Abstract: Methods for profiling exercise sessions are described. An example method of determining cadence of a user disclosed herein includes receiving output signals from a sensor generated in response to consecutive footfalls of the user impacting a deck of a treadmill during an exercise session and processing the output signals from the sensor to determine respective magnitude values of a peak or a trough value of each of the output signals. The method includes detecting whether a first output signal has a first peak or trough value and detecting whether a second output signal has a second peak or trough value, determining a time interval between the first peak or trough value detected and the second peak or trough value detected, and calculating a cadence value of the user based on the time intervals.

Abstract: Methods for profiling exercise sessions are described. An example method of determining cadence of a user disclosed herein includes receiving output signals from a sensor generated in response to consecutive footfalls of the user impacting a deck of a treadmill during an exercise session and processing the output signals from the sensor to determine respective magnitude values of a peak or a trough value of each of the output signals. The method includes detecting whether a first output signal has a first peak or trough value and detecting whether a second output signal has a second peak or trough value, determining a time interval between the first peak or trough value detected and the second peak or trough value detected, and calculating a cadence value of the user based on the time intervals.

Abstract: Methods for profiling exercise sessions are described. An example method for profiling an exercise session includes receiving physical characteristics or workout parameters via an input interface, generating output signals proportional to a magnitude of vertical forces imparted to the exercise machine by a user during the exercise session, and processing the output signals via a control system to determine the magnitude of peak or trough values of the output signals to profile the exercise session.

Abstract: Methods for profiling exercise sessions are described. An example method for profiling an exercise session includes receiving physical characteristics or workout parameters via an input interface, generating output signals proportional to a magnitude of vertical forces imparted to the exercise machine by a user during the exercise session, and processing the output signals via a control system to determine the magnitude of peak or trough values of the output signals to profile the exercise session.

Abstract: Sensing applications for exercise machines are described. An example sensing application for profiling a workout session of an exercise machine comprises a user interface to input physical characteristics or workout parameters. A sensor is operatively coupled to the exercise machine to generate an output signal in proportion to a magnitude of a force imparted on the exercise machine in response to an impact to the exercise machine. A control system processes the output signal to determine a magnitude of a peak or trough value of the output signal, where the control system is to process the peak or trough value of the output signal to profile the exercise session.

Abstract: Sensing applications for exercise machines are described. An example sensing application for profiling a workout session of an exercise machine comprises a user interface to input physical characteristics or workout parameters. A sensor is operatively coupled to the exercise machine to generate an output signal in proportion to a magnitude of a force imparted on the exercise machine in response to an impact to the exercise machine. A control system processes the output signal to determine a magnitude of a peak or trough value of the output signal, where the control system is to process the peak or trough value of the output signal to profile the exercise session.

Abstract: Sensing applications for exercise machines are described. An example sensing application for profiling a workout session of an exercise machine comprises selecting at least one workout parameter or inputting at least one physical characteristic of a user and operating the exercise machine in compliance with the at least one workout parameter selected. The method further comprising reading output signal values from a sensor in which the output signals are generated by a user impact to the exercise machine during the exercise session and processing the output signals. The method further comprising determining workout matrices to profile the exercise session using the processed output signals and providing feedback information based on the workout matrices.

Abstract: Sensing applications for exercise machines are described. An example sensing application for profiling a workout session of an exercise machine comprises selecting at least one workout parameter or inputting at least one physical characteristic of a user and operating the exercise machine in compliance with the at least one workout parameter selected. The method further comprising reading output signal values from a sensor in which the output signals are generated by a user impact to the exercise machine during the exercise session and processing the output signals. The method further comprising determining workout matrices to profile the exercise session using the processed output signals and providing feedback information based on the workout matrices.