Human motion prediction using optimized sliding window polynomial fitting and recursive least squares  

作　　者：Li Qinghua Zhang Zhao Feng Chao Mu Yaqi You Yue Li Yanqiang 

机构地区：[1]School of Electronic and Information Engineering,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China [2]School of Electrical Engineering and Automation,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China [3]Jinan Engineering Laboratory of Human-Machine Intelligent Cooperation,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China [4]Institute of Automation,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250101,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2021年第3期76-85,110,共11页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61701270);the Young Doctor Cooperation Foundation of Qilu University of Technology(Shandong Academy of Sciences)(2017BSHZ008)。

摘　　要：Human motion prediction is a critical issue in human-robot collaboration(HRC)tasks.In order to reduce the local error caused by the limitation of the capture range and sampling frequency of the depth sensor,a hybrid human motion prediction algorithm,optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS)was proposed.The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input,and uses recursive least squares(RLS)to predict the human movement trajectories within the time window.Then,the optimized sliding window polynomial fitting(OSWPF)is used to calculate the multi-step prediction value,and the increment of multi-step prediction value was appropriately constrained.Experimental results show that compared with the existing benchmark algorithms,the OSWPF-RLS algorithm improved the multi-step prediction accuracy of human motion and enhanced the ability to respond to different human movements.

关 键 词：human-robot collaboration(HRC) human motion prediction sliding window polynomial fitting(SWPF)algorithm recursive least squares(RLS) optimized sliding window polynomial fitting and recursive least squares(OSWPF-RLS) 

分 类 号：R319[医药卫生—基础医学]