Human Joint Movement Recognition by Using Ultrasound Echo Based on Test Feature Classifier

We propose a new method for simultaneous joint torque and angle recognition in dynamic motion by using ultrasound echo. Test Feature Classifier has been employed as a classifier. Ultrasound is emitted from the surface of skin and it is reflected inside of living body. Feature values for the recognition are extracted from the reflected wave. The feature value changes as the movement of the muscle relating to the target joint motion. Therefore the joint movement can be recognized based on the feature value. We carried out a experiment of the recognition of the movement of human elbow joint. The task was to classify the test data set into its correct torque class and angle class based on the feature values. The feature value was the rectified integration value on each 6 blocks cut out from the ultrasound echoes. The rate of correctly classified data was 0.74 in the result of the torque recognition. That of in the angle recognition was 0.82. Some data were classified into incorrect classes, however, these data were classified into classes near the correct class. This results shows that the recognition by the proposed method were successful.