Continuous Estimation of Upper Limb Joint Angle Based on Stacked Denoising Autoencoder

In the human-robot interaction system of the rehabilitation robot for stroke rehabilitation, surface electromyography (sEMG) signal-based continuous joint angle estimation has essential significance and implementation value. However, the existing intra-subject mode is time-consuming and lacks generality, while the adoption of the new inter-subject mode tests the model’s generalization ability; at the same time, the often-adopted multi-feature fusion strategy makes the feature dimensionality too high and increases the computational pressure of the system. In this regard, firstly, four time-domain features of multi-channel sEMG are extracted as the initial features; then, a stacked denoising autoencoder (SDAE) network is constructed to encode the initial set of sEMG features in low dimensions and extract more robust low-dimensional features; finally, an LSTM network is introduced as the regression network between sEMG features and joint angles. The results indicate that the feature extraction method proposed is superior to other methods and can be used for the control of the rehabilitation robot with a stable and accurate continuous joint angle estimation during motion.