## 6 Conclusions  
In this paper, we propose a novel variant network CNN-BLSF for forecasting and compensating physiological tremors. At first, to simplify the calculation of model parameters, the structure of the original BLS is used as our rebuild basis. Then, the SAE of the original BLS is replaced by the CNN to extract the time series feature efficiently. Moreover, to better obtain in-depth feature information from the multi-feature layers, the structure of lateral connection is adopted. Moreover, the incremental learning algorithm is adopted to update the network in real-time, which can improve the accuracy by adding new nodes. Finally, compared with the different existing methods, our proposed filter is more efficient in forecasting tremors, and we believe our filter can be applied to the medical area requiring higher accuracy.  
The work improves the correlation of series by using the lateral connection, which seems to be somewhat complex. In view of this, and the superiorities of long short term memory (LSTM) in solving sequence correlation problems, in future study, we will try to achieve the combination of LSTM with BLS.  
Conceptualization, G.L.; methodology, W.Y.; software, W.L.; validation, G.L. and Y.Z.; formal analysis, Y.Z.; writing--original draft preparation, W.Y.; writing--review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Tertiary Education Scientific research project of Guangzhou Municipal Education Bureau [No. 202235364], and the Science and Technology Program of Guangzhou, China [No. 201804010098].  
**Institutional Review Board Statement:** Not applicable.  
Figure 13: The trajectory with tremor and the effect of tremor attenuation. (**a**) The signal with small amplitude and high\(-\)low frequency tremors. (**b**) Tremor attenuation performance of different algorithms in the case of tremors with small amplitude and high\(-\)low frequency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods and Evaluations**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Test**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Total Time**} & \multirow{2}{*}{**R2**} \\  & **SSE** & **RMSE** & & & & \\ \hline BLS & 0.0184 & 0.0027 & 0.0184 & 0.0192 & 0.05 & 84.6\% \\ BLS-IL & 0.0161 & 0.0025 & 0.0191 & 0.0196 & 0.054 & 83.9\% \\ CNN-BLS & 0.0016 & 0.0008 & 0.0012 & 0.005 & 4.02 & 98.9\% \\ CNN-BLS-IL(Our) & 0.00073 & 0.00054 & 0.0005 & 0.0031 & 4.03 & 99.6\% \\ SVM & 0.0302 & 0.0246 & 0.0302 & 0.0246 & 0.1 & 76.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compare among evaluation results with different methods: SSE; RMSE; R2; Time.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the Tertiary Education Scientific research project of Guangzhou Municipal Education Bureau (No. 202235364), the Special projects in key fields of colleges and universities in Guangdong Province (No. 2021 ZDZX1109), the Science and Technology Program of Guangzhou, China (No. 201804010098), the Natural Science Foundation of Guangdong Province (No. 2019A1515012109), and the National Natural Science Foundation of China (No. 61803090).  
**Conflicts of Interest:** The authors declare no conflict of interest.