## 5 Online Classification Experiment
### Results and Discussions
#### 5.2.1 Classification of 17 Voluntary Movements
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author/s** & **Year** & \begin{tabular}{c} **No. of** \\ **Electrodes N\({}_{sh}\)** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Movements N\({}_{m}\)** \\ \end{tabular} & \begin{tabular}{c} **Sampling** \\ **Frequency** \\ \end{tabular} & \(N_{m}/N_{sh}\) & **Accuracy** & \begin{tabular}{c} **Number of** \\ **Subjects** \\ \end{tabular} \\ \hline [PERSON] et al. [30] & 2004 & 6 & 6 W & 200 Hz & 1 & 92.1\% & 3 H and 2 A \\ [PERSON] et al. [23] & 2005 & 4 & 6 IF & 200 Hz & 1.5 & 80\% & 10 H \\ [PERSON] \& Hu [18] & 2008 & 4 & 6 (5 W + R) & 1000 Hz & 1.5 & 95 \% & 11 H \\ \hline [PERSON] et al. [21] & 2009 & \begin{tabular}{c} 32 \\ 19 \\ \end{tabular} & 12 (10 IF \& 2 CF) & 2000 Hz & \begin{tabular}{c} 0.38 \\ 0.63 \\ \end{tabular} & 90\% & \begin{tabular}{c} 5 H \\ 83\% \\ \end{tabular} \\ \hline [PERSON] et al. [17] & 2011 & 2 & 4 W & 1000 Hz & 2 & 88.4\% & 3 H \\ \hline [PERSON] et al. [11] & 2011 & 8 & 7 (4 IF + 2 CF + HG) & 10,000 Hz & 0.88 & 89\% & 5 H \\ \hline [PERSON] et al. [33] & 2011 & 16 & 13 (12 IF + R) & 16,000 Hz & 0.81 & 80\% & 5 H and 1 A \\ [PERSON] et al. [29] & 2012 & 3 & 3 CF & 10,000 Hz & 1 & 97\% & 1 H \\ [PERSON] et al. [19] & 2015 & 8 & 7 (5 W + IF + R) & 1000 Hz & 0.88 & 90\% & 4 H \\ [PERSON] et al. [32] & 2016 & 19 & 19 (12 IF + 7 HG) & 1000 Hz & 1 & 96\% & 9 H \\ \hline This work & - & 6 & \begin{tabular}{c} 17 (10 IF + 6 W + R) \\ 9 (4 IF + 4 W + R) \\ \end{tabular} & 200 Hz & \
\begin{tabular}{c} 2.83 \\ 1.5 \\ \end{tabular} & 63.8\% &
\begin{tabular}{c} 63.8\% \\ 72.9\% \\ \end{tabular} & 12 H \\ \hline \hline \end{tabular}  
* IF: Individual Finger Movement. W: Wrist Movement. R: Rest State. CF: Combine Finger Movement. HG: Hand Grasp Movement. H: Healthy. A: Amuputee.  
\end{table} 
Table 3: Summary of existing EMG based pattern recognition studies. configure the current setup onto existing prosthetic hand. Furthermore, this work also offers the highest ratio of movements to electrodes (\(N_{m}/N_{ch}\)). On the other hand, the limitation of the proposed system lies in the fact that the accuracy is lower than others studies. One of the reasons for this is that duration of each motion is too long. Many participants have reported experiencing difficulty in sustaining each movement for 5 s. Pronation/supination and flexion/extension of the index, middle, and the ring finger movements were especially hard according to the participants. This can be observed by looking at the ANN output signal where the system outputs the correct decisions only for the first 3 s. Muscle fatigue is also another reason for low accuracy. Participants also reported that they experienced muscular fatigue after performing 6-7 continuous classification trials (11-12 trials, if including trials from the training stage). The effects of the fatigue can be seen in the gradual decline of correct rates shown in Figure 12. The results of a paired \(t\)-test show that decline of accuracy due to fatigue starts on the 8 th trial (\(p<0.1\)) and drops significantly on the 9 th trial (\(p<0.05\), approximately 1 h of continuous usage). This observation suggests that in practice, a control strategy that will hold the posture of the prosthetic hand without having to sustain muscular contraction may be desirable to help relief the level of fatigue. Also, re-training of the classifier should be performed after 1 h of continuous usage. Thus, the above results address the research question number 3. It is also worth mentioning that there is a tradeoff between the number of channels and the accuracy of the system. In practice, depending on the demands of the user or the type of application, the number of electrodes should be configured for each user in order to suit the needs of any individual.