Abstract: Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

Abstract: An exercise machine comprises an actuator, a motor coupled to the actuator, and a motor controller coupled to the motor. The motor controller is configured to receive an indication of a characteristic of a workout on the actuator, wherein the workout comprises a next exercise movement, determine a suggested weight for the next exercise movement, based at least in part on a physiological analysis of the workout characteristic, and control torque of the motor for the next exercise movement, based at least in part on the suggested weight.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

Abstract: Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

Abstract: Performance information associated with a previous repetition of an exercise movement is received. Performance of one or more upcoming repetitions is predicted, based at least in part on the performance information associated with the previous repetition of the exercise movement. A failure classification of whether the one or more upcoming repetitions is associated with an occurrence of physical failure is performed, based at least in part on the predicted performance of the one or more upcoming repetitions of the exercise movement. A number of repetitions in reserve is determined, based at least in part on the failure classification.

Abstract: An exercise machine comprises an actuator, a motor coupled to the actuator, and a motor controller coupled to the motor. The motor controller is configured to receive an indication of a characteristic of a workout on the actuator, wherein the workout comprises a next exercise movement, determine a suggested weight for the next exercise movement, based at least in part on a physiological analysis of the workout characteristic, and control torque of the motor for the next exercise movement, based at least in part on the suggested weight.

Abstract: An exercise machine comprises an actuator, a motor coupled to the actuator, and a motor controller coupled to the motor. The motor controller is configured to receive an indication of a characteristic of a workout on the actuator, wherein the workout comprises a next exercise movement, determine a suggested weight for the next exercise movement, based at least in part on a physiological analysis of the workout characteristic, and control torque of the motor for the next exercise movement, based at least in part on the suggested weight.

Abstract: 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.

Abstract: 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.