Embodiments are disclosed for a wearable computer with fitness machine connectivity for improved activity monitoring. In an embodiment, a method comprises: establishing, by a processor of a wireless device, a wireless communication connection with a fitness machine; obtaining, by the processor, machine data from the fitness machine; determining, by the processor, a workout session according to the machine data; initiating the workout session on the wireless device; during the workout session: obtaining, from a sensor of the computing device, physiological data of a user of the fitness machine; determining, by the processor, fitness data for the user based on the physiological data, the machine data and at least one user characteristic; and sending, by the processor, the fitness data to the fitness machine.

A performance scanning system that operates to detect and measure surface parameters of a portion of an athlete and uses the surface parameters to determine the likelihood that the athlete's physical performance has been or will be impaired.

A force measurement system is disclosed herein. The force measurement system includes a force measurement assembly configured to receive a subject, and one or more data processing devices operatively coupled to the force measurement assembly. In one embodiment, the one or more data processing devices are configured to determine a difference between a center of pressure and a center of mass for the subject from output force and/or moment data, and to determine the fall risk of the subject based upon the difference between the center of pressure and the center of mass for the subject. In another embodiment, the one or more data processing devices are configured to determine one or more balance parameters of the subject from the output force and/or moment data while the subject is stepping onto the top surface of the force measurement assembly and/or stepping off the top surface of the force measurement assembly.

A method for optimizing at least one exercise. An exercise apparatus is configured to enable a user to perform the at least one exercise. The method includes receiving user data. The method includes generating, based on the user data, initial target data. The method includes receiving measurement data associated with one or more sensors. The method includes determining, differential data. The determining is based on one or more differences between the initial target data and the measurement data. The method includes receiving cohort data. The method includes generating, via an artificial intelligence engine and based on the differential data, a machine learning model trained to generate message data based on a difference between the differential data and the cohort data. The method includes transmitting, to an interface associated with a user, a message to the user based on the message data.

A method includes receiving treatment data associated with a user capable of using a treatment device to perform a treatment plan. The method also includes receiving alignment data associated with the user while the user engages in at least one activity and receiving at least one alignment characteristic associated with the user and determining, using at least the at least one alignment characteristic, whether the at least one alignment characteristic correlates with at least one secondary condition of the user. The method also includes generating secondary condition information indicating at least the secondary condition and modifying at least one aspect of the treatment plan in response to receiving, from a healthcare professional, treatment plan input. The treatment plan input includes at least one modification to the at least one aspect of the treatment plan and wherein, further, the treatment plan input is generated based on the secondary condition information.

Systems, methods, and computer-readable mediums for generating, by an artificial intelligence engine, an exercise program comprising a first user energy score, wherein the method comprises generating, by the artificial intelligence engine, the exercise program including an exercise plan including a plurality of exercises. Each respective exercise is associated with user energy consumption metrics based at least on a metabolic equivalent of task (MET) value, and based on the user energy consumption metrics, the first user energy score is associated with the exercise program. The method includes receiving data pertaining to a plurality of users. The data includes physical activity goals the plurality of users desires to achieve. The method includes determining second user energy scores for the physical activity goals, and based on the first and second user energy scores, assigning, by the artificial intelligence engine, at least a subset of the plurality of users to the exercise program.

Embodiments provide functionality for determining fitness level of an individual using a fitness testing unit. In one such embodiment, an individual is instructed, via a graphical user interface (GUI) of a fitness testing unit, to perform a series of exercise activities. In turn, via the fitness testing unit, baseline heart rate, maximum heart rate, and recovery heart rate are captured while the individual is performing the series of exercise activities. Then, a fitness level of the individual is determined using an age of the individual and the captured baseline heart rate, maximum heart rate, and recovery heart rate.

A computer-implemented method for machine learning and prediction relating to exercise by a user during a bout of exercise may include providing a physical activity measurement device that includes at least one input and at least one output; providing a machine learning model; via the at least one input, collecting physiological data from the user; adding the physiological data acquired by the collecting to the user's physiological data set; training the machine learning model on the user's said physiological data set; making at least one prediction for the user based on the application of the machine learning model to at least one input during the bout of exercise, and communicating at least one prediction to the user via at least one output.

Computing device enhanced training environment system comprising a computing device, I/O subsystem for permitting a user to enter at least one attribute of the training or of the trainee, a plurality of sensors for generating sensory information, a training environment in which a training activity takes place, a database containing training related information. The computing device enhanced training environment system configured for at least one of the following: design a training program for a plurality of users, monitor training program performance, monitor training performance, instruct a user about the training, determine and/or set difficulty level in training apparatus.

Methods and systems for calibrating a level of intensity of a fitness exercise given a target effort value facilitate optimal exertion for an individual performing the fitness exercise. An instantaneous effort value is determined based on measured real-time and historical data and communicated to the individual. An aspect of the fitness exercise is adjusted based on a difference between the instantaneous effort value and a target effort value.