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Pu Zhao

at Imperial College London

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Proceedings Article | 13 March 2024 Poster + Paper

Deep learning-based patient in-position detection for acupuncture treatment

Pu Zhao, Liaojun Pang, Shufen Cao, Zhicheng Cao

Proceedings Volume 12857, 128570R (2024) https://doi.org/10.1117/12.3023364

KEYWORDS: Object detection, Head, Data modeling, Performance modeling, Neck, Medicine, Evolutionary algorithms, Data acquisition, Cameras, Deep learning

Read Abstract +

As a traditional Chinese medicine practice, acupuncture has long been shown to benefit pain and stress relief (especially for elderly people with chronical cases). Therefore, acupuncture is an important and effective alternative medical therapy for disabled elderly population living in areas of low healthcare coverage, which has become a more and more serious social problem as the Chinese population ages rapidly. However, training of acupuncturists is quite expensive and time consuming. With the arrival of the era of AI, how to automate the process of acupuncture treatment and minimalize the involvement of human labor has emerged as a great challenge and opportunity. This research studies a prerequisite of automatic acupuncture treatment: patient in-position detection during the acupuncture treatment process. We propose a fast and accurate one-stage anchor-free DNN model for patient in-position detection. Our model is an improvement of the basis model, YOLO X. The proposed framework consists of a backbone of CSP-DarkNet, a neck of feature pyramid network and a Decoupled Head. As for loss function, we combine the CIoU and the alpha-IoU losses to inherit both their advantages. A simplified version of the advanced label assignment technique of OTA, as well as data augmentation strategies of Mosaic and MixUp are utilized to improve the algorithm performance. Results on a self-collected dataset of acupuncture treatment (named as ATPD Dataset) show that our algorithm significantly outperform other state-of-the-art methods in the literature that are either multiple-staged or single-staged.

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