Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

The present disclosure is directed towards methods for calculating disease progression rates and sojourn times of solid tumors from metabolic markers and using this calculation to optimize patient-specific diagnosis, scheduling of screening procedures, and dosage or frequency of treatment.

An earpiece module includes a housing configured to be attached to an ear of a person, a first audio sensor within the housing configured to detect auscultatory sounds from an ear canal of the ear and generate a physiological information signal from the auscultatory sounds, and a second audio sensor within the housing and oriented in a direction towards an outside environment of the person. The second audio sensor is configured to detect sounds external to the person including voice sounds and footstep sounds, and to generate an environmental information signal from the external sounds. A processor is configured to receive the physiological information signal and the environmental information signal, process the external sounds in the physiological information signal and the environmental information signal to reduce the voice sounds and the footstep sounds from the physiological information signal and generate a cleaner physiological information signal.

An exercise feedback system generates biofeedback based on physiological adaptations. The exercise feedback system processes physiological data from sensor-equipped garments worn by athletes while performing exercises. The exercise feedback system may use a trained model to determine classifications of segments of the physiological data. Classifications may represent a type of physiological adaptation, for example, power, strength, hypertrophy, endurance, or speed. Athletes can focus on one or more physiological adaptations, which may be based on a specific sport or training goal of an athlete. The exercise feedback system may also use other types of sensor data from the garments such as motion data or bioimpedance information. The exercise feedback system can generate biofeedback including metrics determined using the classifications. For example, the metrics indicate training load aggregated over multiple muscles or workouts, or the biofeedback may notify athletes regarding a risk of injury.

Methods and apparatuses for athletic training optimization are disclosed. In one example, a fitness level change is identified. In one example, a current training intensity is updated to reflect an updated user fitness level.

Embodiments are related to systems and methods for data determining information about a subject, and more particularly to systems and methods for utilizing and distributing information related to measurements of a human subject.

A system, method and corresponding computer program for quantifying physical fatigue are provided, the system comprising: a physiological measure providing unit (20) for providing a physiological measure of the subject, a fatigue index determination unit (200) for determining a fatigue index of the subject. The fatigue index determination unit (200) comprises a first fatigue index determination subunit (210) for determining a first fatigue index based on the physiological measure and a second fatigue index determination subunit (240) for determining a second fatigue index based on the physiological measure. The first fatigue index and the second fatigue index have a respectively different characteristic based on the physiological measure. The system, method and corresponding computer program improve the quantifying of physical fatigue of a subject.

An exercise feedback system monitors the performance of athletes wearing a garment with sensors while exercising. The sensors generate physiological data such as muscle activation data, heart rate data, or data describing the athlete's movement. The system extracts features from the physiological data and compares the features with reference exercise data to determine metrics of performance and biofeedback. Based on the physiological data, the system may also modify exercise training programs for the athlete. The exercise feedback system can display the biofeedback using visuals or audio, as well as modified exercise training programs, via the athlete's client device in real time while the athlete is exercising. By reviewing the biofeedback, the athlete may correct the athlete's exercise form to properly use the target muscles for the exercise, or change the certain workouts to personalize the training program.

Wearable apparatus for monitoring various physiological and environmental factors are provided. Real-time, noninvasive health and environmental monitors include a plurality of compact sensors integrated within small, low-profile devices, such as earpiece modules. Physiological and environmental data is collected and wirelessly transmitted into a wireless network, where the data is stored and/or processed.

A universal 6-DOF mems sensor combined with six degree of motion algorithms and human motion parameters permits individualized real time motion analysis of a user to enable accurate measurements. Data derived thereby is wirelessly sent for viewing to a Bluetooth enabled smartphone or combination smartphone and eyeglass device, such as the Google Glass headset. The sensor is worn on a wrist or ankle band or in combination with a chest mounted cardio heart rate monitor dependent on the biometric parameters measured. Typical physical exercise data gathered includes reps, sets, 10-100 yard dash times, vertical, horizontal and broad jump distances, a range of shuttle times, RAST, steps taken, distance traveled, velocity, acceleration, and calories burned. The heart rate monitor provides cardio assessment and the 6-DOF sensor measures a runner's pace and cadence data.