# A Smartphone-Based Mobility Assistant Using Depth Imaging for Visually Impaired and Blind
## 4 Results and Discussions
### Usability Experience of the Mobile Application  
After the experiment, the participants were asked to evaluate the experience in using the assistive application, and the survey results can be seen in Table 6. Feedback for the obstacle detection experiment include that the device was very lightweight and comfortable to use. The audio warning was helpful but some users found the looping audio warning annoying to listen to at times. The overall assistive capability of the obstacle detection was relatively safe, but caution and using the walking cane is always recommended as blind spots still exist with its detection range limitation. Feedback on the object detection states that it is a reliable feature, but it takes a few practices and learning to use effectively.  
\begin{table}
\begin{tabular}{c|c c} \hline \hline \multirow{2}{*}{**Obstacle Detection Test Results**} & \multirow{2}{*}{**Finish Time**} & **No. of Obstacles Successfully** & **No. of Obstacles the APP** \\  & & **Detected and Evaded** & **Failed to Detect** \\ \hline Participant 1 & 4:57 min & 8 & 2 \\ Participant 2 & 6:53 min & 7 & 4 \\ Participant 3 & 4:08 min & 3 & 6 \\ Participant 4 & 5:58 min & 6 & 3 \\ Participant 5 & 5:15 min & 7 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Obstacle detection evaluation with finish score, successfully detected and evaded, and detection fails of the application.  
\begin{table}
\begin{tabular}{c|c c} \hline \hline \multirow{2}{*}{**Objects**} & \multirow{2}{*}{**Average Confidence Scores**} & \multicolumn{2}{c}{**Successful Grab Counts for Each Participant (Max of 5)**} \\ \hline AC Remote & 72.31\% & 4 \\ Bottle & 96.59\% & 5 \\ Cup & 92.01\% & 5 \\ Scissors & 65.23\% & 2 \\ Mouse & 86.72\% & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Object detection evaluation with average confidence score and successful grab counts for each object.  
During the development of the system, a spectrum of vision loss was extensively researched where it was found out that there is a difference in perceiving and adapting information depending on the severity of blindness [30]. For example, individuals who are born blind have no perception of colors and basic concept of shapes at first. Thus, they need more supervision as compared to those whose vision was gradually lost or worsened over time as they already have the basic concepts in mind. Numerous researches have explored different approaches of technologies and methods to achieve an inclusive navigational aid, such as the previously mentioned related works. Most of the mentioned studies require all sorts of sensors, wiring, or external mechanisms in order to function well to its purpose. While it does not mean a drawback on the system, it is just inconvenient to setup at times as it requires expertise or comprehension of the developed system, whereas utilizing a depth sensing application with an inclusive user interface on a smartphone only is a more accessible option. Smartphone-based navigational aids have already existed for a long time, with examples such as an obstacle detection and classification system using points of interest [31], obstacle collision detection using emitted acoustic signal beep from a microphone on a smartphone [32], obstacle avoidance using smartphone camera and emitted laser light triangulation [33], and many more. These studies provide promising results as a navigational aid to VIB people; however, these implementations have inconsistencies due to hardware and software limitations. The current improvement means that using a mobile phone with a 3D depth camera provides portability and does not require additional costs to purchase expensive hardware and burden the user with extra devices.