A Temporally Smoothed MLP Regression Scheme for Continuous Knee/Ankle Angles Estimation by Using Multi-Channel sEMG

With coexisting-cooperative-cognitive robots gradually appearing in daily life, an instinct and efficient human-robot interaction (HRI) is becoming more and more challenging and necessary. Surface electromyography (sEMG) signals, as one of mainstream manners of the interactions, are employed to predict human intentions. In this paper, to provide natural assistance for standing up and sitting down, sEMG signals acquired from active muscles of one's lower limb are utilized to predict continuous movements. A temporally smoothed multilayer perceptron (MLP) regression scheme is proposed for continuous knee/ankle angles estimation by multi-channel sEMG signals. After correlation analyses of sEMG signals and movements, a series of linear and nonlinear regression models are trained to decode human intentions from pre-processed sEMG. Furthermore, to remove out local fluctuations of direct mappings, temporally smoothed techniques are further implemented as post-processings. In the experiments of standing up and sitting down, extensive results of ten healthy subjects show that a three-layer MLP with the Savitzky-Golay filter achieves the best performance on the mean squared error (MSE, testing: 59.58) and the $R^{2}$ score ($R^{2}$ , testing: 0.948). The proposed regression scheme is compared with other methods and is also verified by measurements of a high-precision visual motion capture system.