Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of operating a fitness tracking system including a plurality of sensors is disclosed herein. The method includes mounting a biometric monitoring device on an article of apparel worn by a user. The method further includes receiving a prompt indicating that the user intends to provide a verbal cue via a microphone provided on the biometric monitoring device. After receiving the verbal cue from the user one of a plurality of exercise modules is selected for execution by the processor. Each of the plurality of exercise modules is configured to generate workout metrics based at least in part on physiological data received from a first of the plurality of sensors without regard to physiological data from a second of the plurality of sensors. The selected exercise module generates workout metrics for the user for a limited period of time ranging from selection of the exercise module until occurrence of a termination event.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of operating a fitness tracking system including a plurality of sensors is disclosed herein. The method includes mounting a biometric monitoring device on an article of apparel worn by a user. The method further includes receiving a prompt indicating that the user intends to provide a verbal cue via a microphone provided on the biometric monitoring device. After receiving the verbal cue from the user one of a plurality of exercise modules is selected for execution by the processor. Each of the plurality of exercise modules is configured to generate workout metrics based at least in part on physiological data received from a first of the plurality of sensors without regard to physiological data from a second of the plurality of sensors. The selected exercise module generates workout metrics for the user for a limited period of time ranging from selection of the exercise module until occurrence of a termination event.

Abstract: 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.

Abstract: A fitness tracking system includes a receiver to obtain geolocation data. A method is used to determine the quality of the geolocation data by analyzing the dispersion of the geolocation coordinates during the user fitness activity. The geolocation data is used for calculating exercise metrics and displaying fitness activity when the geolocation data quality satisfies the data quality criteria.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.