Abstract: Systems and methods are provided for gathering and generating heart rate data to determine the user's current physical condition. On such system include a wearable device having a bio sensor that measures biometrics and a motion sensor that monitors activity. The system also includes a processor coupled to the bio sensor and the motion sensor, and a transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. The instructions are executed to cause the processor to generate biometric data when executed, cause the processor to execute specific functions.

Abstract: Systems, methods, and devices are provided for determining exertion as a measure of accumulated exercise intensity. One such system includes a wearable device comprising a biosensor that periodically measures biometrics (e.g. heart rate); a processor operatively coupled to the biosensor, the processor configured to process electronic signals periodically generated by the biosensor; and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. In particular, the instructions are executed to cause the processor to generate biometric data from the biometrics, to generate a plurality of exercise intensity values based on the biometric data (e.g.

Abstract: Systems, methods, and devices are provided for determining an exercise program recommendation for an anticipated exercise session. One such system includes a wearable device comprising a biosensor that monitors biometrics (e.g. heart rate); a motion sensor that monitors activity; a processor operatively coupled to the biosensor, the processor configured to process electronic signals periodically generated by the biosensor and the motion sensor; and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. In particular, the instructions are executed to cause the processor to generate biometric data from the biometrics (e.g. heart rate information in particular).

Abstract: Systems, methods, and devices are provided for determining an exertion recommendation for an anticipated exercise session. One such system includes a wearable device comprising a biosensor that monitors biometrics (e.g. heart rate); a motion sensor that monitors activity; a processor operatively coupled to the biosensor, the processor configured to process electronic signals periodically generated by the biosensor and the motion sensor; and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. In particular, the instructions are executed to cause the processor to generate biometric data from the biometrics (e.g. heart rate information in particular). Further, the instructions are executed to generate an exertion recommendation based on an exertion model created from and representing a relationship between prior exertion measures and prior response profile measures.

Abstract: Systems and methods are provided for determining performance capacity. One such system includes a wearable device having a biosensor that measures biometrics and a motion sensor that monitors activity. The system also includes a processor coupled to the biosensor and the motion sensor, and a non-transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. The instructions are executed to cause the processor to generate biometric data from the biometrics and activity data from the activity. Further, the instructions are executed to create a response profile based on one or more of a heart rate variability (HRV) score based on the biometric data, a fatigue score based on the activity data, a predicted HRV score based on the biometric and activity data, and a predicted fatigue score based on the biometric data and/or the activity data.

Abstract: Systems and methods are provided for creating a neural network to provide personal recommendations. One such system include a wearable device having a bio sensor that measures biometrics and a motion sensor that monitors activity. The system also includes a processor coupled to the bio sensor and the motion sensor, and a transitory computer-readable medium operatively coupled to the processor and storing instructions that, when executed, cause the processor to execute specific functions. The instructions are executed to cause the processor to generate biometric data when executed, cause the processor to execute specific functions. The instructions are executed to cause the processor to monitor movement to generate physical activity data, monitor a duration and quality of sleep to generate sleep data, and gather biometric data to determine a fatigue score.

Abstract: A system for providing a training load schedule for peak performance positioning includes an apparatus for providing a training load schedule for peak performance positioning. The apparatus includes an initial load schedule module that provides an initial load schedule. The apparatus also includes a fatigue level module that detects a fatigue level. In addition, the apparatus includes a dynamic load schedule module that creates and updates a dynamic load schedule by modifying the initial load schedule based on the fatigue level.

Abstract: A system for providing an interpreted recovery score includes an apparatus for providing an interpreted recovery score. The apparatus includes a fatigue level module that detects a fatigue level. In addition, the apparatus includes a dynamic recovery profile module that creates and updates a dynamic recovery profile based on an archive. The archive includes historical information about the fatigue level. The apparatus also includes an interpreted recovery score module that creates and updates an interpreted recovery score based on the fatigue level and the dynamic recovery profile.

Abstract: A system for creating and dynamically updating a user activity profile includes an earphone, a processor, a sensor module, a memory module. The memory module includes stored computer program code that, along with the memory module and the processor, is configured to carry out a number of operations to create and dynamically update the user activity profile. One such operation involves maintaining an activity archive that includes activity information received from the sensor module and that is representative of a user's activity. Another such operation includes creating and updating a dynamic activity profile based on initial user input and further based on the activity archive. The initial user input contributes to the dynamic activity profile according to a first weighting factor, and the activity archive contributes to the dynamic profile according to a second weighting factor.

Abstract: Systems and methods for providing a sleep recommendation include providing a sleep recommendation using an earphone with a biometric sensor. The system includes a preferred sleep determination module that determines a preferred sleep duration. The system also includes a sleep debt module that creates and updates a sleep debt based on the preferred sleep duration and an actual sleep duration. In addition, the system includes a sleep recommendation module that provides a recommended sleep duration based on the sleep debt.

Abstract: Systems and methods for identifying and presenting information regarding a fitness cycle using earphones with biometric sensors are provided. Fatigue level associated with fatigue experienced in response to a stimulus and recovery from such fatigue may be determined based on heart rate variability (HRV) data and learned user characteristics. One or more cycles of fatigue and recovery can be identified as a fitness cycle(s), each fitness cycle encompassing a period of time beginning with the stimulus associated with the fitness-related activity and progressing through recovery from the fatigue experienced in response to the stimulus associated with the fitness-related activity. Information regarding the fitness cycle(s) can be presented to a user in a variety of ways, including on a display of a computing device in communication with the earphones with biometric sensors.

Abstract: Systems and methods are provided for using earphones with biometric sensors to identify and present information regarding performance periods. Fatigue level associated with fatigue experienced in response to a stimulus and recovery from such fatigue may be determined based on heart rate variability (HRV) data and learned user characteristics. One or more cycles of fatigue and recovery can be identified as a fitness cycle(s), each fitness cycle encompassing a period of time beginning with the stimulus associated with the fitness-related activity and progressing through recovery from the fatigue experienced in response to the stimulus associated with the fitness-related activity. A performance period may be predicted based on a pre-determined fatigue/recovery level instance within a fitness cycle.

Abstract: Systems and methods are disclosed for providing lifestyle recommendations using earphones with biometric sensors. In one embodiment, the system includes a pair of earphones including: speakers; a processor; a heartrate sensor electrically coupled to the processor; and a motion sensor electrically coupled to the processor. In this embodiment the system also includes a memory coupled to a processor and having instructions stored thereon that, when executed by the processor: monitor a movement of a user based on signals generated by the motion sensor; detect an activity of the user based on the monitored movement of the user; create an activity score associated with the user's movement; detect a fatigue level of the user based on signals generated by the heart rate sensor; and display on a display a lifestyle recommendation to the user based on the user's detected activity, the created activity score, and the detected fatigue level.

Abstract: Systems and methods are disclosed for providing a user a training load schedule for peak performance using earphones with biometric sensors. In one embodiment, the system includes earphones, including: speakers; a processor; a heartrate sensor electrically coupled to the processor; and a motion sensor electrically coupled to the processor. In this embodiment, the system also includes a memory coupled to a processor and having instructions stored that, when executed by the processor: display on a display an initial load schedule stored in a memory to the user; calculate a fatigue level of the user based on signals generated by the heartrate sensor; modify the initial load schedule based on the calculated fatigue level to create a dynamic load schedule for the user; and display on the display the dynamic load schedule to the user.

Abstract: Systems and methods are disclosed for providing a user an intelligent goal recommendation for an activity level using earphones with biometric sensors. In one embodiment, the system includes earphones, including: speakers; a processor; a heartrate sensor electrically coupled to the processor; and a motion sensor electrically coupled to the processor. In this embodiment, the system also includes a memory coupled to a processor and having instructions stored that, when executed by the processor: monitor a movement of a user based on signals generated by the motion sensor; determine an activity score of the user based on the monitored movement of the user; detect a fatigue level of the user based on signals generated by the heart rate sensor; and display on a display an activity level recommendation based on the user's activity score, detected fatigue level, and a stored archive including historical information about the user's movement and fatigue level.

Abstract: A system for providing a smart activity score includes an earphone with a biometric sensor used for providing a smart activity score to a user. The system includes a movement monitoring module that monitors a movement to determine a metabolic loading associated with the movement during a score period. The system also includes a period activity score module that creates and updates a period activity score based on the metabolic loading and the movement. The period activity score is created and updated for the score period. In addition, the system includes a smart activity score module that creates and updates a smart activity score by aggregating a set of period activity scores.

Abstract: A system for providing a sleep recommendation includes an apparatus for providing a sleep recommendation. The apparatus includes a preferred sleep determination module that determines a preferred sleep duration. The apparatus also includes a sleep debt module that creates and updates a sleep debt based on the preferred sleep duration and an actual sleep duration. In addition, the apparatus includes a sleep recommendation module that provides a recommended sleep duration based on the sleep debt.

Abstract: Systems and methods for identifying and presenting information regarding performance periods are provided. Fatigue level associated with fatigue experienced in response to a stimulus and recovery from such fatigue may be determined based on heart rate variability (HRV) data and learned user characteristics. One or more cycles of fatigue and recovery can be identified as a fitness cycle(s), each fitness cycle encompassing a period of time beginning with the stimulus associated with the fitness-related activity and progressing through recovery from the fatigue experienced in response to the stimulus associated with the fitness-related activity. A performance period may be predicted based on a pre-determined fatigue/recovery level instance within a fitness cycle.

Abstract: Systems and methods for identifying and presenting information regarding a fitness cycle are provided. Fatigue level associated with fatigue experienced in response to a stimulus and recovery from such fatigue may be determined based on heart rate variability (HRV) data and learned user characteristics. One or more cycles of fatigue and recovery can be identified as a fitness cycle(s), each fitness cycle encompassing a period of time beginning with the stimulus associated with the fitness-related activity and progressing through recovery from the fatigue experienced in response to the stimulus associated with the fitness-related activity. Information regarding the fitness cycle(s) can be presented to a user in a variety of ways.

Abstract: A system for providing a lifestyle recommendation includes an apparatus for providing a lifestyle recommendation. The apparatus includes a movement monitoring module that monitors a movement to detect an activity and create an activity score associated with the movement. The apparatus also includes a fatigue level module that detects a fatigue level. In addition, the apparatus includes a lifestyle recommendation module that provides a lifestyle recommendation based on the activity, the activity score, and the fatigue level.