Effect of upper-limb positions on motion pattern recognition using electromyography

Previous studies of electromyographic (EMG) pattern recognition for neural prosthesis control mainly focused on the estimation of offline classification accuracy. Factors that may affect the performance in operating prosthesis in practice were rarely considered. In the preliminary study we investigated effects of the variation of limb positions on classification performance. Eight channels of myoelectric signals and a LDA classifier were used to identify seven classes of forearm movements in five transradial amputees. Our pilot results showed that the classification error of inter-position was about 4 times more than that of single position across the five transradial amputees (p<0.02), indicating the significant effect of limb position variation on classification performance. We further attempted to reduce the limb position effect by training the classifier with EMG from multiple EMG recording positions, and the classification performance was compared with that of single position training. It was found multiple positions training method could decrease the classification error as more positions involved, which reached its minimum value 11.25% when all five positions were included.