Abstract: Systems and methods for monitoring and assessing cognitive performance are described, based on physiological data captured from one or more sensors in a user's wearable device while the user performs a task. A determination of a current level of cognitive fatigue of the user may be made based on the physiological data, and at least one recovery recommendation delivered to the user based on the current level of cognitive fatigue in order to reduce cognitive fatigue.

Abstract: Apparatus and associated methods relate to capturing physiological data from a sensor configured in a user's wearable device while the user performs a task, individualizing the physiological data to the user based on comparison with historical user physiological data, measuring the user's cognitive function determined in relationship to the individual's physiological data, and automatically notifying the user of cognitive fatigue and performance detected based on evaluating the measured cognitive function over time. In an illustrative example, the wearable device maybe a gaming headset. The measured cognitive function may be, for example, determined as a function of electroencephalograph or heart rate variability data captured from a user while the user performs a task. Some examples may provide recovery recommendations based on the detected cognitive fatigue.

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: 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 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, 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, 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 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 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: A system for identifying fatigue sources includes a processor and at least one computer program residing on the processor. The computer program is stored on a non-transitory computer readable medium having computer executable code embodied thereon. The computer executable code is configured to detect a fatigue level. The computer executable code is further configured to receive fatigue contribution data. In addition, the computer executable code is configured to identify a fatigue source based on the fatigue level and the fatigue contribution data.

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 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, 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 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: 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: 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 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 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.