## 4 Discussion  
### The Classification Accuracy Comparison with Other Typical Methods  
The average classification accuracy of the amputees after transfer learning achieved 78.1% on the 17-class motion intention recognition task. Compared with the other two similar studies in Table 2, both of which tended to transfer knowledge from intact-limb to amputated-limb subjects, the proposed method had a better recognition performance.  
Table 3 compares the recognition performance of our method with the state-of-the art methods assessed on amputees (DB3). [PERSON], [PERSON] et al. [32] classified 50 movements on 11 amputees to establish a baseline. In their study, the CNN's average classification accuracy was under 40%, and the SVM classifier had the best accuracy at 42.7%. [PERSON], [PERSON] et al. [26] employed different features (e.g., WL, MAV, auto-regressive co-efficient (ARC), and logarithmic variance (LV)) in a REM (random Fourier mapping) classifier and obtained an average accuracy of 53.3% for 50 movements on 11 amputees. These two studies both classified 50 movements, which is more than twice the number of classes considered in our study (i.e., 17 movements); thus, the accuracy gap between theirs and ours may not be so large, and we have listed them only to present a baseline. [PERSON] and [PERSON] [33] enhanced their categorization accuracy further. Using the extreme learning machines (ELM) technique, their study achieved a mean accuracy of 67.0% for 17 movements. Inspired by [32], [PERSON], [PERSON] et al. [19] utilized a deep learning model similar to Le-net and demonstrated a greater average accuracy of 67.5%. In our experiments, the results showed a superior performance with an average accuracy of 78.1%. Note that [PERSON] and [PERSON] [34], [PERSON], [PERSON] et al. [19], [PERSON], [PERSON] et al. [35], and [PERSON], [PERSON] et al. [36] removed Subject 7 from their work, as his forearm was entirely lost, as is shown in Table 1. This severe condition can result in an exceptionally low rate of correct recognition. Consequently, a new set of models were trained which excluded Subject 7, as the above references did, for comparison. The proposed method then attained an average accuracy of 83.5%, which surpasses the state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON], [PERSON] et al. [37]** & **[PERSON], [PERSON] et al. [19]** & **Proposed** \\ \hline Motions & 17 & 17 & 17 \\ Int./Amp. & 20/9 & 20/11 & 20/11 \\ Features & Avg. MAV/VAR/WL & mDWT & mDWT \\ Classifier & SVM & CNN & LECNN \\ Non-transfer & 52.1\% & 62.0\% & **73.4\%** \\ Transfer & 51.9\% & 67.5\% & **78.1\%** \\ Improvement & \(-\)0.02\% & 5.5\% & 4.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition performance with the other two transfer strategies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gestures** & **Amputes** & **Features** & **Model** & **Accuracy** \\ \hline [PERSON], [PERSON] et al. [32] & 50 & 11 & RMS & SVM & 42.7\% \\ [PERSON], [PERSON] et al. [26] & 50 & 11 & LV/ARC/WL/MAV & RFM & 53.3\% \\ Cene and Balbinot [33] & 17 & 10 & RMS & CNN & 56.9\% \\ [PERSON] and Balbinot [34] & 17 & 11 & Avg. RMS/MAV/SD & ELM & 67.0\% \\ [PERSON], [PERSON] et al. [35] & 17 & 10 & Feature Selection & SVM-REF & 74.8\% \\ [PERSON], [PERSON] et al. [36] & 10 & 10 & Spectrogram & CNN & 73.3\% \\ [PERSON], [PERSON] et al. [19] & 17 & 11 & mDWT & CNN & 67.5\% \\ [PERSON], [PERSON] et al. [19] & 17 & 10 & mDWT & CNN & 82.3\% \\ Proposed & 17 & 11 & mDWT & LECNN & **78.1\%** \\ Proposed & 17 & 10 & mDWT & LECNN & **83.5\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of classification performance to the state-of-the-art methods on amputees.