Kinematic-driven human-robot interaction system with deep learning for flexible acupuncture needling manipulations

Abstract

Acupuncture is an important therapeutic method in traditional Chinese medicine and essentially characterized by flexible needling manipulations that are complicated for repetitions. In this work, we designed a bio-inspired acupuncture robot combined with human–machine interaction system that can implement flexible and precise needle manipulations as manual acupuncture. In the proposed framework, the manual needling process was recorded with online imaging techniques. With a deep learning-based acupuncture estimator, kinetic parameters of lift insertion (LI) and twist rotation (TR) manipulations were extracted in three-dimensional space and the precent of correct key points for operator’s hand pose estimation achieved 94.03%. It is founded that the variation of operator’s finger vector angle for LI manipulation was much weaker than TR manipulation. Furthermore, inspired by kinematic characteristics of manual acupuncture, a six-degree-of-freedom robot was used to simulate the coordinated motion of hand joints in needling manipulations through robot kinematics analysis and trajectory planning with polynomial interpolation method. Numerical and experiential results showed that the acupuncture robot can mimic various needling manipulations as manual acupuncture. The flexibility and stability of the robotic acupuncture framework were evaluated for a wide range of needling parameters. It was demonstrated that the robot can follow the periodic dynamics of LI and TR manipulations with various frequencies and amplitudes, and perform accurate acupuncture operations with smooth needling trajectories as joint jitters during needle manipulations were completely eliminated. The presented results provide a new strategy for modern applications of acupuncture treatment with robotic and flexible needling manipulations to improve the therapeutic efficacy.