PERSONALIZED STROKE RECOGNITION ALGORITHM

• US 20160136502A1
• Filed: 11/17/2014
• Published: 05/19/2016
• Est. Priority Date: 11/21/2013
• Status: Abandoned Application

• Current Assignee: Caddieon Incorporated
• Original Assignee: Caddieon Incorporated
• Inventors: Voutilainen, Osmo
• Application Number:
  US14/542,682
• Publication Number:
  US 20160136502A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06F 2218/12 Classification; Matching
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G09B 19/0038 Sports
• CPC Class Codes: H04M 1/72412 using two-way short-range wireless interfaces

Abstract

In a method, system, detection apparatus and computer program for recognizing a collision of a golf club with a golf ball, the recognition procedure is configured to be executed partly in a motion sensor of a detection device, partly in the processor unit of the detection device and a mobile information processing device. The most complex processing and computation steps are executed in the processor unit of the mobile information processing device. Due to the shared computations the algorithm is accurate and configurable yet the power consumption of the detection device is low at the same time which provides for a long operating time. A method for improving recognition accuracy further may include a procedure of personalizing the configuration of the algorithm.

Claims

1. A method for improving accuracy of a golf stroke recognition algorithm comprising:

Recording sensor data stream from sample stroke taken by player;

Computing a personal parameter set for stroke recognition algorithm from said sensor data stream; and

Updating a default parameter set with said personal parameter set in the stroke recognition algorithm;

2. The method of claim 1 wherein the recorded sensor data is at least one of acceleration, velocity and orientation.

3. The method of claim 2 wherein the sensor is part of a detection device attached to player'"'"'s wrist or forearm or club.

4. The method of claim 1 wherein the player takes at least one sample stroke.

5. The method of claim 1 wherein the computed parameter set defines acceptance window for stroke timing or orientation of the player during stroke or both.

6. The method of claim 1 wherein the computing of personal parameter set comprises the steps of

Extracting data slice from recorded data stream around time of collision of a club with a ball;

Filtering said data slice and extracting target slice from filtered data slice;

Computing personal stroke target by normalizing said target slice with standard deviation and mean of target slice;

Computing auto-correlation of said personal stroke target with filtered and normalized data slice;

Defining time difference between auto-correlation peaks in resulting swing and hit correlation signals exceeding correlation thresholds;

Defining personal pass window for stroke timing around said time difference; and

Defining personal pass window for orientation around nominal orientation in the sample stroke.

7. The method of claim 6 wherein the personal stroke target comprises hit target and swing target or one of them.

8. The method of claim 7 wherein swing model may be further divided into sub-models for address, backswing, downswing and follow-through or their combination.

9. The method of claim 1 wherein the computing step is accomplished on detection device, on mobile device or on server or manually.

10. The method of claim 1 wherein the computing step is accomplished in specific training mode for stroke recognition algorithm optimization.

11. The method of claim 1 further comprising verifying the personal stroke recognition algorithm using said personal parameter set with a stroke or multiple strokes;

12. The method of claim 11 wherein the verification of the personal parameter set is deemed accepted if strokes are recognized accurately.

13. The method of claim 11 wherein the steps are accomplished using data streams recorded from strokes during round of golf.

14. The method of claim 13 wherein at least one of computing, updating and verifying steps is accomplished online during the round of golf or offline after the round of golf.

15. The method of claim 1 further comprising defining club specific parameter sets.