A method includes causing display, during a first time period and via a first set of multiple smart mirrors, of live video depicting at least one user associated with the first set of multiple smart mirrors, without displaying a workout video. The method also includes causing display, during a second time period following and mutually exclusive of the first time period, and via a second set of multiple smart mirrors, of a workout video and a representation of at least one user associated with the second set of smart mirrors. The method also includes causing display, during a third time period following and mutually exclusive of the second time period, and via a third set of multiple smart mirrors, of live video depicting at least one user associated with the third set of multiple smart mirrors.

A method includes causing display, during a first time period and via a first set of multiple smart mirrors, of live video depicting at least one user associated with the first set of multiple smart mirrors, without displaying a workout video. The method also includes causing display, during a second time period following and mutually exclusive of the first time period, and via a second set of multiple smart mirrors, of a workout video and a representation of at least one user associated with the second set of smart mirrors. The method also includes causing display, during a third time period following and mutually exclusive of the second time period, and via a third set of multiple smart mirrors, of live video depicting at least one user associated with the third set of multiple smart mirrors.

Systems and methods for positioning one or more sensors on a user. The system has user sensors, apparatus sensors, and treatment sensors. A processing device, executing computer readable instructions stored in a memory, cause the processing device to: generate an enhanced environment representative of an environment; receive apparatus data representative of a location of the apparatus in the environment; generate an apparatus avatar in the enhanced environment; receive user data representative of a location of the user in the environment; generate a user avatar in the enhanced environment; receive treatment data representative of one or more locations of the treatment sensors in the environment; generate, treatment sensor avatars in the enhanced environment; calculate a treatment location for each treatment sensor, wherein the treatment location is associated with an anatomical structure of a user; and generate instruction data representing an instruction for positioning the treatment sensors at the treatment location.

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.

An earpiece module includes a physiological sensor, an external energy sensor, a transceiver, a communication module, a data storage component, and a power source. The communication module includes a microphone, a speaker, and a signal processor. The signal processor processes audio information received from a remote source via the transceiver and communicates the processed audio information to a subject via the speaker. The signal processor processes information in real time from the physiological sensor and the external energy sensor, and the signal processor provides biofeedback to the subject based on signals produced by the physiological sensor. The data storage component includes a plurality of algorithms. At least one algorithm focuses processing resources on extracting physiological information from the physiological sensor, at least one algorithm is configured to be modified or uploaded wirelessly via the transceiver, and at least one algorithm is a compression/decompression (CODEC) algorithm.

Automated systems and methods are presented for determining the physiological response of human or suitable animal subjects to physical exertion. The methods and systems can include monitoring sensors that capture the motion of the subject along with corresponding physiological data, and can track such motion for the duration of a period of physical exertion. The system is able to acquire an initial stream of physiological data from the subject during a range of physical exertion activities that are representative of the events intended to be monitored with the proposed method and system, enabling a corresponding dynamic physiological response model to be created. The motion tracking system and physiological response model can then be used to predict the physiological response to physical exertion events under a prescribed framework, including applications during real-time event monitoring.

Automated systems and methods are presented for determining the physiological response of human or suitable animal subjects to physical exertion. The methods and systems can include monitoring sensors that capture the motion of the subject along with corresponding physiological data, and can track such motion for the duration of a period of physical exertion. The system is able to acquire an initial stream of physiological data from the subject during a range of physical exertion activities that are representative of the events intended to be monitored with the proposed method and system, enabling a corresponding dynamic physiological response model to be created. The motion tracking system and physiological response model can then be used to predict the physiological response to physical exertion events under a prescribed framework, including applications during real-time event monitoring.

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 apparatus adapted to be worn at or near at least one ear of a subject includes a battery, a reflective pulse oximeter, a motion sensor, an analog-to-digital convertor configured to convert analog signals from the reflective pulse oximeter and the motion sensor into digitized information, a speaker, a digital memory device configured to store at least one algorithm for signal processing, a transceiver, and a signal processor. The signal processor is configured to process data from the reflective pulse oximeter to monitor cardiopulmonary functioning of the subject, process data from the motion sensor to monitor head and body motion, execute the at least one algorithm for assessing a health state of a subject, poll the reflective pulse oximeter and the motion sensor at certain time intervals to extend life of the battery, and process digital audio information into analog sounds to be presented to the subject via the speaker.

A control system for controlling one or more of a plurality of exercise apparatuses across a network comprises a processor; a communication subsystem configured to communication with the plurality of exercise apparatuses across the network; and memory for storing information about one or more users. The information comprises, for each user, identity information, including a user identifier, and a resistance level indicator. Upon receipt of a user identifier from an exercise apparatus in the network, the processor is configured to identify the resistance level indicator stored in the memory corresponding to the user identifier, and cause the communication subsystem to transmit to the exercise apparatus the resistance level indicator for that user. Upon receipt of a performance parameter of a user from an exercise apparatus in the network, the processor is configured to determine whether or not to modify the resistance level indicator of that user stored in the memory based on the received performance parameter.