Project Abstract

Motivation and Background:The intersection of computer vision technologies and Traditional Chinese Medicine (TCM) offers novel approaches to medical diagnostics and treatments. Utilizing YOLO technology for human body recognition, this project aims to enhance the accuracy and efficiency of TCM diagnostics, addressing the need for integration of modern technological advances with traditional medical practices. This is particularly timely and impactful as it leverages cutting-edge AI capabilities to support and enhance historical health practices.Research Proposition:This research is uniquely positioned at the convergence of deep learning and TCM, utilizing the YOLO algorithm to analyze body poses and identify key anatomical features. The main aim is to develop a system that supports TCM practitioners by providing precise acupoint localization and pathological feature recognition, thereby improving treatment accuracy and personalized care.Methods Employed:The methodology involves using YOLO-based algorithms for dynamic and static human body recognition to detect key features such as body postures and acupoints critical for TCM. The project utilizes OpenCV for image processing, Python for programming, along with TensorFlow or PyTorch for deploying deep learning models, ensuring a robust design that is grounded in both theoretical and practical applicability.Main Findings:(Anticipated as the project progresses) The project expects to achieve high accuracy in body pose recognition and feature detection which are integral for identifying TCM-related physiological markers and acupoints. The findings aim to contribute significantly to personalized medicine by accurately suggesting treatment protocols based on empirical data.Conclusion and Contribution:The project promises to significantly advance the integration of AI in medicine by providing a reliable, efficient, and user-friendly system for TCM diagnostics and treatment planning. It will bridge the gap between traditional medical knowledge and modern technology, providing insights that were previously inaccessible, and enhancing both the practitioner’s and patient’s experience with precise, data-driven support.

Keywords: Human Body Recognition, Traditional Chinese Medicine, Acupoints

 Conference Details

Session: Presentation Stream 25 at Presentation Slot 10

Location: GH049 at Wednesday 8th 13:30 – 17:00

Markers: Megan Venn-Wycherley, Fernando Maestre Avila

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for work