During a first time period and for a first user, a second user is automatically selected based on competitive data of the first user and competitive data of the second user, and a workout selection is sent to cause a video of a workout to be displayed during a second time period on a smart mirror of the first user and a smart mirror of the second user. During the second time period, a live stream of the first user exercising is displayed at the smart mirror of the second user, and a live stream of the second user exercising is received and displayed at the smart mirror of the first user. During the second time period, a performance score of the first user and a performance score of the second user is displayed at the smart mirrors of the first user and the second user.

A processor-implemented method includes receiving a request that specifies a workout performed at a first time, a list of users, a second time after the first time, an overlay to be displayed during a rebroadcast associated with the request, and a skill level of the workout. The request is compared to calendar data, and a session acknowledgment message is sent to the compute device of the first user based on the comparison. An invitation message is sent to compute devices of a second user and a third user, identifying the second time, and invitation responses are received from the second user and the third user. In response to the invitation responses, a video of the workout is rebroadcast at the second time, to a smart mirror of the first user, a smart mirror of the second user and a smart mirror of the third user.

A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface. and a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; identify at least one enhanced component using the treatment data; generate an enhanced environment using the at least one enhanced component and the treatment plan; output at least one aspect of the enhanced environment to at least one of the patient interface and another interface; receive subsequent treatment data pertaining to the user; and selectively modify at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan using the subsequent treatment data.

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.

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.

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.

A system includes an earpiece and a telecommunications device in communication with the earpiece. The earpiece includes a power source, a processor configured to process at least one algorithm stored within the earpiece, and an optical sensor. The optical sensor includes at least one optical emitter configured to direct optical energy to a region of an ear of a subject wearing the earpiece and at least one optical detector configured to sense absorbed, scattered, and/or reflected optical energy emanating from the ear region. The telecommunications device is configured to modify the at least one algorithm, to download additional algorithms to the earpiece, and to activate and deactivate the optical sensor.

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.

An exercise system may be configured to gather and quantify data related to energy expenditure during physical activity. The exercise system may include at least one force sensing node and a portable hub that is physically separate and distinct from the at least one force sensing node. The at least one force sensing node may be configured to gather data related to energy expenditure during physical activity. The portable hub may be configured to quantify data related to energy expended during physical activity. For example, according to exemplary implementations, the portable hub may be include a processor configured to execute an exercise analysis module. The exercise analysis module may be configured to determine one or more exercise parameters based on an output signal generated at the at least one force sensing node responsive to force exerted on the at least one force sensing node during physical activity.

We report method of detecting a pathological body state of a patient, comprising receiving a body signal of the patient; determining a body index from said body signal; determining an activity level of said patient; determining a value range for said body index for said patient, based at least in part on said activity level; comparing said body index to said value range; and detecting a pathological state when said body index is outside said value range. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.