determine whether the user is climbing stairs based on the user's step rate and the user's changes in elevation. When the processing circuitry determines that the user is climbing stairs, the processing circuitry may use the pressure sensor and motion sensor to track and store the number of flights of stairs climbed by the user. | 03-10-2016 |