## 3 Design and Implementation
### Classification Algorithm
Participants tended to think they flipped the tag enough for 180 degrees even though they actually flipped the tag for less than the required threshold. The VF180 gesture was prone to errors and being mistakenly recognized as vertical flip of 90 degrees (VF90) or as some other undefined pattern. Since the haptic gesture of complete TO takes longer duration compared to the other gestures, it can accidentally be recognized as a horizontal flipping in the case when users make the motion too rapidly or too slowly. The TO gesture includes flipping manipulations in both the left and right directions. It showed rather high ratios of confusion with both horizontal flipping of 90 degrees to the right (HFR) and horizontal flipping of 90 degrees to the left (HFL). The haptic gestures that resulted in no response occurred when gestures were made too rapidly, causing a lack of sufficient signal accumulation for sampling. #### 4.2.2 User Preference Evaluation

Some haptic gestures were preferred by the participants above others. To test the usability of the haptic passcode, we performed a second test with the same participants on user preference of haptic gestures used to construct the passcode. Firstly, we asked the participants to construct a personal passcode in the range of three to six digits and examined their preferences. Participants were allowed to use same gestures repetitively to make the passcode. The numbers of each haptic gesture used to construct each digit were counted out of the total 270 digits. It was found that the favorite gesture for making a haptic passcode with the ID tag by the participants was HFR followed by TAP and HFL. The least preferred haptic gesture was TO, followed by shaking (SHK) and VF180. The participants tended to avoid those patterns which require relatively bigger motion and take a longer time to make. The TO and VF180 gestures were the ones with both lower recognition accuracies and lower preference by the participants among all the gestures. The preference of each gesture used to construct a passcode is shown in Table 1.

The average time taken to complete a passcode input linearly increased with the number of digits made with the gestures. It was observed that the average time to completely input six digits of a passcode was more than 19 s while the estimated time based on latency calculation was approximately 10 s (Figure 4). The difference can be explained in two ways. First, the average completion time is composed of the system response time and the time taken for user recognition of the system confirmation since the participants needed to pause until the system confirmed the user input with visual, auditory, or tactile feedback before making a gesture for the next digit. The other reason may be the burden of memory. As the passcode included more digits to construct, participants seemed to need more time to retrieve their passcode combinations. The required time to complete the six-digit passcode \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Recognition Rate (\%)**} & \multicolumn{8}{c}{**Actual Pattern**} \\ \cline{2-9}  & **TAP** & **SHK** & **HFR** & **HFL** & **VF90** & **VF180** & **TO** \\ \hline \multirow{8}{*}{**Recognized Pattern**} & **TAP** & 95.3 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **SHK** & 0 & 92.7 & 0 & 0 & 0 & 0 & 0 \\  & **HFR** & 0 & 0.7 & 92.0 & 9.3 & 0 & 0 & 10.0 \\  & **HFL** & 0 & 0 & 0 & 88.0 & 0 & 0 & 4.7 \\  & **VF90** & 0 & 0 & 0 & 0 & 94.0 & 7.3 & 0 \\  & **VF180** & 0 & 0 & 0 & 0 & 0 & 74.0 & 0 \\  & **TO** & 0 & 0 & 0 & 0 & 0 & 0 & 82.0 \\  & **No Response** & 4.7 & 6.7 & 8.0 & 2.7 & 6.0 & 18.7 & 3.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for recognition accuracy of the haptic gestures using the ID tag. increased more rapidly as a result. The participants suggested that the time to input a passcode took too long for the system to recognize and needs to be faster.