Sensor-based athletic activity measurements

• US 9,446,287 B2
• Filed: 07/01/2011
• Issued: 09/20/2016
• Est. Priority Date: 07/01/2011
• Status: Active Grant

• Current Assignee: Nike, Inc.
• Original Assignee: Nike, Inc.
• Inventors: Weast, Aaron B., Mullin, James M.
• Primary Examiner(s): Do, An
• Application Number:
  US13/175,216
• Publication Number:
  US 20130006583A1
• Time in Patent Office: 1,908 Days
• Field of Search: 702/127, 702/160, 702/189
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 5/1036 Measuring load distribution, e.g. podologic studies
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6807 Footwear
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A63B 2024/0065 Evaluating the fitness, e.g. fitness level or fitness index
• CPC Class Codes: A63B 2220/20 Distances or displacements
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2230/75 calorie expenditure
• 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

Abstract

Determining pace or speed based on sensor data may include determining an amount of contact time a user'"'"'s foot has with a workout surface such as the ground. Contact time may be determined by identifying samples in the sensor data that correspond to various events such as a heelstrike, a toe-off and a subsequent heelstrike. In one example, these events may be identified by determining a sequence of three sample values (e.g., a triplet) that exceeds corresponding thresholds. The validity of an identified triplet (e.g., heelstrike, toe-off and heelstrike) may be confirmed by determining whether a difference between a last event sample and a middle event sample is greater than a difference between the middle event sample and an initial event sample. Once confirmed, a contact time may be determined from the triplet. A linear or non-linear relationship may then be applied to the contact time to determine a speed or pace.

Claims

1. A method comprising:

receiving a signal generated by a foot-based sensor during athletic activity performed by a user, wherein the signal comprises a plurality of samples;

processing the signal based on a rolling standard deviation;

identifying, from the rolling standard deviation processed signal, a sequence of three samples in the signal that corresponds to a physical foot-step taken by the user, wherein the sequence of three samples includes a first heelstrike sample, a toe-off sample and a second heelstrike sample;

determining a pace of the user based on an elapsed time between at least two of the first heelstrike, toe-off and second heelstrike samples; and

responsive to having received a user request to determine a pace during the athletic activity, providing the determined pace to a user display device.

2. The method of claim 1, further comprising:

determining a first difference between a value of the second heelstrike sample and a value of the toe-off sample;

determining a second difference between a value of the toe-off sample and the value of the first heelstrike sample;

determining whether the first difference is greater than the second difference; and

in response to determining that the first difference is greater than the second difference, adding the sequence of three samples to a list.

3. The method of claim 2, further comprising:

adding a plurality of three sample sequences to the list; and

determining a plurality of contact times of the user based on the plurality of three sample sequences in the list.

4. The method of claim 3, further comprising:

determining a mean contact time of the plurality of contact times;

determining a standard deviation between the plurality of contact times; and

filtering out one or more contact times of the plurality of contact times that exceed a threshold number of standard deviations from the mean contact time.

5. The method of claim 1, wherein determining the pace of the user based on an elapsed time between at least two of the first heelstrike, toe-off and second heelstrike samples includes:

determining a contact time by determining a difference between the toe-off sample and the first heelstrike sample;

selecting a predefined linear relationship from a plurality of linear relationships, wherein each of the plurality of linear relationships corresponds to a different type of athletic activity; and

applying the predefined linear relationship to the contact time to calculate the pace of the user.

6. The method of claim 1, wherein identifying the sequence of three samples in the signal that corresponds to a physical foot-step taken by the user includes:

selecting a first sample from the plurality of samples;

determining whether a value of the first sample is at or above a first threshold sample value;

in response to determining that the value of the first sample is not at or above the first threshold sample value, selecting a subsequent sample in the plurality of samples; and

in response to determining that the value of the first sample is at or above the first threshold sample value;

determining a maximum sample value in a first sample window of a predefined size, wherein a lower bound of the first sample window corresponds to the first sample, and

identifying a sample corresponding to the maximum sample value as the first heelstrike sample.

7. The method of claim 6, wherein in response to determining that the value of the first sample is at or above the first threshold sample value, the method further comprises:

determining a maximum sample value in a second sample window; and

determining whether the maximum sample value of the second sample window is at or above a second threshold sample value; and

in response to determining that the maximum sample value of the second sample window is at or above the second threshold sample value, identifying a sample corresponding to the maximum sample value of the second sample window as the toe-off sample.

8. The method of claim 1, wherein the rolling standard deviation includes a plurality of standard deviations corresponding to the plurality of samples.

9. The method of claim 1, wherein the rolling standard deviation is calculated using an equation as follows:

10. An apparatus comprising:

a foot-based sensor;

at least one processor; and

one or more non-transitory computer readable media coupled to the at least one processor and storing computer readable instructions that, when executed, cause the apparatus to;

receive a signal generated by the foot-based sensor during athletic activity performed by a user, wherein the signal comprises a plurality of samples;

process the signal based on a rolling standard deviation;

identify, from the rolling standard deviation processed signal, a sequence of three samples in the signal that corresponds to a physical foot-step taken by the user, wherein the sequence of three samples includes a first heelstrike sample, a toe-off sample and a second heelstrike sample;

determine a pace of the user based on an elapsed time between at least two of the first heelstrike, toe-off and second heelstrike samples; and

responsive having received a user request to determine a pace during the athletic activity, provide the determined pace to a user display device.

11. The apparatus of claim 10, wherein the one or more non-transitory computer readable media contains further computer readable instructions that, when executed, cause the apparatus to:

determine a first difference between a value of the second heelstrike sample and a value of the toe-off sample;

determine a second difference between a value of the toe-off sample and the value of the first heelstrike sample;

determine whether the first difference is greater than the second difference; and

in response to determining that the first difference is greater than the second difference, adding the sequence of three samples to a list.

12. The apparatus of claim 11, wherein the one or more non-transitory computer readable media contains further computer readable instructions that, when executed, cause the apparatus to:

add a plurality of three sample sequences to the list; and

determine a plurality of contact times of the user based on the plurality of three sample sequences in the list.

13. The apparatus of claim 12, wherein the one or more non-transitory computer readable media contains further computer readable instructions that, when executed, cause the apparatus to:

determine a mean contact time of the plurality of contact times;

determine a standard deviation between the plurality of contact times; and

filter out one or more contact times of the plurality of contact times that exceed a threshold number of standard deviations from the mean contact time.

14. One or more non-transitory computer readable media storing computer readable instructions that, when executed, cause an apparatus to:

receive a signal generated by a foot-based sensor during athletic activity performed by a user, wherein the signal comprises a plurality of samples;

process the signal based on a rolling standard deviation;

identify, from the rolling standard deviation processed signal, a sequence of three samples in the signal that corresponds to a physical foot-step taken by the user, wherein the sequence of three samples includes a first heelstrike sample, a toe-off sample and a second heelstrike sample; and

determine a pace of the user based on an elapsed time between at least two of the first heelstrike, toe-off and second heelstrike samples and

responsive to having received a user request to determine a pace during the athletic activity, provide the determined pace to a user display device.

15. The one or more non-transitory computer readable media of claim 14, wherein determining the pace of the user based on an elapsed time between at least two of the first heelstrike, toe-off and second-heelstrike samples includes:

determining a contact time by determining a difference between the toe-off sample and the first heelstrike sample;

selecting a predefined linear relationship from a plurality of linear relationships, wherein each of the plurality of linear relationships corresponds to a different type of athletic activity; and

applying the predefined linear relationship to the contact time to calculate the pace of the user.

16. The one or more non-transitory computer readable media of claim 14, wherein identifying the sequence of three samples in the signal that corresponds a physical foot-step taken by the user includes:

selecting a first sample from the plurality of samples;

determining whether a value of the first sample is at or above a first threshold sample value;

in response to determining that the value of the first sample is not at or above the first threshold sample value, selecting a subsequent sample in the plurality of samples; and

in response to determining that the value of the first sample is at or above the first threshold sample value;

determining a maximum sample value in a first sample window of a predefined size, wherein a lower bound of the first sample window corresponds to the first sample, and

identifying a sample corresponding to the maximum sample value as the first heelstrike sample.

17. The one or more non-transitory computer readable media of claim 14, further comprising computer readable instructions that, when executed, cause the apparatus to:

determine a first difference between a value of the second heelstrike sample and a value of the toe-off sample;

determine a second difference between a value of the toe-off sample and the value of the first heelstrike sample;

determine whether the first difference is greater than the second difference;

in response to determining that the first difference is greater than the second difference, add the sequence of three samples to a list;

add a plurality of three sample sequences to the list; and

determine a plurality of contact times of the user based on the plurality of three sample sequences in the list.