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• [P1037] A Novel and Robust Algorithm to Model Handwriting Skill for Haptic Applications
  Author(s): Govindarajan Srimathveeravalli, Xinyan Li, Puneet Singla, Thenkurussi Kesavadas
  
  

  Abstract
  A necessary step to building effective writing skill training system requires developing good methods for modeling human skill adequately. A number of groups have represented character information in various languages for writing skill based on trajectory information. These methods are often data intensive, tedious to implement and do not encode force information. To overcome these restrictions a novel present a novel modeling methodology that has good accuracy, robustness, flexibility and encodes force information involved in writing characters. This modeling methodology, based on Global-Local Approximation technique, has the capability to provide temporal force or position information independent of time, decoupling velocity information of the sample data used to capture skilled tasks. This modeling approach can be extended many human skilled tasks such as surgery, art and sports

  
   

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