Matching system for correlating accelerometer data to known movements

• US 9,833,173 B2
• Filed: 03/18/2013
• Issued: 12/05/2017
• Est. Priority Date: 04/19/2012
• Status: Expired due to Fees

• Current Assignee: Abraham Carter, David Scott, Maxwell Mann
• Original Assignee: Abraham Carter, David Scott, Maxwell Mann
• Inventors: Carter, Abraham, Scott, David, Mann, Maxwell
• Primary Examiner(s): Le, Toan
• Assistant Examiner(s): SUN, XIUQIN
• Application Number:
  US13/846,662
• Publication Number:
  US 20130282324A1
• Time in Patent Office: 1,723 Days
• Field of Search: 702141
• 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 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

The present invention extends to methods, systems, and computer program products for providing a matching system for correlating accelerometer data to known movements. Data representing known movements can be obtained and stored in a database such as by processing and storing accelerometer data obtained from one or more accelerometers worn by a user while performing a particular movement. The accelerometer data obtained from a particular movement can be processed to generate a feature set descriptive of the accelerations associated with a particular movement or series of movements.

Claims

1. A system implemented by a portable computing device for creating a custom entry in a database that represents a custom movement performed by a user wearing one or more accelerometers, the system comprising:

wearable devices incorporating accelerometers;

a portable computing device for receiving wireless input from the wearable devices, said input being formatted into a request that a custom entry be created in a database of the portable computing device that stores a plurality of entries, each custom entry representing one or more feature sets of accelerometer data that are generated when a particular movement is performed;

the portable computing device receiving accelerometer data from one or more accelerometers worn by a user while performing a custom movement;

using a processor of the portable computing device to generate one or more feature sets by;

extracting a chunk of the accelerometer data corresponding to a plurality of axes;

splitting the chunk into a plurality of time series, each time series including the accelerometer data from a particular axis;

for each time series, extracting a magnitude of various frequencies in the data of each time series;

for each time series, summing groups of magnitudes into bins; and

for each time series, aggregating bins to form feature sets; and

storing the custom entry in the database, the custom entry including the feature sets and an identifier of the custom movement;

receiving additional accelerometer data at the portable computing device from the one or more accelerometers worn by the user while performing the custom movement;

accessing the database with the processor to determine that the additional accelerometer data received from the one or more accelerometers includes the one or more feature sets of the custom entry;

displaying on the portable computer device an indication that the custom movement has been performed by the user;

for each of a plurality of feature sets in the database including the feature set of the custom entry, using the processor to determine the inverse Euclidean metric of the feature set of the unknown movement and the feature set; and

using the processor to determine which feature set of the plurality of feature sets in the database yields the greatest inverse Euclidean metric.

2. The system of claim 1, wherein displaying the indication comprises incrementing a count of the number of times the user has performed the custom movement.

3. The system of claim 1, wherein the custom entry is stored on a first device, the method further comprising:

transmitting the custom entry to a second device to permit the second device to identify when a second user performs the custom movement.

4. The system of claim 1, wherein the chunk comprises accelerometer data received over a particular interval of time.

5. The system of claim 1, wherein the time series includes accelerometer data from multiple accelerometers.

6. The method of system 1, wherein the chunk is split by the processor into three time series corresponding to each of the x-axis, y-axis, and z-axis and wherein each of the three time series is used by the processor to form the feature sets.

7. A system implemented by a portable computing device for creating a custom entry in a database that represents a custom movement performed by a user wearing one or more accelerometers, the system comprising:

wearable devices incorporating accelerometers;

a portable computing device for receiving input from a user requesting that a custom entry be created;

the portable computing device receiving accelerometer data at the portable computing device from one or more accelerometers worn by the user while performing a custom movement;

extracting a chunk of the accelerometer data with a processor of the portable computing device;

splitting the chunk into a time series using the processor;

extracting a magnitude of at least one frequency in the time series using the processor;

summing groups of magnitudes into bins using the processor;

aggregating bins with the processor to form feature sets;

storing the feature sets as the custom entry that corresponds to the custom movement performed by the user in the database;

receiving additional accelerometer data at the portable computing device from the one or more accelerometers worn by the user while performing the custom movement;

accessing the database with the processor to determine that the additional accelerometer data received from the one or more accelerometers includes the one or more feature sets of the custom entry, comprising;

for each of a plurality of feature sets in the database including the feature set of the custom entry, using the processor to determine the inverse Euclidean metric of the feature set of the unknown movement and the feature set; and

using the processor to determine which feature set of the plurality of feature sets in the database yields the greatest inverse Euclidean metric; and

displaying an indication that the custom movement associated with the greatest inverse Euclidian metric has been performed by the user.

8. The system of claim 7, wherein the one or more accelerometers comprises a first accelerometer worn around the user'"'"'s wrist and a second accelerometer worn around the user'"'"'s ankle.

9. The system of claim 7, wherein the database is located on a portable computing device.

10. The system of claim 7, wherein receiving input from a user requesting that a custom entry be created comprises receiving an identifier of the custom movement from the user.

11. The system of claim 7, wherein receiving input from a user requesting that a custom entry be created comprises receiving an input from the user indicating a start of the custom movement and an input from the user indicating a stop of the custom movement.

12. The system of claim 7, wherein receiving input from a user requesting that a custom entry be created comprises receiving an input from the user indicating a time duration over which the accelerometer data will be received.

13. A method implemented by a portable computing device for creating a custom entry in a database that represents a custom movement performed by a user wearing one or more accelerometers, the method comprising:

receiving input from a user at the portable computing device requesting that a custom entry be created;

receiving accelerometer data at the portable computing device from one or more accelerometers worn by the user while performing a custom movement;

extracting a chunk of the accelerometer data received over a particular time interval using a processor of the portable computing device;

splitting the chunk into three time series corresponding to each of an x-axis, a y-axis, and a z-axis using the processor;

extracting a magnitude of various frequencies in the data of each time series using the processor;

for each time series, summing groups of magnitudes into bins;

for each time series, aggregating bins to form feature sets;

storing the feature sets as the custom entry that corresponds to the custom movement performed by the user in a database;

generating an additional feature set corresponding to the custom movement performed at a different speed;

receiving additional accelerometer data from the one or more accelerometers worn by the user while performing the custom movement;

accessing the database to determine that the additional accelerometer data received from the one or more accelerometers includes the one or more feature sets corresponding to the custom movement and to determine the speed at which the custom movement was performed; and

displaying an indication that the custom movement has been performed by the user and the speed at which the custom movement was performed;

wherein determining that the additional accelerometer data received from the one or more accelerometers includes the one or more feature sets of the custom movement comprises;

for each of a plurality of feature sets in the database including the feature set of the custom entry, determining the inverse Euclidean metric of the feature set of the unknown movement and the feature set; and

determining which feature set of the plurality of feature sets in the database yields the greatest inverse Euclidean metric.

14. The method of claim 13, wherein the custom entry further comprises a speed factor.

15. The method of claim 13, wherein the inverse Euclidean metric comprises a frequency-specific weighting factor.