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.

A method of and apparatus (10) for providing a personalized audio-visual instructional aid for assisting a person (11) to emulate preferred positions and/or movements in undertaking an activity by capturing position and/or movement data of the person (11) or an object associated with and controlled by the person undertaking the activity using position and/or movement sensing devices such as MEMS sensors (13) on the person (11) or object and using a computer (16) to analyze and compare the captured data with pre-stored data relating to preferred positions and/or movement in undertaking the activity and generating a visual presentation (19) based on the differences between the captured position and/or movement of the person or object and the preferred positions and/or movement and adding to the generated visual presentation, audio instructional comments relating to the differences.

An interactive exercise system includes a mechanical support system and a display module held by the mechanical support system. A mirror element is attached to at least partially cover the display module. At least one movable arm is connected to the support system and at least one force-controlled component is connected to the mechanical support system. The force-controlled component can be graspable by a user and allows for a range of exercise types and programs.

A computer program product, apparatus, and method may include various operations. The operations may include calibrating a sensor device to identify a heading, wherein the sensor device includes an inertial measurement unit, receiving three-dimensional motion data from the sensor device secured to a body part of a person as the person performs an activity involving movement of the body part, and causing generation of output based on the three-dimensional motion data, wherein the output is based on the movement of the sensor device.

Methods and systems for detecting configuration states of a golf club and adjustment systems of a golf club, such as a shaft connection system. A golf club configuration detection system captures configuration data from the adjustment system of the golf club by a configuration detection device, such as a camera, a barcode scanner, or an RFID scanner. The captured configuration data is compared to reference configuration data to determine a configuration state of the adjustment system. Swing data and ball-flight data are tracked for golf-ball strikes with the golf club in the detected configuration state. Recommendations for configuration states may be generated based on the tracked data.

A training, rehabilitation, and recovery system comprises an exercise apparatus including a user interface member coupled to a plurality of links and joints, brakes capable of resisting movement of at least a subset of the links or joints, and sensors capable of sensing movement at the joints or the user interface member. The system also includes a processor configured to receive from the sensors positional data of the links or joints over an initial movement of the apparatus by a user, from which positional coordinates of the user interface member are calculated and a reference trajectory is established. An end space is defined based on the reference trajectory. Over a subsequent movement of the apparatus by the user, the processor receives additional positional data and determines a completion of a repetition based on the positional coordinates of the subsequent movement and the defined end space.

A sports training device includes a glove with a main body. The main body has a palm portion and a back portion. The main body is configured to receive a hand of a user. A plurality of finger receptacles is disposed on the main body. Each finger receptacle of the plurality of finger receptacles has a proximal end disposed adjacent to the main body and a distal end disposed opposite the proximal end. The plurality of finger receptacles includes a thumb receptacle, a first finger receptacle, and a second finger receptacle. Each of the distal end of the thumb receptacle, the distal end of the first finger receptacle, and the distal end of the second finger receptacle are open. A sensor is disposed on the glove and is configured to monitor a release of an object to be thrown.

Systems and methods for enhancing fitness instruction, performance tracking, and motivation is described. A device collects information about an exercise movement and predicts movement of a subject in the third dimension. A fitness instructor performing exercise movements, and a user performing exercise movements, can be used to train a predictive model. A predictive model can suggest exercise feedback, measure performance of an exercise movement, and motivate a user to exercise. The power generated by a user can be measured. Cryptographic hashing and a distributed ledger network can be used to enhance exercise motivation and provide rewards for completing exercises movements. Rewards may be registered on a distributed ledger network and become the property of a user.

A gesture recognition device configured to detect a gesture performed by a user to actuate a motor of a closure mechanism of an article of footwear. The gesture recognition device may include a sensor unit with an accelerometer sensor, and an analysis unit in operative communication with the sensor unit. The analysis unit may be configured to execute a gesture confirmation algorithm to confirm or reject possible gesture event data received from the sensor unit as a true gesture event. If the gesture confirmation algorithm confirms the possible gesture event data as a true gesture event, the analysis unit may output a signal to actuate the motor.

Disclosed is a computer-implemented method for educating subjects on the performance of elemental movements or series of movements within a set of precision body movements or complex patterns of activity.