A multifunctional ranging telescope includes a main body, a wind direction and wind speed sensing device, a display device and a power source. The wind direction and wind speed sensing device is movably connected to the main body and includes a sensing element. When the wind direction and wind speed sensing device moves to a sensing position, the sensing element is located outside the main body. The display device is located on a surface of the main body and electrically connected to the wind direction and wind speed sensing device to display a wind direction and wind speed information. The power source is configured to supply power to the wind direction and wind speed sensing device and the display device.

A computer-implemented system may include an electromechanical machine configured to be manipulated by a user while the user performs a treatment plan, an interface comprising a display configured to present information associated with the treatment plan, and a processing device configured to receive, from one or more data sources, information associated with the user, wherein the information comprises one or more risk factors associated with a cardiac condition or a cardiac outcome, generate, using one or more trained machine learning models, the treatment plan for the user, wherein the treatment plan is generated based on the information associated with the user, and the treatment plan comprises one or more exercises associated with managing the one or more risk factors in order to reduce a probability of a cardiac intervention for the user, and transmit the treatment plan to cause the electromechanical machine to implement the one or more exercises.

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.

An exercise device includes a frame, handlebars supported by the frame, and a computing device. The handlebars include a yoke that is movable relative to the frame, a biasing element positioned between the yoke and the frame, and a sensor configured to measure a movement of the yoke.

The present invention relates to a rehabilitation exercise system for upper and lower limbs, comprising: a rehabilitation exercise device for rehabilitation of arms or legs and an information processing terminal which is connected to the rehabilitation exercise device via a wireless communication network; wherein the rehabilitation exercise device comprises at least one type of sensors which detect a first information; wherein the information processing terminal is configured to transmit a second information to the rehabilitation exercise device via the wireless communication network; wherein the rehabilitation exercise device is configured to control a rehabilitation exercise load based on the first information and the second information. Therefore, it is possible to provide a more efficient rehabilitation by controlling a rehabilitation exercise load of the rehabilitation exercise device based on the first information detected by the rehabilitation exercise device and the second information received from the information processing terminal.

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.

A walking training system includes a treadmill, a center-of-gravity position detection unit that detects a center-of-gravity position of a trainee from a load received from a sole of the trainee on a belt of the treadmill, a posture detection unit that detects a posture of the trainee, a center-of-gravity position estimation unit that estimates the center-of-gravity position of the trainee from the detected posture of the trainee, a determination unit that determines whether a difference between the detected center-of-gravity position and the estimated center-of-gravity position exceeds a predetermined value, and a notification unit that sends, when the determination unit determines that the difference exceeds the predetermined value, a notification notifying that whether the difference exceeds the predetermined value.

A walking state measurement system includes: an endless belt that defines a walking surface on which a subject walks and that travels along a walking front-rear direction; a marker provided on at least the walking surface along a circumferential direction of the belt; a first camera for taking an image of the subject from front, the first camera taking an image of the marker on the walking surface from above to generate a first image; a second camera for taking an image of the subject from front, the second camera taking an image of the marker on the walking surface from above to generate a second image; and a correction processing unit that corrects an image generated by at least either the first camera or the second camera based on a position of an image area of the marker included in each of the first image and the second image.