  Traditional myoelectric pattern recognition (MPR) systems excel within
controlled laboratory environments but they are interfered when confronted with
anomaly or novel motions not encountered during the training phase. Utilizing
metric ways to distinguish the target and novel motions based on extractors
compared to training set is a prevalent idea to alleviate such interference. An
innovative method for anomaly motion detection was proposed based on simplified
log-Euclidean distance (SLED) of symmetric positive definite manifolds. The
SLED enhances the discrimination between target and novel motions. Moreover, it
generates a more flexible shaping of motion boundaries to segregate target and
novel motions, therefore effectively detecting the novel ones. The proposed
method was evaluated using surface-electromyographic (sEMG) armband data
recorded while performing 6 target and 8 novel hand motions. Based on linear
discriminate analysis (LDA) and convolution prototype network (CPN) feature
extractors, the proposed method achieved accuracies of 89.7% and 93.9% in novel
motion detection respectively, while maintaining a target motion classification
accuracy of 90%, outperforming the existing ones with statistical significance
(p<0.05). This study provided a valuable solution for improving the robustness
of MPR systems against anomaly motion interference.
