METHOD, DEVICE AND SYSTEM FOR FITNESS TRACKING

• US 20160313174A1
• Filed: 04/26/2016
• Published: 10/27/2016
• Est. Priority Date: 04/26/2015
• Status: Active Grant

• Current Assignee: Samuel Lightstone
• Original Assignee: Samuel Lightstone
• Inventors: Lightstone, Samuel
• Granted Patent: US 9,677,928 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A43B 17/006 multilayered
• CPC Class Codes: A43B 3/34 with electrical or electronic arrangements
• CPC Class Codes: A43D 999/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: A63B 2220/51 Force
• CPC Class Codes: A63B 2220/56 Pressure
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/01 User's weight
• 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
• CPC Class Codes: G01G 19/50 having additional measuring devices, e.g. for height

Abstract

A system, device and method for fitness tracking include one or more insoles, the one or more insoles having embedded therein a plurality of sensors; a computer having a processor and memory; and one or more code sets stored in the memory and executing by the processor, which, when executed, configure the processor to: receive, as inputs, data collected from the plurality of sensors embedded in the one or more insoles; filter the data; execute an evolutionary algorithm on the data, wherein the evolutionary algorithm converges the data from the plurality of sensors; and determine, based on the converged data, a total weight applied to the one or more insoles.

Claims

1. A method of fitness tracking, performed on a computer having a processor, a memory, and one or more code sets stored in the memory and executing by the processor, the method comprising:

receiving, as inputs, data collected from a plurality of sensors embedded in one or more insoles;

filtering the data;

executing an evolutionary algorithm on the data, wherein the evolutionary algorithm converges the data from the plurality of sensors; and

determining, based on the converged data, a total weight applied to the one or more insoles.

2. The method as in claim 1, further comprising:

generating a test data set based on one or more samples of the data from a first set of the plurality of sensors, wherein the first set of the plurality of sensors are embedded in a first insole of the one or more insoles;

associating a known weight quantity with a second set of the plurality of sensors;

wherein the second set of the plurality of sensors are embedded in a second insole of the one or more insoles;

applying a fitness function to the test data and the known weight quantity; and

determining, based on the fitness function, whether or not the genetic algorithm has optimally converged, such that a predicted weight can be applied to a new data sample outside the test data set.

3. The method as in claim 2, wherein the first insole and the second insole constitute a pair of insoles adapted to be worn by a single user.

4. The method as in claim 1, wherein the filtering comprises at least one of:

low-pass filtering, Gaussian filtering, moving average filtering, smoothing, rejection of outlier data points, interpolation, frequency, domain transformation, and dimension reduction.

5. The method as in claim 1, wherein the evolutionary algorithm is one of:

a genetic algorithm, a particle swarm optimization algorithm, and a differential evolution algorithm.

6. The method as in claim 1, wherein the plurality of sensors comprise at least one of force sensors and pressure sensors.

7. The method as in claim 1, wherein the plurality of sensors are embedded in one or more regions of the one or more insoles, and wherein each region of the one or more insoles in adapted to contact a region of a foot of a user.

8. The method as in claim 1, wherein the data collected from the plurality of sensors embedded in the one or more insoles is transmitted wirelessly to the processor.

9. A system for fitness tracking, comprising:

one or more insoles, the one or more insoles having embedded therein a plurality of sensors;

a computer having a processor and memory; and

one or more code sets stored in the memory and executing by the processor, which, when executed, configure the processor to;

receive, as inputs, data collected from the plurality of sensors embedded in the one or more insoles;

filter the data;

execute an evolutionary algorithm on the data, wherein the evolutionary algorithm converges the data from the plurality of sensors; and

determine, based on the converged data, a total weight applied to the one or more insoles.

10. The system as in claim 9, wherein the one or more code sets further configure the processor to:

generate a test data set based on one or more samples of the data from a first set of the plurality of sensors, wherein the first set of the plurality of sensors are embedded in a first insole of the one or more insoles;

associate a known weight quantity to a second set of the plurality of sensors;

wherein the second set of the plurality of sensors are embedded in a second insole of the one or more insoles;

apply a fitness function to the test data and the known weight quantity; and

determine, based on the fitness function, whether or not the genetic algorithm has optimally converged, such that a predicted weight can be applied to a new data sample outside the test data set.

11. The system as in claim 10, wherein the first insole and the second insole constitute a pair of insoles adapted to be worn by a single user.

12. The system as in claim 9, wherein the filtering comprises at least one of:

low-pass filtering, Gaussian filtering, moving average filtering, smoothing, rejection of outlier data points, interpolation, frequency, domain transformation, and dimension reduction.

13. The system as in claim 9, wherein the evolutionary algorithm is one of:

a genetic algorithm, a particle swarm optimization algorithm, and a differential evolution algorithm.

14. The system as in claim 9, wherein the plurality of sensors comprises at least one of force sensors and pressure sensors.

15. The system as in claim 9, wherein the plurality of sensors is embedded in one or more regions of the one or more insoles, and wherein each region of the one or more insoles in adapted to contact a region of a foot of a user.

16. The system as in claim 9, wherein the data collected from the plurality of sensors embedded in the one or more insoles is transmitted wirelessly to the processor.

17. A device for fitness tracking, comprising:

one or more insoles, the one or more insoles having embedded therein a plurality of sensors and wireless data transmitter, the one or more insoles configured to collect data and wireless transmit the data to a computer having a processor, memory, and one or more code sets stored in the memory and executing by the processor, which, when executed, configure the processor to;

receive, as inputs, the data collected from the plurality of sensors embedded in the one or more insoles;

filter the data;

execute an evolutionary algorithm on the data, wherein the evolutionary algorithm converges the data from the plurality of sensors; and

determine, based on the converged data, a total weight applied to the one or more insoles.

18. The device as in claim 17, wherein the one or more code sets further configure the processor to:

generate a test data set based on one or more samples of the data from a first set of the plurality of sensors, wherein the first set of the plurality of sensors are embedded in a first insole of the one or more insoles;

associate a known weight quantity to a second set of the plurality of sensors;

wherein the second set of the plurality of sensors are embedded in a second insole of the one or more insoles;

apply a fitness function to the test data and the known weight quantity; and

determine, based on the fitness function, whether or not the genetic algorithm has optimally converged, such that a predicted weight can be applied to a new data sample outside the test data set.

19. The device as in claim 17, wherein the first insole and the second insole constitute a pair of insoles adapted to be worn by a single user.

20. The device as in claim 17, wherein the evolutionary algorithm is one of:

a genetic algorithm, a particle swarm optimization algorithm, and a differential evolution algorithm.