Personalized stroke recognition algorithm

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.

What is claimed is: 
     
         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.