Classification of Multiple Finger Motions During Dynamic Upper Limb Movements

To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.