## 4 Experiments and Analysis
### Experiment 2: Measuring the Effectiveness of the Correction System in Real Life  
To show that the proposed system was as effective as an FHP correction system, we conducted the following experiments: two experiments with and without personalized feedback. In the first experiment, participants sat indoors facing a computer monitor similar to a real office environment, and they used the computer for web surfing, typing, etc. The experiment lasted 30 min, and the 10 participants (Table 2) sat in a chair in the room and initially used the computer with only the sensors attached, such that the subjects were not aware of their current CVA. In the second experiment, we used the proposed system to allow the subjects to see their CVA and posture in real time on the monitor. In the absence of personalized feedback, participants often became engrossed in the computer screen, thus decreasing their CVAs. Table 6 shows the average CVA of each participant up to 3 min after starting the experiment, as well as the drop in their CVAs due to being immersed in the screen. Table 6 shows that the CVA of the participants varied from person to person when they were immersed in a screen, and it was shown that it can drop by up to 6 degrees or more from the start. This naturally seems to progress to an FHP posture, but the participants were unable to recognize it. Figure 17: FHP correction (red-shaded area: _Case_ 1 and blue-shaded area: _Case_ 2). Figure 18: Posture correction via visual representation of the current posture. (**a**) _Case_ 1 in Algorithm#2 and (**b**) _Case_ 2 in Algorithm#2. Furthermore, under the same conditions, we used the proposed method to let the participants know their CVAs in real time, and we identified the times at which they were in an FHP in the two experiments, which was then tabulated. The FHP time was calculated as the time required for Algorithm#2 to run an experiment, which lasted about 900 s. Additionally, the average CVA for each experiment is shown in Table 7.  
Table 7 shows that, in the 15-min experiment, the FHP time in the presence of the feedback from our system varied from participant to participant. However, these values decreased by more than 5 min on average, and the average CVA increased by about 3\({}^{\circ}\). Some of the participants even tried to maintain their initial good posture to avoid the alert; therefore, the system was considered effective for correcting bad posture. It is also a personalized system because the baseline for determining the FHP status is set differently for each person. If the _baseline CVA_ was adjusted only in accordance with Person#1 and thus all the CVAs below 53\({}^{\circ}\) were considered an FHP, then Person#2, Person#4, and Person#6 would have been considered to be exhibiting an FHP even though they were not actually showing an FHP. In Table 8, several of the approaches for FHP detection reported in the literature are compared and corrected with the proposed method. First, the feasibility of the FHP detection, which is the neck forward posture, was checked. Other approaches such as the one proposed by [PERSON] et al. [21], which used sensors on chairs, and the approach proposed by [PERSON] et al. [18], which used the Openpose software, (v. 1.7.0) can detect incorrect posture but not the FHP. Subsequently, the feasibility of customizing the system by considering the different physical conditions of each person was also checked. Studies that used depth cameras [15] or IMU sensors [36] utilized fixed threshold values; hence, they cannot detect an FHP depending on the user's environment or physical condition.