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

doi:10.19682/j.cnki.1005-8885.2021.0009

中国邮电高校学报(英文) ›› 2021, Vol. 28 ›› Issue (3): 76-85.doi: 10.19682/j.cnki.1005-8885.2021.0009

• Artificial Intelligence • 上一篇下一篇

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

李庆华¹,张钊²,冯超²,沐雅琪²,尤越²,李研强²

1. 齐鲁工业大学（山东省科学院）;济南市人机智能协同工程实验室
2. 齐鲁工业大学（山东省科学院）

收稿日期:2020-07-13 修回日期:2020-11-23 出版日期:2021-06-30 发布日期:2021-06-22
通讯作者: 李研强 E-mail:lisim51@126.com
基金资助:
国家自然科学基金;齐鲁工业大学（山东省科学院）青年博士合作基金项目

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

Received:2020-07-13 Revised:2020-11-23 Online:2021-06-30 Published:2021-06-22
Supported by:
National Natural Science Foundation of China;Young Doctor Cooperation Foundation of Qilu University of Technology (Shandong Academy of Sciences)

摘要/Abstract

摘要：

Human motion prediction is a critical issue in human-robot collaboration (HRC) tasks. In order to reduce thelocal 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)

Abstract:

step prediction accuracy of human motion and enhanced the ability to respond to different human movements.

Key words:

中图分类号:

TP18

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

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

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