Forward Head Posture Classification Using Deep Learning Models on Facial Recognition Data

Byeongsu Kim (1), Minsu Chae (2), Yeongmin Kim (3), Inyong Jeong (4), Hwamin Lee (5)
(1) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
(2) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
(3) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
(4) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
(5) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, Republic of Korea
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How to cite (IJASEIT) :
Kim , Byeongsu, et al. “Forward Head Posture Classification Using Deep Learning Models on Facial Recognition Data ”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 805-10, doi:10.18517/ijaseit.14.2.18519.
Forward Head Posture (FHP) refers to a condition where the head protrudes forward, significantly contributing to neck pain and being associated with decreased productivity and psychological distress. This study investigates the nuanced classification of FHP and proposes a universally applicable methodology for its identification and analysis using deep learning. Leveraging the Korean Facial Image (K-FACE) dataset, rigorous image preprocessing with the Yolo-v8 model was conducted to facilitate accurate measurement of the CranioVertebral Angle (CVA) from various perspectives. The study meticulously evaluated the classification effectiveness of three advanced deep learning models: EfficientNet-B7, NFNet-F7, and ResNet-152. Among these, EfficientNet-B7 demonstrated superior performance with an accuracy of 0.69 and a recall score of 0.69 compared to other models. Additionally, comparisons based on camera angles within the EfficientNet-B7 model highlighted its excellence, particularly at the ±75° angle. The importance of image regions in EfficientNet-B7 was confirmed through Grad-CAM analysis, emphasizing the critical role of the neck region in accurately classifying FHP images. This comprehensive performance comparison and the proposed detailed classification methodology underscore the potential for generalization in FHP classification. Furthermore, by leveraging a unique dataset and employing state-of-the-art classification techniques, this research offers a novel perspective on the discourse surrounding FHP. Future research could integrate expanded facial image datasets and apply transfer learning techniques to further enhance the precision of FHP classification, thereby improving diagnostic accuracy and offering targeted interventions for individuals experiencing neck pain associated with FHP.

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