| Using Artificial Intelligence to Automatically Assess Forward Head Posture with Mobile Images in a Telerehabilitation Setting |
| Paper ID : 1137-SPORTCONGRESS (R3) |
| Authors |
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Faezeh Teymouri * Former M.Sc. Student, Faculty of Physical Education and Sports Sciences, Allameh Tabataba’i University |
| Abstract |
| Introduction: Forward Head Posture (FHP), also called “text neck,” has become a common postural problem among Generation Z due to prolonged smartphone use. This condition leads to increased cervical flexion, muscular imbalance, and decreased proprioceptive control. Traditional posture assessments often require in-person evaluation, which limits accessibility and efficiency. With the rise of telerehabilitation (TR), there is a growing need for reliable, objective, and automatic methods to assess posture remotely. Methods: This conceptual study proposes an AI-based framework to automatically evaluate FHP using mobile phone images in a telerehabilitation setting. Participants capture side-view photos using standard smartphones. The AI algorithm (such as OpenPose or MediaPipe) detects key anatomical landmarks (ear, shoulder, and neck points) and calculates the Craniovertebral Angle (CVA). Smaller CVA values indicate a higher degree of forward head deviation. The trained AI model can classify postures as normal or abnormal and provide real-time feedback through an online platform. Results: The proposed model demonstrates how AI can be integrated into digital rehabilitation systems to support remote posture monitoring. This approach offers a faster, more objective, and more accessible way to evaluate posture, reducing the need for manual measurements. It also provides valuable data for therapists to design and monitor corrective exercise programs effectively. Conclusion: AI-assisted posture analysis can enhance the accuracy and scalability of telerehabilitation programs. The integration of AI tools into corrective exercise practice supports continuous monitoring, early detection, and prevention of postural disorders among young adults. Future research should validate the accuracy of this model and explore its application in broader clinical and educational contexts |
| Keywords |
| Artificial intelligence, Forward Head Posture, Telerehabilitation, Generation Z, Posture assessment |
| Status: Abstract Accepted (Poster Presentation) |
