Sensing applications for exercise machines

• US 7,914,420 B2
• Filed: 07/18/2008
• Issued: 03/29/2011
• Est. Priority Date: 07/18/2007
• Status: Active Grant

• Current Assignee: Life Fitness LLC
• Original Assignee: Brunswick Corporation
• Inventors: DeYoung, Glenn Allen, Cate, Casparus, Daly, Juliette C.
• Primary Examiner(s): RICHMAN, GLENN E
• Application Number:
  US12/176,068
• Publication Number:
  US 20090023556A1
• Time in Patent Office: 984 Days
• Field of Search: 482 1- 9, 482/51, 482/54, 482900-902, 119/700
• US Class Current: 482/9
• CPC Class Codes: A63B 2024/0012 Comparing movements or motion sequences with a registered reference
• CPC Class Codes: A63B 2024/0065 Evaluating the fitness, e.g. fitness level or fitness index
• CPC Class Codes: A63B 2024/0078 Exercise efforts programmed as a function of time
• CPC Class Codes: A63B 2071/065 Visualisation of specific exercise parameters
• CPC Class Codes: A63B 22/0228 with variable resilience
• CPC Class Codes: A63B 22/0235 driven by a motor
• CPC Class Codes: A63B 22/025 electrically, e.g. D.C. motors with variable speed control
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/22 Stride length
• CPC Class Codes: A63B 2220/34 Angular speed
• CPC Class Codes: A63B 2220/53 Force of an impact, e.g. blow or punch
• CPC Class Codes: A63B 2220/833 Sensors arranged on the exercise apparatus or sports implement
• CPC Class Codes: A63B 2230/06 heartbeat rate only
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
• CPC Class Codes: Y10S 482/901 having computer circuitry

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.

Claims

1. A method of profiling an exercise session of an exercise machine, comprising:

selecting at least one workout parameter;

operating the exercise machine in compliance with the at least one workout parameter;

reading output signal values from a sensor, wherein the output signals are generated by a user impact to the exercise machine during the exercise session;

processing the output signals, wherein processing the output signals includes determining peak or trough values of the output signals and detecting whether a first output signal comprises a first new peak or trough value and detecting whether a second output signal comprises a second new peak or trough value;

determining workout matrices to profile the exercise session using the processed output signals; and

providing feedback information based on the workout matrices.

2. A method as described in claim 1, wherein operating the exercise machine comprises adjusting the speed of a drive member operatively coupled to the exercise machine, actuating an incline adjustor to adjust the incline angle of the exercise machine, or actuating a stiffness adjustor to adjust the stiffness of the exercise machine.

3. A method as described in claim 1, wherein the exercise machine is a treadmill and wherein determining a first workout matrix comprises determining a cadence of a user and a second workout matrix comprises a stride length of a user.

4. A method as described in claim 1, further comprising causing an arbitrator that receives candidate signals from biopotential sensors operatively coupled to the exercise machine to ignore the output signals of the sensor and determining a heart rate value experienced by a user by comparing the candidate signals of the biopotential sensors and the output signals of the sensor.

5. A method as described in claim 1, wherein the exercise machine is a treadmill and further comprising using the output signals generated by the sensor to determine whether the user is running or walking.

6. A method as described in claim 1, wherein the exercise machine is a treadmill and further comprising using the output signals generated by the sensor to determine whether to choose a walking metabolic cost equation or a running metabolic cost equation to determine the caloric expenditure of the user during the exercise session.

7. A method as described in claim 1, wherein the exercise machine is a treadmill and further comprising using the processed output signals to determine a proper deck stiffness value based on the physical characteristics of a user, the workout parameters received by the user, or the deck deflection measured by the sensor.

8. A method as described in claim 1, wherein the exercise machine is a treadmill and further comprising activating the treadmill from a standby status when the output signal generated by sensor is greater than an inactivity threshold value for a predetermined period of time.

9. A method of claim 1, wherein the output signals are generated in response to consecutive footfalls of a user impacting a deck of the treadmill during the exercise session.

10. A method of claim 9, wherein the output signals are generated in response to a deflection in the deck of a treadmill caused by the footfalls impacting the deck.

11. A method of claim 9, wherein processing the output signals comprises determining a time interval between consecutive footfalls.

12. A method of claim 1, wherein processing the output signals comprises determining a time interval between the peak or trough values of the output signals.

13. A method of claim 1, wherein processing the output signals comprises determining a peak or trough magnitude of the output signals.

14. A method of claim 1, further comprising processing the peak or trough values of the output signals to profile the matrices of a user during the exercise session.

15. A method of claim 1, wherein the sensors comprise piezoelectric sensors.

16. A method of profiling an exercise session of an exercise machine, comprising:

operating the exercise machine in compliance with at least one workout parameter selected by a user or at least one physical characteristic to be provided by the user;

reading output signal values generated by sensors disposed along a deck of the exercise machine, wherein the output signals are proportional to the magnitude of the forces imparted on the exercise machine by the user during the exercise session; and

processing the output signals to determine workout matrices to profile the exercise session using the processed output signals, wherein processing the output signal comprises determining peak or trough values of the output signals and detecting whether a first output signal comprises a first new peak or trough value and detecting whether a second output signal comprises a second new peak or trough value.

17. A method of claim 16, further comprising providing feedback information based on the workout matrices.

18. A method of profiling an exercise session of a treadmill, the method comprising:

operatively coupling at least one biopotential sensor to the treadmill to determine a heart rate value experienced by a user during the exercise session and operatively coupling at least one deflection sensor to the treadmill to profile the exercise session;

reading output signals from the deflection sensor that are generated by the user impacting the exercise machine during the exercise session; and

causing an arbitrator that receives candidate signals from the biopotential sensor to ignore the output signals of the deflection sensor and determining a heart rate value experienced by the user by comparing the candidate signals of the biopotential sensor and the output signals of the deflection sensor.

19. A method of claim 18, further comprising processing the output signals to determine workout matrices, wherein a first workout matrix comprises determining a cadence of the user and a second workout matrix comprises a stride length of the user.