In recent years, surface electromyogram signals have been increasingly used to operate wearable devices. These devices can aid to help workers or soldiers to lower the load in the task to boost efficiency. However, achieving effective signal prediction has always been a challenge. It is critical to use an appropriate signal preprocessing method and prediction algorithm when developing a controller that can accurately predict and control human movements in real time. For this purpose, this article investigates the effect of various surface electromyogram preprocessing methods and algorithms on prediction results. Walking data (surface electromyogram angle) were collected from 10 adults (5 males and 5 females). To investigate the effect of preprocessing methods on the experimental results, the raw surface electromyogram signals were grouped and subjected to different preprocessing (bandpass/principal component analysis/independent component analysis, respectively). The processed data were then imported into the random forest and support vector regression algorithm for training and prediction. Multiple scenarios were combined to compare the results. The independent component analysis-processed data had the best performance in terms of convergence time and prediction accuracy in the support vector regression algorithm. The prediction accuracy of knee motion with this scheme was 94.54% ± 2.98. Notably, the forecast time was halved in comparison to the other combinations. The independent component analysis algorithm’s “blind source separation” feature effectively separates the original surface electromyogram signal and reduces signal noise, hence increasing prediction efficiency. The main contribution of this work is that the method (independent component analysis + support vector regression) has the potency of best prediction of surface electromyogram signal for knee movement. This work is the first step toward myoelectric control of assisted exoskeleton robots through discrete decoding.

With the promotion of soft viscoelastic materials in the field of flexible bionic robots, the behavior of sliding friction between soft viscoelastic materials and rigid materials such as metals has attracted great attention. In this study, the research object was silicone, and a research method combining a dynamic force testing technique and a non-contact optical testing technique for deformation and strain was proposed to study the frictional behavior of silicone and the effect of the tooth structure surface on the sliding friction. we analyzed the temporal changes in the friction force for different loads, the dynamic characteristics of large deformation and micro-strain on the front side (define the contact surface as the bottom surface) of the silicone . The results showed that within a certain range, the sliding friction coefficient of the silicone varies linearly with the load, and there were stick-slip and deformation waves during the relative motion. The larger the load was, the faster the deformation wave propagated, and the tooth structure surface had sliding friction anisotropy.

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With the promotion of soft viscoelastic materials in the field of flexible bionic robots, the behavior of sliding friction between soft viscoelastic materials and rigid materials such as metals has attracted great attention. In this study, the research object was silicone, and a research method combining a dynamic force testing technique and a non-contact optical testing technique for deformation and strain was proposed to study the frictional behavior of silicone and the effect of the tooth structure surface on the sliding friction. we analyzed the temporal changes in the friction force for different loads, the dynamic characteristics of large deformation and micro-strain on the front side (define the contact surface as the bottom surface) of the silicone. The results showed that within a certain range, the sliding friction coefficient of the silicone varies linearly with the load, and there were stick-slip and deformation waves during the relative motion. The larger the load was, the faster the deformation wave propagated, and the tooth structure surface had sliding friction anisotropy.