|Strategies for Feet Massage Robot to Position the Pelma Acupoints with Model Predictive and Real-time Optimization
Yingzi Wei*, Kanfeng Gu, Xujing Cui, Changzhong Hao, Hongguang Wang, and Yong Chang
International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp.628-636, 2016
Abstract : It is one of critical factors for a massage robot to find and position the acupuncture point precisely in
order to cure the diseases. Based on large amount of sample data offline, Chinese medical empirical knowledge
is also introduced to build the prediction model. The massagist prescription and the robot mechanism devise are
both considered for robot positioning. Least squares method is of simplicity, easy to use and high efficiency.
Its real-time calculation is very effective, too. A modeling method for robot positioning is proposed based on
least squares. Knowledge consultation is set for the calculation of acupoint position. The robot needs to get the
feature points of a foot to be massaged. The foot contour sampling data are divided into piecewise curve fitting. Qlearning
is adopted to optimize the robot positioning for they are model free. CMAC (Cerebellar Model Articulation
Controller) cerebellum model is incorporated into the function approximation of Q learning. The learning system
is rewarded by referring to the strengths of instrumental signal. By the direct representation, the model of human
pelma acupoint is expressed with the vector variables and formal computer language. Through prediction model’s
calculation, the robot will work out the rough position of acupuncture point. Meanwhile, Q learning does the online
adjustment for accurate location. These strategies provide for the robot to automatically search and position the
pelma acupoint with little real-time computation and storage. The idea of this paper also prompts a research cue for
the development of Chinese medical standardization.
Feet massage robot, knowledge consultation, least-squares method, reinforcement learning, robot positioning.
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