## 3 FHP Detection and Correction Using an IMU Sensor
### FHP Detection and Correction by Sensing Data Processing  
Section 3.1 mentions that the _baseline CVA_ is different for each person; hence, it is difficult to find a clear standard for determining FHP. Therefore, at the start of the  
Figure 1: Cranivertebral angle (CVA) measurement: (**a**) CVA definition. (**b**) CVA actual measurement: good posture (CVA = 50\({}^{\circ}\)). (**c**) CVA actual measurement: bad posture (CVA = 35\({}^{\circ}\)).  
Figure 2: Tools to measure CVA: (**a**) inclinometer and (**b**) CROM.  
experiment, the participant was instructed to maintain a good posture for \(>\sim\)10 s. In the meantime, this reference angle was set as their _baseline CVA_ using the average of the measured angles. Subsequently, as shown in Figure 3, the obtained sensing values received were continuously processed by transiting between Algorithm#1 and Algorithm#2, and the information regarding the current state was provided to participant.  
Setting the standard angle as the user's initial good posture after the start of the experiment starts will trigger Algorithm#1. If Algorithm#1 determines that it is a bad posture, then Algorithm#2 operates instead. If Algorithm#2 determines that the participant is out of a bad posture, then the system returns to Algorithm#1.  
Figure 4 is an algorithm that checks if current users have bad posture (FHP).  
According to [PERSON] et al. [37], people with neck pain have an average CVA of approximately 5\({}^{\circ}\) lower than those without neck pain. Therefore, neck pain is related to FHP [3]. The value obtained by subtracting 5\({}^{\circ}\) from the CVA corresponding to the good posture of a user (standard CVA) was set as the _baseline CVA_ in Figures 4 and 5; according to _Threshold_1 in Figure 4, it was determined whether the participant was in FHP when their wrong posture persisted for more than a few seconds. In this study, _Threshold_1 was set to 20. Because the time until a new angle was received was every 0.5 s, Algorithm#1 checks if the posture is wrong, i.e., if the posture is likely to be FHP, for more than 10 s. If Algorithm#1 recognizes it as a bad posture, Algorithm#2 works to induce the user to correct their posture on their own. Algorithm#2 determines the posture state by calculating how different the recently obtained CVA value is from previous ones. Finally, Algorithm#2 induces the user to escape the bad posture state.  
As shown in Figure 5, _Threshold_2 is obtained by adding one to the average sensing values for 10 s before the new sensing value enters the immediately preceding sensing  
Figure 4: Algorithm#1: determination of bad posture.  
Figure 3: The forward head posture (FHP) detection and correction system.  
values. If the new angle is less than _Threshold2_, it is classified as _Case 1_ and remarks that posture has not improved.  
If the entry angle is greater than the _baseline__CVA_ and less than _Threshold2_, it is classified as _Case 2_ and that the posture is improving but not sufficiently enough. However, if it is greater than the _baseline__CVA_, it is classified as _Case 3_ and is considered to be out of the bad posture state.