Provided are an electronic device, a method for pushing information, and related products. The method includes the following. A first set of data on multiple substances in a first position of a body of a user is obtained by scanning the first position with a substance detecting sensor. A body part corresponding to the first position is determined with a camera. Fitness plan is determined according to the first set of data and the body part and then pushed via a display.

A system for planning a fitness exercise course includes a corresponding module establishing a corresponding relationship between a training target and each of a plurality of fitness exercise items, wherein the training target includes a specific muscle, a muscle group and a whole body muscle; a sensing module sensing a physiological state of a body builder; and a determining module, according to the corresponding relationship, in response to the physiological state, selecting one of the plurality of fitness exercise items and a combination of fitness exercise items to complete the training target.

A system includes wearable devices positioned on a subject in different locations. Each wearable device includes motion sensors that measure the subject's movement in three dimensions. The motion sensors generate raw sensory data as the subject performs a physical movement. A data filter is selected based on a condition of the subject and a designated movement corresponding to the physical movement, and used to convert the raw sensory data into formatted data. A level of compliance of the physical movement with a movement model for the designated movement is determined by applying comparative modeling techniques to the formatted data and the movement model. Real-time feedback is delivered dynamically to the subject by the wearable devices during the performance of the physical movement based on the level of compliance. The movement model can be generated, and the comparative modeling techniques can be selected, based on the condition of the subject.

A physical exercise apparatus includes a frame equipped with a crankset and a saddle. The saddle includes a chassis fastened to the frame, two saddle parts, and articulation members for articulating each saddle part relative to the frame around a pitch axis, a roll axis, and a yaw axis. The apparatus includes sensors for detecting a pitch movement, a roll movement, and a yaw movement of each saddle part respectively about the pitch, roll, and yaw axes, these movements resulting from pedaling made by a user. At least one calculation unit determines, from output signals of the sensors, angular amplitudes of the pitch, roll, and yaw movements. At least one screen is provided in order to display, depending on the angular amplitudes determined by the calculation unit, the position on each saddle part of a bearing point of an ischium of a user in the process of pedaling.

A pelvic floor muscle training device and system include a pelvic floor muscle training device, an intelligent terminal, a cloud server, a VR device, and a few application programs. The user does Kegel exercise wearing the training device, with the help of the VR device and scene induction, synergistic or independently, thus enhancing pelvic floor muscle function.

To provide an information processing apparatus, an information processing method, and a program that can detect a practice swing on the basis of sensor data from a motion sensor. An information processing apparatus includes a control section executing processing for detecting, on the basis of sensor data detected by a motion sensor provided on an object, that a practice swing has been performed with the object. The object includes a grip portion. Assuming that an X axis is a straight line parallel to an axis of the grip portion and that a Y axis runs straight to the X axis, the sensor data includes an angular velocity of the object in a Y axis direction. The control section detects that the practice swing has been performed in a case where the angular velocity of the object in the Y axis direction changes from positive to negative.

An exercise assisting device includes at least one processor. The processor obtains motion data on a current exercise form of a user performing a certain repetitive exercise motion including a certain motion state. Based on the obtained motion data, the processor determines a point of time at which the motion state occurs in one cycle of the repetitive exercise motion. Based on the obtained motion data and the point of time, the processor obtains the current exercise form in the motion state. The processor obtains a difference between the obtained current exercise form and a target exercise form. The processor causes an actuator to apply a stimulus to a body of the user based on the difference. The actuator is attached to a part of the body of the user constituting the exercise form. The stimulus suggests correction of the exercise form and indicates a direction to move the part.

A processing device receives three dimensional (3D) motion capture data corresponding to a subject user performing a physical activity and receives attribute data associated with the subject user. The processing device determines a personalized reference data set for the subject user based on 3D motion capture data associated with a group of users performing the physical activity, wherein each user from the group of users shares at least a portion of the first attribute data with the subject user. The processing device provides the personalized reference data set as an input to a trained machine learning model and obtains an output of the trained machine learning model, wherein the output comprises a recommendation for the subject user pertaining to improvement of the physical activity.

A method includes providing a first video file to a plurality of exercise machines, the first video file including content associated with an exercise class. The method also includes receiving user data from the plurality of exercise machines, the user data including respective settings associated with a common performance metric. In such a method, the respective settings are used on the plurality of exercise machines during playback of a particular part of the first video file. The method also includes identifying a timestamp associated with the particular part of the first video file, and generating an executable control corresponding to the performance metric. The method further includes generating a second video file comprising the content and the executable control. In such methods, playback of the second video file causes display of the executable control at a part of the second video file corresponding to the timestamp.

An athletic training system includes an athlete data repository, an adaptive algorithm, and at least one training unit. The athlete data repository includes biometric and performance data of athletes. The adaptive algorithm has access to the athlete data repository and is trained on biometric and performance data to generate athlete-specific training programs for use with the training unit. Each athlete-specific training program is adapted to biometric and performance data of an individual athlete. The training unit includes electronically adjustable athletic resistance equipment and a controller. The controller is configured to receive at least one athlete-specific training program of the plurality of athlete-specific training programs and to adjust resistance levels of the adjustable resistance equipment in response to the received athlete-specific training program. The controller is also configured to record performance data of an athlete's use of the training unit in accordance with the received athlete-specific training program.