## 4 Experiments and Analysis
### Experiment 1: The FHP Detection and Correction System  
Figure 15 captures a portion of the real-time CVA of Person#2, where the horizontal axis represents time and the vertical axis represents the CVAs.  
Figure 14: Experimental environment: MPU6050 attached to the cervical spine.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Standard CVA (\({}^{\circ}\))** \\ \hline Person\#1 & 58 \\ Person\#2 & 48 \\ Person\#3 & 56 \\ Person\#4 & 50 \\ Person\#5 & 46 \\ Person\#6 & 58 \\ Person\#7 & 49 \\ Person\#8 & 45 \\ Person\#9 & 46 \\ Person\#10 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard CVA.  
Figure 13: The raw and sampled data of the sensor angles.  
The standard CVA for Person#2 was 48\({}^{\circ}\), as shown in Table 5. Thus, Algorithm#1 subtracted 5 from the average angle and defined 43\({}^{\circ}\) as the _baseline__CVA_ for Person#2. Changes in the CVA and system judgment over time are shown in Figure 15. Times corresponding to the region shaded with blue indicate that the subject had a good posture, while those shaded with red indicate that the subject had an FHP.  
At about 250 s after the start of the experiment, the participant's CVA drops below the baseline. However, because Algorithm#1 is based on a bad posture sustained for more than 10 s, it only judges this to be a bad posture after about 10 s and then gives the participant a visual reminder. Algorithm#2 is triggered when this happens, and it tries to bring the CVA back to the baseline. If satisfied, it considers this a good posture. At about 310 s after the start of the experiment, if the CVA falls below baseline again and lasts for more than 10 s, then Algorithm #1 again considers the participant to have poor posture. Figure 16 shows the participant's state changes through the posture images.  
In the following section, how and why _Case_ 1 and _Case_ 2 were distinguished will be explained with respect to Algorithm#2 using real experimental data.  
Figures 17 and 18 shows some of the real-time CVA data obtained from Person#7, who underwent experiments, and the corresponding posture images. Algorithm#1 could determine that the participant was in a bad posture. However, after several experiments, we decided to use _Threshold2_ to distinguish between cases where the user belatelly realizes that they are assuming a bad posture, or cases where they try to maintain a good posture (_Case_ 1), as well as cases where the user tries to correct the bad posture (_Case_ 2). _Threshold2_ is the average of the data received in the previous 10 s plus 1. Because our system is a personalized system, users can also set _Threshold2_ to be based on the average of the previous values for 10 s plus a number such as 0.5 or 2 instead of 1. Therefore, if the user maintains a bad posture, the CVA is unlikely to pass over _Threshold2_. Figure 17 shows a case where the CVA slowly increased from 510 s, crossed _Threshold2_ at 517 s, and then transitioned to _Case_ 2. However, the CVA did not become greater than the baseline, and--at 522 s--it fell below _Threshold2_ again, thus transitioning to _Case_ 1. From then until 535 s, Person#7's CVA remained at 42; as such, the system continued to alert their poor  
Figure 16: Posture correction via a visual representation of the current posture. (**a**) Screen display immediately at 240 s after the experiment starts. (**b**) Screen display at 280 s after the experiment starts.  
Figure 15: FHP detection (blue-shaded area: good posture and red-shaded area: FHP posture).  
posture. After receiving the alert, Person#7 corrected their posture and their CVA increased, eventually bringing the CVA above the threshold and out of the bad posture. _Case_ 2 was for alerting the user when the user corrected their posture and their CVA increased but was still below the baseline; this helped the user escape their bad posture state.