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.