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.

An adjustable climbing wall system enabling the arrangement of a force sensor with an attached climbing hold in various positions attached to a frame or wall. The system comprises a subframe element that is configured to a couple with a force plate. The subframe element is configured to be adjustable from a first position to a second position. The force plate is configured to couple with a climbing hold and the force sensor is configured to output a signal indicative of the forces applied to the climbing hold.

A device for training and rehabilitation of a limb is provided. The device provides a board with a plurality of movement tracks to allow for controlled movement of the limb in various directions. Blockers and other controlling structures may be arranged on the device to limit range of motion of the movement of the limb.

A method includes identifying a first point on an object striking element in a first image and a second image from images. The first and second images are captured within a predetermined time span of an impact of the object with the element and determining an impact time of the impact in combination with determining a position in the first and/or second images corresponding to a location of the object at the impact time. Based on the positions of the first point in the first and second images, determining a position in the first and/or second images corresponding to a position of the first point at the impact time. The method also includes determining a distance from the imager to a location at which the object and the element impact one another and determining an impact location of the object relative to the first point.

An electronic device and method for data-driven assistance for users involved in physical activities is provided. The electronic device receives first sensor data associated with a movement pattern of one or more parts of a body of a user and receives first information associated with a location where the user performs the physical activity. The electronic device determines one or more first indicators which are likely to have affected the user or the performance of the user in the physical activity. Thereafter, the electronic device generates presentation data based on an application of a first machine learning model on the determined one or more first indicators and the received first information. The generated presentation data includes one or more improvement suggestions for the user in relation to the physical activity. The electronic device controls a display device to display the presentation data.

Provided are a bilateral limb coordination training system and a bilateral limb coordination training control method. The bilateral limb coordination training control method includes: detecting, by a force sensor, force information of a three degree-of-freedom bilateral motion mechanism and sending the force information to a main controller; determining, by the main controller, target motion information of the three degree-of-freedom bilateral motion mechanism of a robot according to the force information; and controlling, by the main controller, the three degree-of-freedom bilateral motion mechanism to perform a corresponding motion based on the target motion information and sending training data and running status information generated during training to an upper computer; where a training instruction includes a bilateral non-association motion training instruction, a bilateral flexible-association motion training instruction or a bilateral rigid-association motion training instruction.

Examples are disclosed that relate to methods, computing devices, and systems for determining a user's readiness for an operational task. One example provides a method comprising, during a training phase, receiving training input data including, for each user training session, a training data pair including, as input, a distance of travel, a mode of travel, and one or more environmental conditions, and as ground truth output, a time elapsed for performance of the training task. An artificial intelligence (AI) performance model is trained to model user performance of the training task based on the training data pairs. During a run-time phase, operational input data is received that is associated with an operational task performed by the user. The performance model is used to infer a predicted time elapsed for performance of the operational task.

A method determines running phases of a user on a treadmill having first and second rotating elements rotating about respective rotational axes transverse to a longitudinal axis of the treadmill. A physical exercise surface connects to the rotating elements. An electric motor operatively associated with one rotating element rotates the rotating elements and the physical exercise surface. A processor connects to the electric motor and controls the electric motor. The processor determines a magnitude variable over time, which is correlated to user interaction with the exercise surface while exercising and has a periodic trend. The processor determines time instants of at least two points of the periodic trend. The time instants of each point representing instants of user interaction with the exercise surface. The processor determines a running phase of the user on the exercise surface as a function of the respective time instants of the periodic trend.

Disclosed are an electronic device for generating workout types and a method of operating the electronic device. According to an embodiment, an electronic device includes: a memory and at least one processor connected to the memory, wherein the at least one processor is configured to: acquire a workout type name in response to a request for adding a workout type, recommend at least one data type based on the workout type name, generate a new workout type corresponding to the workout type name based on a data type selected from among the at least one recommended data type, and acquire workout measurement information corresponding to the selected data type in response to execution of the new workout type.