# A Brunnstrom Stage Evaluation System with Fuzzy-Accelerometer
## 3 Experimental Design and Results
It shows that E6 (60 %) is classified as ST5, despite its FMR score of 37 % being similar to ST4 results but is correctly classified as ST5 \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimenter** & **Number of Motion** & **Flexion** & **Pronation** & **Lap to Chin** & **Simulate Level** \\ \hline \multicolumn{7}{c}{**Bad Side**} & **Good Side** \\ \hline E1–E10 & 10 & 0–30 & 0–30 & 0–1 & 5 & ST2 \\ E1–E10 & 10 & 30–60 & 30–60 & 1–2 & 5 & ST3 \\ E1–E10 & 10 & 60–90 & 60–90 & 2–3 & 5 & ST4 \\ E1–E10 & 10 & 90–120 & 90–120 & 2–4 & 5 & STS \\ E1–E10 & 10 & 120–150 & 120–150 & 3–4 & 5 & ST6 \\ E1–E10 & 10 & 150–180 & 150–180 & 5 & 5 & Healthy \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation Motions references. \begin{table}
\begin{tabular}{c c} \hline \hline
**Range** & **Brunnstrom Level** \\ \hline
0\% \(>\) Level \(<\) 17\% & ST2 \\
17\% \(\geq\) Level \(<\) 34\% & ST3 \\
34\% \(\geq\) Level \(<\) 51\% & ST4 \\
51\% \(\geq\) Level \(<\) 68\% & ST5 \\
68\% \(\geq\) Level \(<\) 85\% & ST6 \\
85\% \(\geq\) Level \(<\) 100\% & Healthy \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Brunnstrom Level mapping table. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **ST2** & **ST3** \\ \hline
**Experimenter** & **FMR** & **PMR** & **LTCMR** \\ \hline E1 & 29.8\% & 10.3\% & 20.0\% & 15.8\% & 39.5\% \\ E2 & 31.0\% & 27.2\% & 0.0\% & 27.0\% & 40.0\% \\ E3 & 27.5\% & 17.2\% & 20.0\% & 15.1\% & 36.7\% \\ E4 & 42.4\% & 22.1\% & 20.0\% & 25.5\% & 45.0\% & 42.8\% \\ E5 & 27.7\% & 23.9\% & 20.0\% & 15.2\% & 36.1\% & 41.0\% \\because its PMR score of 72.6 % is similar to that of ST6. In the ST6 results, the action assessment results of all subjects match the reference table, thus all ten subjects are correctly classified as ST6. The Healthy results of all subjects match the reference table, so all ten subjects are correctly classified as Healthy. Figure 11 shows that a comparison of the actual experimental results with preset results. ST2 shows eight subjects successfully classified, while two are classified as ST3. Nine subjects are correctly classified as ST3, while one is classified as ST4. Likewise, nine subjects are correctly classified as ST4, and one is misclassified as ST3. For ST5-Healthy, all 30 data results are correctly classified. Thus, for the entire simulation, 56 results were correctly classified and four were misclassified, for a success rate of 93.3 %. From the experimental results, the errors were concentrated between ST2 and ST4 in Table 5 because the movement of the test subjects differs from that of actual patients, corresponding to a low Brunnstrom level due to low stability and angle of action. There is no way to confirm the standard deviation, thus during simulations, subjects trying to mimic low action stability result in more exaggerated movements making it difficult to obtain a standard for the action. Thus the resulting value of the action may differ from the expected result. ST5-Healthy results are closer to the actions of healthy subjects, and thus ST2-ST4 miscategorizations are wavied. The experimental results show that the fuzzy algorithm can effectively distinguish the features of different conditions and make correct classifications. Unlike Artificial Neural Networks (ANN) or Support Vector Machine (SVM) the proposed system allows physical therapists to make immediate adjustments to accommodate the actual condition and needs of hemiplegic patients. Although the fuzzy algorithm used in this paper is a subjective and simple method, it does not require the construction of complex mathematical models, and using rules of thumb it can be adjusted at any time in response to  
Figure 11: Default results comparison with actual results.