Technologies for evaluating a user's musculoskeletal health and prescribing treatment plans for the user based on the musculoskeletal health is provided. The disclosed techniques include a computing system receiving input data from a client device. The system formatting the input data to generate one or more sets of user characteristic data values and determining initial levels of user musculoskeletal health for the user based upon the sets of user characteristic data values. The system selects a set of treatment plans for the user based on the initial levels of user musculoskeletal health. The system presents the set of treatment plans to the user using media that allows the user to interactively perform exercises prescribed in the treatment plan.

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.

A method of generating a calibration factor is described. In an example, the method may include establishing a wireless communication connection with a fitness machine. The method may also include obtaining first machine data from the fitness machine. The first machine data may specify a type of the fitness machine. The method may also include initiating a workout session on the wearable computer based on the first machine data. During the workout session, the method may also include obtaining sensor data, obtaining second machine data, and determining the calibration factor based on the second machine data and the sensor data. The method may also include generating a workout session summary based on the calibration factor.

A method for displaying archived exercise classes comprising displaying information about archived exercise classes that can be accessed by a first user via a computer network on a display screen at a first location, wherein the first user can select among a plurality of archived classes, outputting digital video and audio content comprising the selected archived class, detecting a performance parameter for the first user at a particular point in the selected class, displaying the performance parameter on the display screen, and displaying performance parameters from a second user at a second location on the display screen such that at least one of the performance parameters from the first user and at least one of the performance parameters from the second user at the same point in the class are presented for comparison.

Fitness tracking devices and methods of operating the same. The fitness tracking device includes a sensor circuit to generate sensor data; a processor coupled to the sensor circuit; and a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: buffer sensor data associated with motion of the user limb; generate an exercise prediction based on a prediction model and the sensor data, the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about at least one sensor axis, wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb; and transmit a signal representing the exercise prediction for display on a user interface.

An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.

Methods and systems are provided for measuring anatomical dimensions from a single two-dimensional (2D) digital image. The digital image is taken from the front/anterior view using a mobile, handheld communication device. The linear measurements are used to estimate the body volume of the individual. Total body density is calculated from estimated body volume and body weight. Body composition (fat mass and fat-free mass) of the individual is derived from density using known mathematical conversion formulas. A method for estimating body composition analysis is provided. Systems and methods are provided that determine an individual's fitness by determining body composition of the individual using a picture of the individual, determining the individual's VO.sub.2 max based on the individual's movement (e.g., walking and/or running a measured distance over a measured amount of time), and determining the individual's fitness based on the body composition and the determined VO.sub.2 max.

A method for updating a treatment plan. The treatment plan is associated with a user using a treatment apparatus to perform the treatment plan. The method includes receiving first data associated with a first diagnosis of the user. The method includes generating, based on the first data, an initial treatment plan to be performed on the treatment apparatus by the user. The method includes receiving second data associated with a first attribute of the user. The method includes generating, via an artificial intelligence engine, a machine learning model trained to generate an updated treatment plan based on the initial treatment plan and the second data.

The present invention relates to selectively adjustable speed and incline levels for treadmills. An example treadmill includes a platform around which a belt rotates, a drive motor for controlling a speed of rotation of the belt, a linear motor for controlling an incline of the platform, a human machine interface configured to receive from a user a first selection regarding at least one of the speed of rotation of the belt and the incline of the platform, at least one manual lever configured to receive from the user a second selection to respectively refine the first selection, and at least one controller that selectively changes the speed of rotation of the belt or the incline of the platform based on the first selection received by the human machine interface, and selectively and respectively refines the first selection based on the second selection received by the at least one manual lever.