SENSING APPLICATIONS FOR EXERCISE MACHINES

• US 20090023556A1
• Filed: 07/18/2008
• Published: 01/22/2009
• Est. Priority Date: 07/18/2007
• Status: Active Grant

• Current Assignee: Life Fitness LLC
• Original Assignee: Brunswick Corporation
• Inventors: Daly, Juliette C., DeYoung, Glenn Allen, Cate, Casparus
• Granted Patent: US 7,914,420 B2
• Time in Patent Office: Days
• 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 a workout session of an exercise machine, comprising:

selecting at least one workout parameter or inputting at least one physical characteristic of a user;

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

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;

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 matrix comprises a stride length of a user.

4. A method as described in claim 3, 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 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, 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 determining a cadence value of a user exercising on a treadmill, the method comprising:

receiving a plurality of output signal from a sensor generated in response to consecutive footfalls of a user impacting a deck of the treadmill during an exercise session;

processing the plurality of output signals from the sensor to determine a magnitude value of a peak or a trough value of the output signal;

detecting whether a first output signal has a first new peak or trough value and detecting whether a second output signal has a second new peak or trough value;

determining a time interval between the first new peak or trough value detected and the second new peak or trough value detected; and

calculating the cadence value of a user by counting the time intervals between consecutive new peak or trough values generated by the output signals for each footstrike impacting the deck of the treadmill during the exercise session.

10. A method as described in claim 9, further comprising providing a cadence training program to the user, the method comprising:

receiving a target cadence value;

comparing the calculated cadence with the target cadence value;

in response to the calculated cadence being less than the target cadence value, prompting the user to incrementally increase the speed value of the treadmill or prompting a user to shorten a stride length; and

in response to the calculated cadence being greater than the target cadence value, prompting the user to incrementally decrease the speed value of the treadmill or prompting a user to lengthen a stride length if the calculated cadence is greater than the target cadence.

11. A method as described in claim 10, wherein receiving the target cadence value comprises receiving the target cadence value from the user interface or a data structure having recommended cadence values based on the user'"'"'s physical characteristics and workout parameters selected via the user interface.

12. A method as described in claim 10, further comprising directing a device controller to cause a speed adjustor to automatically increase or decrease the speed value of a drive member driving the belt.

13. A method as described in claim 9, further comprising determining the stride length of a user exercising on a treadmill, the method comprising:

receiving a speed value of a belt moving over a deck of the treadmill from a user interface or a speed sensor;

calculating the stride length of a user by multiplying the time interval between the first and second peak or trough values of the first and second output signals by the speed value of the belt.

14. A method as defined in claim 13, further comprising comparing the calculated stride length with a nominal stride length, wherein the nominal stride length is retrieved from a data structure having look-up tables that define average stride lengths based on the user'"'"'s physical characteristics and workout parameters selected via a user interface.

15. A method as described in claim 13, further comprising adjusting a metabolic cost equation when the calculated cadence value is either less than or greater than the target cadence value.

16-58. -58. (canceled)

59. A system for profiling an exercise session of an exercise machine, comprising:

a user interface to enable a user to input physical characteristics or workout parameters;

a plurality of sensors operatively coupled to the exercise machine that generate output signals in response to a user impacting the exercise machine during the exercise session, wherein the sensors produce output signals that are proportional to the magnitude of the forces imparted on the exercise machine by a user during the exercise session, and wherein the control system determines the peak or trough values of the output signal; and

a control system to process the output signals to determine the magnitude of the peak or trough values of the output signals, wherein the control system is to process the peak or trough values of the output signals to profile matrices of the user during the exercise session.

60. A system as described in claim 59 wherein the sensors comprise piezoelectric sensors.

61. A system as described in claim 59, wherein the matrices of the user comprise a cadence of the user, a stride length of a user, a gait of the user, or a caloric expenditure of the user.

62. A system as described in claim 61, wherein the exercise machine is a treadmill and the cadence of the user comprises counting footstrikes impacting a deck of the treadmill within a period of time or counting the time interval between consecutive footstrikes impacting the deck, wherein the output signals are generated in response to a deflection in the deck caused by the footstrikes impacting the deck, wherein the footstrikes are represented by the peak or trough values of the output signals.

63. A system as described in claim 59, wherein the user'"'"'s physical characteristics include, height, weight, age, gender, or inseam.

64. A system as described in claim 59, wherein the workout parameters comprise a belt speed, a deck incline angle, a deck stiffness value, a target cadence, a target stride length, or a target heart rate.

65. A system as described in claim 59, wherein the exercise machine is a treadmill and wherein the output signals are processed by the control system to determine whether the user is running or walking.

66. A system as described in claim 59, wherein the exercise machine is a treadmill and wherein the output signals are processed by the control system to select between a running metabolic cost equation or a walking metabolic cost equation when determining a caloric expenditure of the user during the exercise session.

67. A system as described in claim 59, wherein the exercise machine is a treadmill and further comprising a cadence coach, wherein the cadence coach compares a calculated cadence value determined by the output signals with a target cadence selected by the user via the user interface or retrieved from look-up tables based on the input physical characteristics and workout parameters received by the user interface.

68. A system as described in claim 59, wherein the exercise machine is a treadmill and further comprising a deck stiffness adjustor to adjust the stiffness of the deck if the deck stiffness value received from the input interface is not between lower and upper threshold deck stiffness values retrieved from a data structure having a look-up table that are based on the input physical characteristics and workout parameters received from the user interface.

69. A system as described in claim 59, wherein the exercise machine is a treadmill and further comprising activating the treadmill from standby status when the sensor generates an signal output greater than an inactivity threshold value for a predetermined period of time.