An intelligent system that automatically adjusting optimal rehabilitation intensity or exercise volume with personalized exercise prescription, comprising: upload the physiological information data measured by registered members to a cloud data integration server through the Internet; an expert diagnostic unit, which can download and use the member's physiological information data from the cloud data integration server. After the diagnosis, a personalized exercise prescription is issued and uploaded to the cloud data integration server; a rehabilitation fitness equipment unit can download the exercise prescription from the cloud data integration server to control and automatically adjust the optimal rehabilitation intensity or exercise volume; after being diagnosed by experts such as doctors, rehabilitators or fitness coaches, the optimal parameter values of a continuously updated artificial intelligence exercise prescription can be used to improve the efficacy of personal rehabilitation or exercise fitness.

The present disclosure provides a system that quantitatively tracks an individual's diet and exercise using smart devices (phones, watches, and other wearables). Unlike existing programs, which work in energy units (calories), the present system works in mass units (grams) and satisfies the fundamental physics law of conservation of mass. Food ingested is tracked as well as exercise in order to place the user on a quantitative, custom diet that safely and effectively results in weight loss. In addition to rigorously treating the problem of weight loss by addressing the physics that underlies diet and exercise, the system empirically learns about the user over time such that performance may be optimized.

A method provides for optimizing at least one exercise. The method includes receiving first image data. The first image data includes first pixel data associated with the user performing the exercise at a first time. The method includes receiving second image data. The second image data includes second pixel data associated with the user performing the exercise at a second time. The method includes determining, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user. The method includes generating outline data based on the deviation data and corresponding to a frontal area of the user. The method may also include determining drag coefficient data based on the frontal area of the user. The method includes determining, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.

A method for optimizing at least one exercise is provided. The method includes receiving user data. The user data includes biometric attribute data associated with a user of an exercise device. The method includes generating at least one interval, the at least one interval including at least duration data and target power data each associated with the exercise. The method includes during the at least one interval, receiving measurement data associated with at least one of the user and the exercise device. The method includes calculating, based on the measurement data and the user data, energy data associated with an expended energy of the user during the at least one interval, the energy data including quantity data and source information each associated with at least one energy source. The method includes generating, via an artificial intelligence engine, a machine learning model trained to identify the at least one energy source.

A method provides for optimizing at least one exercise. The method includes receiving first image data. The first image data includes first pixel data associated with the user performing the exercise at a first time. The method includes receiving second image data. The second image data includes second pixel data associated with the user performing the exercise at a second time. The method includes determining, based on a difference between the first pixel data and the second pixel data, deviation data associated with a profile of the user. The method includes generating outline data based on the deviation data and corresponding to a frontal area of the user. The method may also include determining drag coefficient data based on the frontal area of the user. The method includes determining, based on the outline data and the drag coefficient data, drag force data associated with the user using the exercise device.

The present disclosure provides a system that quantitatively tracks an individual's diet and exercise using smart devices (phones, watches, and other wearables). Unlike existing programs, which work in energy units (calories), the present system works in mass units (grams) and satisfies the fundamental physics law of conservation of mass. Food ingested is tracked as well as exercise in order to place the user on a quantitative, custom diet that safely and effectively results in weight loss. In addition to rigorously treating the problem of weight loss by addressing the physics that underlies diet and exercise, the system empirically learns about the user over time such that performance may be optimized.

The present disclosure provides a system that quantitatively tracks an individual's diet and exercise using smart devices (phones, watches, and other wearables). Unlike existing programs, which work in energy units (calories), the present system works in mass units (grams) and satisfies the fundamental physics law of conservation of mass. Food ingested is tracked as well as exercise in order to place the user on a quantitative, custom diet that safely and effectively results in weight loss. In addition to rigorously treating the problem of weight loss by addressing the physics that underlies diet and exercise, the system empirically learns about the user over time such that performance may be optimized.

A head mounted virtual reality object synchronized physical training system includes a virtual reality device, a physical training equipment, and a physiological signal sensor. The virtual reality device includes a display operable to display a virtual reality object according to an operation of the user. The virtual reality device is detachably mounted to a head of the user and provides the user with a function of interaction. The physical training equipment includes an exercise data sensor operable to detect exercise data that the user operates the physical training equipment. The physiological signal sensor is operable to detect a human body physiological signal and a human body movement signal of the user. The physical training equipment and the physiological signal sensor have a function of synchronization with the virtual reality object displayed on the virtual reality device.

An information processing apparatus includes a setting unit and a providing unit. The setting unit sets an exercise intensity. The providing unit provides exercise information used by a user to achieve the exercise intensity.

The present disclosure provides a system that quantitatively tracks an individual's diet and exercise using smart devices (phones, watches, and other wearables). Unlike existing programs, which work in energy units (calories), the present system works in mass units (grams) and satisfies the fundamental physics law of conservation of mass. Food ingested is tracked as well as exercise in order to place the user on a quantitative, custom diet that safely and effectively results in weight loss. In addition to rigorously treating the problem of weight loss by addressing the physics that underlies diet and exercise, the system empirically learns about the user over time such that performance may be optimized.