Upper Limb Motion Information Acquisition and Gesture Recognition Based on Acceleration Sensor

In order to evaluate the effective of rehabilitation training for stroke patients, based on acceleration sensor, a new method of identifying the gesture of arm was proposed in this paper. Information collection, signal transportation, denoising and gesture recognition were concluded in our system. The information of upper limb was collected by using acceleration sensor. Wavelet transform was applied to smooth the signal in order to reduce the affection of noise. Then support vector machine (SVM) was used to distinguish seven movements by selecting an appropriate kernel function. Finally the effect of rehabilitation training was evaluated. Experimental result shows that by combining zero-crossing points, four points on the site, four points locus and time domain feature, bend elbow and lateral raise can be separated from other gestures. Compared with BP neural network, the SVM can achieve a good result. An accuracy of 90% was reached by using the new feature and RBF kernel in our method.