Inertial sensor based recognition of 3-D character gestures with an ensemble classifiers

We present a 3-D input medium based on inertial sensors for on-line character recognition and an ensemble classification scheme for the recognition task. The system allows user to write a character in the air as a gesture, with a sensor-embedded device held in hand. The kinds of sensors used are 3-axis accelerometer and 3-axis gyroscope generating acceleration and angular velocity signals respectively. For character recognition, we used the technique of FDA (Fisher discriminant analysis). We tried different combinations of sensor signals to test the recognition performance. It is also possible to estimate a 2-D handwriting trajectory from the sensor signals. The best recognition rate of 93.23%, in case we use only raw sensor signals, was attained when all 6 sensor signals were combined. The recognition rate of 92.22% was reached if the estimated trajectory was used as input. Finally we tested the ensemble method and the generalization rate of 95.04% was attained on the ensemble recognizer consisting of 3 FDA recognizers based on acceleration-only, angular-velocity-only and handwriting trajectory respectively.

[1]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[2]  D. B. Cox,et al.  Integration of GPS with Inertial Navigation Systems , 1978 .

[3]  Jing Yang,et al.  A miniaturized attitude estimation system for a gesture-based input device with fuzzy logic approach , 2003 .

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Kyoung-Ho Kang,et al.  Self-contained spatial input device for wearable computers , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[6]  Davi Geiger,et al.  An on-line handwriting recognition system using Fisher segmental matching and Hypotheses Propagation Network , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[7]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Hugh F. Durrant-Whyte,et al.  The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications , 2001, IEEE Trans. Robotics Autom..

[9]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..