# Wearable Vibration Based Computer Interaction and Communication System for Deaf
## 4 Experimental Results  
This study primarily selects the best feature methods and classification methods. Then, the prototype device has been tested using these selected methods on themselves. Meanwhile, tests have also been done on a computer environment and shown comparatively. The ReliefF method aims to find features' values and whether dependencies exist by trying to reveal them. This study selects the two best features using the ReliefF method. The two best feature methods turned out to be the Lmax and ZCR. The results of the feature extraction method described above are shown in Figure 4 according to the data we took from the data set. As can be seen, the best categorizing method is the Lmax with ZCR using SVM. The results in Figure 4 show the mean values between 1 and 4 m.  
As seen in Table 1, the results obtained in real time and data were transferred to the digital environment and compared with the results obtained after the stages of preprocessing, feature extraction and classification. The results in Table 1 show the mean values between 1 and 4 m.  
Figure 4: Knn’s and SVM’s Classification Success of Feature Extraction Methods (SVM: Support Vector Machine; Knn: K Nearest Neighborhood). Our wearable device produces results when there are more than 1 person. As can be seen in the Figure 5, people from 1-m and 3-m distances called the hearing-impaired individual. Our wearable device has noticed the individual who is close to him and he has directed that direction. As shown in Figure 5, the person with hearing impairment perceives this when the Person C behind the hearing impaired person calls to himself. 98% success was achieved in the results made in the room environment. It gives visual warning according to the proximity and distance. As shown in Table 2, measurements were taken in the room environment, corridor and outside environment. As shown in Table 2, our wearable device was tested in room, the hallway and the exterior with a distance of 1 and 4 m. The success rate is shown by taking the average of the measured values with the sound meter. Each experiment was tested and the results were compared. In Table 2, the average level increase is caused by the increase of noise level in noisy environment and outdoor environment, but the success did not decrease much. As seen in Table 3, perception of the direction of sound at a distance of ne meter was obtained as 97%. The best success rate was obtained by the sounds received from left and right directions. The success rate decreased with the increase of distance. As the direction increased, two directions were considered in the perception of sound, and the success rate decreased. And the direction where the success rate was the lowest was the front. The main reason for that is the placement of the developed human-computer interface system on the back of the individual. The sound coming from the front is being mixed up with the sounds coming from left or right. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environment** & **Measurement** & **Direction Detection (1 m)** & **Direction Detection (4 m)** & **Sound Detection** \\ \hline Room & 30 Db (A) & 98\% & 97\% & 100\% \\ Corridor & 34 Db (A) & 96\% & 93\% & 100\% \\ Outdoor & 40 Db (A) & 93\% & 89\% & 99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Success rate of different environment. Figure 5: Testing wearable system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Left** & **Right** & **Front** & **Back** \\ \hline Accurate perception of the system & 94.2\% & 94.3\% & 92\% & 92.7\% \\ Accurate perception of the individual & 92.8\% & 93\% & 91.1\% & 90.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Success rate of our system real time and obtained after computer. As seen in Table 4, when perception of where the sound is coming from is considered, high success was obtained. As can be seen in Table 4, voice perception without checking the direction had great success.