{"title":"Human-exoskeleton interaction portrait.","authors":"Mohammad Shushtari, Julia Foellmer, Arash Arami","doi":"10.1186/s12984-024-01447-1","DOIUrl":null,"url":null,"abstract":"<p><p>Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot interaction and co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the interaction portrait (IP), which visualizes this variable's distribution in polar coordinates. We applied IP to compare a recently developed hybrid torque controller (HTC) based on kinematic state feedback and a novel adaptive model-based torque controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals that this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing overall muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduces interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user's interaction strategy formation, showcasing IP analysis's potential in comparing and designing novel controllers to optimize human-robot interaction in wearable robots.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11373187/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of NeuroEngineering and Rehabilitation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12984-024-01447-1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot interaction and co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the interaction portrait (IP), which visualizes this variable's distribution in polar coordinates. We applied IP to compare a recently developed hybrid torque controller (HTC) based on kinematic state feedback and a novel adaptive model-based torque controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals that this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing overall muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduces interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user's interaction strategy formation, showcasing IP analysis's potential in comparing and designing novel controllers to optimize human-robot interaction in wearable robots.
人机物理交互包含优化用户体验、提高机器人性能和客观评估用户适应性的关键信息。本研究引入了一种新方法,通过分析作为二维随机变量的肌肉活动和交互扭矩,评估下肢外骨骼中的人机交互和协同适应。我们引入了交互肖像(IP),它将这一变量在极坐标中的分布可视化。我们应用 IP 比较了最近开发的基于运动状态反馈的混合扭矩控制器(HTC)和本文提出的具有在线学习功能的新型基于模型的自适应扭矩控制器(AMTC)与基于时间的控制器(TBC)在不同速度下的跑步机行走过程。与TBC相比,HTC和AMTC都显著降低了用户的归一化摄氧量,表明用户与骨骼的协调性得到了增强。IP 分析表明,这种改善源于两种不同的共同适应策略,仅靠传统的肌肉活动或交互扭矩分析是无法识别的。HTC 鼓励用户将控制权交给外骨骼,从而减少了整体肌肉活动,但增加了交互扭矩,因为外骨骼会对用户的动态进行补偿。相反,AMTC 则通过增加肌肉运动来促进用户参与,并降低交互扭矩,使其更符合康复和步态训练应用。IP 阶段演化深入揭示了每个用户交互策略的形成,展示了 IP 分析在比较和设计新型控制器以优化可穿戴机器人的人机交互方面的潜力。
期刊介绍:
Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.