{"title":"Minimizing Interference in Robotic Rehabilitation via Asymmetric Stiffness Force Fields","authors":"Yasuhiro Kato;Sho Sakaino;Toshiaki Tsuji","doi":"10.1109/OJIES.2025.3570789","DOIUrl":null,"url":null,"abstract":"This article introduces a novel approach for guiding human arm movement in the context of robotic rehabilitation. We propose upper limb movement guidance using a force field based on an asymmetric stiffness matrix. By introducing asymmetry in stiffness design, the proposed force field can deflect arm movement toward the target direction of a reaching movement while minimizing impeding effects. We hypothesize that this method can guide a human in the desired direction without interfering with their voluntary movement. To evaluate the performance of the human arm guidance technique, we conducted upper limb reaching experiments using a 2-degree-of-freedom robot arm with ten healthy volunteers. The experimental results revealed that the proposed approach demonstrated a similar reduction in movement error compared to the conventional stiffness approach. Moreover, participants exhibited higher movement activeness, and robotic interference with human movement was lower. The proposed approach may improve movement guidance based on stiffness control by enabling the robot to guide without inhibiting voluntary movement.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"6 ","pages":"840-850"},"PeriodicalIF":4.3000,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006019","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11006019/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This article introduces a novel approach for guiding human arm movement in the context of robotic rehabilitation. We propose upper limb movement guidance using a force field based on an asymmetric stiffness matrix. By introducing asymmetry in stiffness design, the proposed force field can deflect arm movement toward the target direction of a reaching movement while minimizing impeding effects. We hypothesize that this method can guide a human in the desired direction without interfering with their voluntary movement. To evaluate the performance of the human arm guidance technique, we conducted upper limb reaching experiments using a 2-degree-of-freedom robot arm with ten healthy volunteers. The experimental results revealed that the proposed approach demonstrated a similar reduction in movement error compared to the conventional stiffness approach. Moreover, participants exhibited higher movement activeness, and robotic interference with human movement was lower. The proposed approach may improve movement guidance based on stiffness control by enabling the robot to guide without inhibiting voluntary movement.
期刊介绍:
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.