Lower limb motion recognition based on surface electromyography signals and its experimental verification on a novel multi-posture lower limb rehabilitation robots☆

•Different eigenvectors are tested respectively. Combined with different kinds of BP neural network and ELM classifiers, two motion decoders are constructed for lying and sitting postures, respectively.•The experimental platform of this study is a novel multi-posture lower limb rehabilitation robot, which has three postures: lying, reclining and sitting.•A simple and effective connection method based on label number retrieval is designed, and two different motion decoders based on lying and sitting are constructed.•Through real-time experiments, the effectiveness of active control based on sEMG signals is verified.