An information related to the position of an actuator coupled to a cable which is coupled to a motor is received. A filter is used to provide an input to a motor controller coupled to the motor, to adjust torque on the motor such that a strength curve is implemented relative to the position of the actuator.

A method of generating a calibration factor is described. In an example, the method may include establishing a wireless communication connection with a fitness machine. The method may also include obtaining first machine data from the fitness machine. The first machine data may specify a type of the fitness machine. The method may also include initiating a workout session on the wearable computer based on the first machine data. During the workout session, the method may also include obtaining sensor data, obtaining second machine data, and determining the calibration factor based on the second machine data and the sensor data. The method may also include generating a workout session summary based on the calibration factor.

A jump counting method for jump rope is provided. The jump counting method comprises: S1, obtaining an original video data of a jump rope movement, and extracting an audio data and an image data from the original video data; S2, calculating the number of jumps of the rope jumper according to an audio information and an image information extracted from the audio data and the image data; and S3, outputting and displaying the calculation result.

Systems and methods for adjusting resistance on an exercise cycle having a frame and a flywheel include calibration, homing and auto-follow routines. A resistance apparatus comprising an actuator is configured to selectively position the resistance apparatus relative to the flywheel, wherein a distance between the resistance apparatus to the flywheel corresponds to resistance applied to the flywheel. Control components are configured to control operation of the resistance system in response to instructions, and a computing device is configured to output media for an exercise class to a user, the exercise class comprising one or more target resistance ranges corresponding to a segment of the exercise class. The computing device is further configured to selectively implement auto-follow logic configured to determine a target resistance value for a current segment of the exercise class and instruct the control components to adjust the resistance system to the target resistance value.

The device comprises at least one accelerometer, and a controller receiving input signals from the at least one accelerometer. The controller configured to filter stroke characteristics from the input signal using a filter module. The controller then applies a first statistical module on the filtered signal and obtains a first output signal. Due to the first statistical module, the first output signal is obtained, which is agnostic to type of swim stroke employed by the swimmer. The controller then determines the swim metric based on the first output signal and an adaptive threshold value. The swim metric is lap completion or lap count or turn event, during swimming by a swimmer. The device consumes less power and also agnostic to swim styles and turn styles employed by swimmers.

A head guard is provided. The head guard includes one or more sensors as part of an sensory input and communications system. The head guard wirelessly communicates data to remote computing devices for intelligent data collection.

Assisted racking of digital resistance includes detecting a state of a cable. A motor is mechanically coupled to the cable to provide resistance during an exercise by tensioning the cable. It further includes determining completion of the exercise based at least in part on the detected state of the cable. It further includes selectively removing resistance from the cable based at least in part on the determination that the user has completed the exercise.

Controlling an exercise machine includes receiving a stream of measurements of extension of a component of an exercise machine. It further includes characterizing the stream of measurements including detecting at least one extremum having at least one extremum parameter. It further includes matching the extremum parameter with a previously determined signature associated with a user. It further includes changing an output of the exercise machine based at least in part on the match.

Controlling an exercise machine includes receiving a stream of measurements of extension of a component of an exercise machine. It further includes detecting a first phase of a repeated motion. It further includes detecting a transition to a second phase of the repeated motion. A time constraint is applied to the detection of the transition to the second phase of the repeated motion. It further includes controlling a resistance associated with the second phase of the repeated motion.

Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, an alignment plan capable of enabling, during a treatment session, the aligning of a user's body. The method comprises generating machine learning models trained to identify alignment plans. The method also comprises receiving treatment data that comprises a first position of the user's body, aspects of a treatment plan, and one or more attributes of the user. The method further comprises generating the alignment plan by using the machine learning models. The generating is based on at least the aspects of the treatment plan and at least one of the one or more attributes of the user. The alignment plan may comprise a target position of the user's body and one or more elements for adjusting the user's body from the first position to the target position. The method also comprises transmitting the plan to a computing device.