A two-stage classification strategy to reduce the effect of wrist orientation in surface myoelectric pattern recognition

The myoelectric Pattern Recognition (PR) collects surface Electromyographic (sEMG) signals using the electrodes placed on the upper limb of the amputee. Then it recognizes patterns in those signals based on the intended limb movement using signal processing and machine learning techniques. The performance of the PR system should be robust against multiple factors, like wrist orientation, muscle force level changes, limb position changes, and electrode shifts. This paper demonstrates how performance is affected by wrist orientation and proposes a method to overcome those effects. A two-stage classification technique with Dynamic Time Warping (DTW) as the classifier, along with features extracted from a three-axis accelerometer and six-channel sEMG sensors, is proposed here. Accelerometer features are used to identify the wrist orientation, and sEMG features are used to classify the various limb movements performed by ten subjects. The performance of the proposed method was measured by classification error and classification accuracy of limb movements. The corresponding results were compared with the state-of-the-art techniques.