Under Armour, Inc.

Myfitnesspal, Inc.

Under Armor, Inc.

F Grant Kovach

Mark A Oleson

Jeffrey Allen

Michael Mazzoleni

Nathan Dau

Angela Nelligan

Jason Berns

Samuel J Hollenbach

Kevin Patrick Fallon

Daniel Sargeant

John Martin

Tucker H Fort

Alan Guyan

Christopher M Green

Kirsten Jane Climer

Matthew Trexler

Mari Lucero

Jeff Knight

Bradford J Fults

Paul Winsper

Yvonne Lin

Jeffrey Knight

Chirstopher Green

Gamir Shrestha

Tara Marie Marchionna

Tara Machionna

Angela Ferg

A method of operating a fitness tracking system includes receiving at least one first value and at least one second value for at least one foot action parameter from a sensor during a user workout. The method further includes determining whether the user has experienced a first type of fatigue and a second type of fatigue based on the at least one first value and the at least one second value, wherein the first type of fatigue is defined by different rules than the second type of fatigue. When it is determined that the user experienced the first type of fatigue, a first recommended action is provided to the user via a personal electronic device of the user. When it is determined that the user experienced the second type of fatigue, a second recommended action is provided to the user via the personal electronic device.

System and method for detecting fatigue and providing coaching in response

A method of operating a fitness tracking system includes generating movement data corresponding to movement of a user, sampling the generated movement data at a first sampling rate as first sampled data when operating the fitness tracking system in an activity detection mode, and sampling the generated movement data at a second sampling rate as second sampled data when operating the fitness tracking system in a workout mode. The second sampling rate is greater than the first sampling rate.

Fitness tracking system and method of operating a fitness tracking system

A fitness tracking system includes a shoe, a magnetometer, and a controller. The magnetometer is mounted on the shoe and is configured to generate three-axis direction data in response to movement of the shoe during a predetermined time period. The controller is operably connected to the magnetometer and is configured to generate two-axis calibrated direction data based on the three-axis direction data after the predetermined time period. The two-axis calibrated direction data corresponds to an orientation of the shoe during the predetermined time period.

System and method for tracking movement

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model.

System and method for estimating cardiorespiratory fitness

A fitness tracking system for generating movement variables corresponding to movement of a user includes a monitoring device, a personal electronic device, and a remote processing server. The monitoring device is configured to be worn or carried by the user and includes a movement sensor configured to collect movement data. The personal electronic device is operably connected to the monitoring device. At least one of the personal electronic device and the monitoring device is configured to calculate feature data by applying a set of rules to the movement data, to calculate raw speed data corresponding to a speed of the user from the subset of the movement data, and to calculate raw distance data corresponding to a distance moved by the user from the subset of the movement data. The remote processing server includes a machine learning model for processing at least the feature data.

System and method for estimating movement variables

System and method for generating route data

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model, and displaying the first and second CRF levels.

A shoe includes: a heel portion; a toe portion; a sole portion; and an electrical assembly. The electrical assembly includes at least: a magnetometer configured to determine a direction of movement of the shoe; and an accelerometer configured to determine acceleration of the shoe. Direction and acceleration data are used to generate a display of a map indicating traversal of a route by a wearer of the shoe at user interface of a user device.

A fitness tracking system includes a shoe, a monitoring device, and a controller. The monitoring device is mounted on the shoe and includes an accelerometer configured to generate acceleration data corresponding to acceleration of a foot received by the shoe. The controller is operably connected to the accelerometer and is configured to collect sampled acceleration data by sampling the generated acceleration data, to identify foot strike data of the sampled acceleration data, to identify a local minimum of the sampled acceleration data collected prior to the foot strike data, and to determine foot strike characteristic data corresponding to the foot strike data based on an acceleration value at the local minimum.

System and method for determining foot strike pattern

A receiver apparatus is configured to receive a first and second signal from one or more sensors, the first signal comprising a measure of one or more exercise parameters of the user at a first time period of an exercise session, and the second signal comprising a measure of the one or more exercise parameters at a second time period. A processor is configured to store the first and second signals in a memory, generate a fatigue signal based on a comparison of the measured one or more exercise parameters of the first signal to the respective measured one or more exercise parameters of the second signal, generate an exercise efficiency signal based on a comparison of the measured one or more exercise parameters of the second signal to an a priori exercise parameter threshold, and cause the exercise efficiency signal and the fatigue signal to be simultaneously displayed.

System and method for monitoring efficiency versus fatigue

A physiological monitoring system includes a first handheld computing device, a second handheld computing device, and a biometric sensor device provided on an article of apparel. A method of operating the physiological monitoring system comprises receiving, at the second handheld computing device, user physiological data transmitted from the biometric sensor device, the second handheld computing device configured for wireless communication with the biometric sensor device and the first handheld computing device according to a communications protocol in a device network. The method further comprises operating the second handheld computing device in a first mode wherein the second handheld computing device operates as a slave controlled by the first handheld computing device in the device network. The method also comprises operating the second handheld computing device in a second mode wherein the second handheld computing device operates independent of the first handheld computing device in the device network.

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Baltimore, MD, United States of America

F Grant Kovach