**5. Conclusions**

In this study, a new method of combining RF and PCA was utilized to establish an estimation model from sEMG to the knee. Better than BPPCA, the RFPCA method is able to predict the knee angle at a low root mean square error about 5◦ and the execution time is several times smaller, and these results indicate that RFPCA is suitable for knee movement estimation with high accuracy and requiring minimal time. Both the sample size and the input dimension of the RFPCA were investigated. RFPCA is insensitive to the estimation accuracy when the sample size increases to a certain value, but requires more time. Also, as the previous sEMG increases, the accuracy of the RFPCA first increases and then decreases, and the work would be beneficial to the estimation model construction using ML methods. Moreover, the results of the RFPCA are more stable in comparison to the BPPCA in terms of sample size and previous sEMG input, and RFPCA is also robust in terms of differences among test subjects. All in all, the estimation of RFPCA performs well in the estimation from sEMG to knee movement in this work, which is conductive to motion analysis and exoskeleton control.

Future work of improving the estimation accuracy will be done by extracting more signal features or fusing other physical sensors, as well as testing more people and other daily activities such as different speeds of walking, and ascending or descending stairs. Furthermore, a wider application for RFPCA may be utilized for estimation using different sEMG data from various people to find a general relationship between sEMG and joint motion. Finally, the RFPCA method will be applied to estimate human motion for exoskeleton control.

**Author Contributions:** Conceptualization, Z.L., X.G. and C.X.; methodology, Z.L. and X.G.; software, Z.L.; validation, Z.L., X.G. and C.X.; formal analysis, Z.L. and X.G.; investigation, Z.L. and K.Z.; resources, Z.L. and K.Z.; data curation, Z.L., X.G. and C.X.; writing—original draft preparation, all the authors; writing—review and editing, all the authors; visualization, Z.L. and X.G.; supervision, X.G. and C.X.; project administration, X.G.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by NATIONAL DEFENSE BASIC SCIENTIFIC RESEARCH PROGRAM OF CHINA, gran<sup>t</sup> number B1020132012.

**Acknowledgments:** Thanks to the help of the participants in the experiment and the support of Laboratory for Individual Equipment Technology at School of Mechanical Engineering, Nanjing University of Science and Technology. We are also grateful for the assistance of the editors and reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.
