## 2 Related Works  
The research on determining an individual's posture has resulted in the suggestion of methods for analyzing captured images of people with sensors. [PERSON] et al. [11] placed a kinetic camera on a monitor to capture a human participant's frontal view, wherein the depth image analysis was used to measure the distances between the camera and the forehead, as well as between the camera and the torso. The difference between these two distances was then used to determine the FHP status. Meanwhile, [PERSON] et al. [12] used a smartphone to take the frontal image of an individual, and the neck tilt was measured based on the tilt of the smartphone and the tilt of the face (which was obtained by analyzing the frontal image). Recently, some researchers have attempted to classify posture using machine learning. [PERSON] et al. [13] took a picture of a person with a smartphone, and they then used the tilt of the smartphone and the slope of the face obtained through image analysis for measuring the tilt of the neck. [PERSON] et al. [14] cut a video recording of a person's sitting posture into frames to collect images, and they then used a MobileNetV2-based model to classify the sitting posture into three categories (straight, backward, and forward), wherein an accuracy of 91.5% was achieved. [PERSON] [15] utilized Openpose [16] to extract body joints and lines connecting joints, while a convolutional neural network (CNN)-based model was used to distinguish between good and bad postures, and it achieved a 90% accuracy. However, when analyzing posture through camera images, classification or detection accuracy varies greatly depending on the camera position, and considerable training data are required to accordingly determine human posture given a particular human body structure. In addition, there is a risk of privacy violation because the image analysis process exposes either the front or side view of an individual. Meanwhile, studies that utilize sensor data to determine posture are as follows. In a study by [PERSON] et al. (2018), sensors were attached to chairs and cushions to determine the sitting posture, and participants were required to sit on a chair with six pressure sensors to measure the force applied to each sensor. Based on this, a system was proposed that classifies the sitting posture and notifies the individual whether the posture is good or bad via a mobile application. Meanwhile, in the study conducted by [PERSON] et al. (2018) for determining the sitting posture using machine learning, six pressure sensors were placed on a chair cushion and six distance sensors were attached to the backrest to collect data while a k-nearest neighbors algorithm was used to determine the participant's sitting posture. [PERSON] et al. (2018) attached six flex sensors to a chair and used an artificial neural network to determine the sitting posture. However, in the aforementioned studies, the number of sensors required to predict the sitting posture was high, thus making the approach costly. The indirect location of sensors on the chair cushion or backrest limited the sensors' ability to accurately distinguish between normal and FHP states. Because of these disadvantages, studies have reported the direct attachment of wearable sensors to the human body to determine the user's posture for effective posture correction ([PERSON], 2018). For instance, [PERSON] et al. (2018) attached three IMU sensors to a participant's back and shoulders in the form of a belt; therefore, a system that can determine the user's back posture by detecting body tilt via sensors was created; in addition, accordingly, sound was also used by the system for the purposes of an alarm. Meanwhile, [PERSON] et al. (2018) attached an IMU sensor to the ear of a participant to obtain gyroscope values, as well as for determining whether the user was in an FHP state. Gyroscopes use the Coriolis force, the inertial force of a rotating object, to measure angular velocity, and they also integrate it to obtain the neck tilt ([PERSON] et al., 2018).