The disclosed physical exercise apparatus (2) comprises a frame (2′) equipped with a crankset and a saddle (26), the saddle itself comprising a chassis (262) fastened to the frame, two saddle parts (266) and articulation members (267, 268) for articulating each saddle part relative to the frame around a pitch axis (A26), about a roll axis (B26) and about a yaw axis (L26). This apparatus (2) also comprises sensors (30, 50) for detecting a pitch movement (T), a roll movement (R), and a yaw movement (L) 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 (40) is configured to determine, from output signals (S.sub.30, S.sub.50) of the sensors, angular amplitudes (α, β, γ) of the pitch (T), roll (R), and yaw (L) movements. At least one screen (29) is provided in order to display, depending on the angular amplitudes determined by the calculation unit (40), the position on each saddle part of a bearing point (P.sub.G, P.sub.D) of an ischium of a user in the process of pedaling.

A method for monitoring a person performing a physical exercise based on a sequence of image frames showing an exercise activity of the person. The method includes extracting, based on the sequence of image frames, for each image frame a set of body key points using a neural network, the set of body key points being indicative of a posture of the person in the image frame; deriving, based on a subset of the body key points in each image frame, at least one characteristic parameter indicating a progression of a movement of the person; detecting a start loop condition by evaluating the time progression of the at least one characteristic parameter, said start loop condition indicating a transition from a start posture of the person to the movement of the person when performing the physical exercise, wherein a loop of exercising encompasses one single repetition of the physical exercise; detecting an end loop condition by evaluating the time progression of at least one of the characteristic parameters, said end loop condition indicating a transition from the movement of the person when performing the physical exercise to an intermediate posture, wherein, as a result, the start of the loop and the end of the loop are determined; and deriving the time period for a single loop of the physical exercise based on the start of the loop and the end of the loop and evaluating the time period.

The present disclosure provides a sit-up motion information management system and detection method based on an Internet of things. The method includes: acquiring detection data of a sensor at present time; determining whether the detection data of the sensor at the present time meet a first counting condition, determining that a sit-up action is correct if yes, and incrementing a flag value by one; acquiring detection data of the sensor at next time; determining whether the detection data of the sensor at the next time meet a second counting condition, determining that a sit-up action is correct if yes, and incrementing a flag value by one; determining whether a flag value is greater than or equal to a preset value to obtain a third determination result; and determining, if the third determination result indicates yes, that one rep of sit-up actions is completed, and recording a number of sit-up reps.

An apparatus for natural torso and limbs tracking and feedback for electronic interaction with fall safety support. The apparatus comprises a body harness worn on the body of a user, a support structure designed to bear the weight of the user in the event of a stumble, trip, or fall, and a plurality of tethers attached at one end to the harness and at the other end to the support structure. One or more sensors are integrated into the system to measure aspects of the user's movement and used as input to control a computer system. In the event of stumble, trip, or fall, all of, or a portion of, the user's body weight is borne by the tethers as a safety mechanism to prevent injury. The system is designed to be used with virtual reality systems wherein the user's vision is blocked or obscured by a virtual reality visor.

Disclosed herein are an apparatus and method for evaluating a human motion using a mobile robot. The method may include identifying the exercise motion of a user by analyzing an image of the entire body of the user captured using a camera installed in the mobile robot, evaluating the pose of the user by comparing the standard pose of the identified exercise motion with images of the entire body of the user captured by the camera of the mobile robot from two or more target locations, and comprehensively evaluating the exercise motion of the user based on the pose evaluation information of the user from each of the two or more target locations.

A system and method for using a body joystick to interact with a virtual or mixed-reality machines or software applications that uses a support arm to hold a device for attaching the body joystick to a user's body, a sensor for detecting movement, and a tether and actuator for providing feedback to the user about interactions with the virtual or mixed-reality machines or software applications.

Described herein is a method for fine-tuning a putter and manufacture thereof. More specifically, an application (App) that is utilised for static and dynamic data point analysis along with algorithms to best determine how attributes of a putter need to be set up to maximise consistency of a user's putter stroke and roll of the ball. A customised putter is tailored and manufactured for each individual based on the output data of the App and a fitting system of the putter is multi-adjustable therein.

Disclosed are techniques for leveraging machine learning to generate posture adjustment values for specific body postures of a player to improve loop drive techniques, such as in table tennis. Video clips of a player hitting a ball with a loop drive technique are analyzed to determine values for specific body postures and qualities of the ball after being hit. A machine learning model is generated to analyze relationships between body posture values and ball qualities. Upon receiving a video clip of a live session of a player hitting a ball using a loop drive technique, the machine learning model is used to generate adjustment values for body postures of the player to impart improved loop drive qualities to the ball, such as faster topspin.

An electronic device is provided. The electronic device includes at least one sensor, a communication circuitry, and at least one processor operatively connected with the at least one sensor and the communication circuitry, wherein the at least one processor may be configured to, if exercise information related to an exercise to be performed is received from a server through the communication circuitry, identify a movement for each part of a body to be performed based on the exercise information, transmit an activation command to at least one of a plurality of connected wearable devices through the communication circuitry based on the identified movement, transmit, to the server, at least one of a sensing value detected through the at least one sensor or a sensing value received from the at least one wearable device, and provide feedback received from the server.

A system for providing haptic feedback to a user, the system comprising a first piezoelectric transducer array comprising multiple piezoelectric transducers, respective ones of which being configured to generate a second signal at a second frequency, and at least one of which being configured to generate a first signal at a first frequency, and a second piezoelectric transducer array comprising multiple piezoelectric transducers, respective ones of which configured to receive the second signal, and at least one of which configured to generate a third signal to initiate generation of the first signal at the said at least one piezoelectric transducer of the first piezoelectric transducer array, the first frequency suitable for providing a haptic feedback signal for a user.