Qualification of arm gestures using hidden Markov models

We propose the use of hidden Markov models (HMMs) to qualify arm gestures. A HMM is trained based on the reference or correct gesture. Then, samples of the gesture that we want to score are used to train a second HMM. Both HMMs are compared, and a measure of their similarity is used to qualify the gesture. We used 3 different metrics to compare HMMs: Levinson, Kullback-Leibler and Porikli. For this, a visual system was developed to track a person's arm, which serves as input to the models that qualify the gestures. We applied this method to qualify the arm movements of stroke patients under rehabilitation. We analyzed three therapeutic gestures: flexion, circular and abduction. A HMM is trained to represent the movement of a healthy person for each gesture, which is compared with the HMMs obtained for each patient. The results are compared with the scales that are used in therapy. From the analysis of several experiments, the Porikli metric was the best to qualify the three gestures, in terms of the motricity index.

[1]  Philip Chan,et al.  Toward accurate dynamic time warping in linear time and space , 2007, Intell. Data Anal..

[2]  W. Rymer,et al.  Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. , 2014, Journal of rehabilitation research and development.

[3]  Markus Falkhausen,et al.  Calculation of distance measures between hidden Markov models , 1995, EUROSPEECH.

[4]  F. Porikli.  Trajectory Distance Metric Using Hidden Markov Model Based Representation , 2004 .

[5]  Gary R. Bradski,et al.  Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[6]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[7]  D. Reinkensmeyer,et al.  Java Therapy : Web-Based Robotic Rehabilit ation , 2001 .

[8]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[9]  Luis Enrique Sucar,et al.  Visual Recognition of Similar Gestures , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[10]  L. R. Rabiner,et al.  A probabilistic distance measure for hidden Markov models , 1985, AT&T Technical Journal.

[11]  Lawrence R. Rabiner,et al.  A tutorial on Hidden Markov Models , 1986 .

[12]  P. Stratford,et al.  Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke. , 1993, Physical therapy.

[13]  Jiangwen Deng,et al.  An HMM-based approach for gesture segmentation and recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[14]  Stan Salvador,et al.  FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space , 2004 .

[15]  D.J. Reinkensmeyer,et al.  Web-based telerehabilitation for the upper extremity after stroke , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..