Real-time monitoring of manual acupuncture stimulation parameters based on domain adaptive 3D hand pose estimation. (May 2023)

• Record Type: Journal Article
• Authors: Xu, Liuliu; Gong, Haifan; Zhong, Yun; Wang, Fan; Wang, Shouxin; Lu, Lu; Ding, Jinru; Zhao, Chen; Tang, Wenchao; Xu, Jie
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Acupuncture monitoring -- Hand pose estimation -- Domain adaptation -- Neural networks -- Benchmark; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104681
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Manual acupuncture (MA) is a widely used type of therapy method in the world, its treatment result and clinical safety are highly related to the selection of stimulation parameters (needling amplitude and frequency) of acupuncturists. However, to date, there is no stimulation parameter measurement solution that can be conveniently used in the clinic. Thus, there is an urgent need to develop a single camera-based real-time monitoring system for MA operation. This monitoring system is expected to give the result of both the amplitude and frequency of MA. Considering that constructing the labeled MA monitoring dataset is laborious and time-consuming and that there is a large amount of unlabeled data, we propose an adaptive orientation-based domain adaptation framework to alleviate the domain shift and achieve better performance. Moreover, we contribute a benchmark that contains 20 videos of on-body MA operation and 30 videos of on-simulator MA operation with 3D coordinates to facilitate the future development of real-time MA monitoring. Extensive experiments on the proposed benchmark demonstrate the superiority of the proposed methods on both movement estimation and frequency estimation of hand acupuncture. The application prospects of this framework for the clinical work of MA included the investigation of dose–effect relationship of MA, enhancement of its operation safety, etc. Our data is publicly available at https://github.com/SHUTCM-tcme/SUTCM-AM . Highlights: A novel framework was proposed to accurately monitor the manual acupuncture stimulation parameters. This framework can be used to investigate the dose–effect relationship of acupuncture and improve its safety. A high-quality benchmark for real-time hand movement estimation was constructed. The effectiveness of the proposed method was demonstrated in extensive experiments.