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Simulating cardiac signals on 3D human models for photoplethysmography development 在三维人体模型上模拟心脏信号以开发光电式血压计
Pub Date : 2024-01-10 DOI: 10.3389/frobt.2023.1266535
Danyi Wang, J. Chahl
Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge.Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos.Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans.Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.
简介基于图像的心率估计技术为医疗保健监测提供了一种非接触式方法,可改善数百万人的生活。为了全面测试或优化基于图像的心率提取方法,数据集应包含大量因素,如身体运动、光照条件和生理状态等。然而,收集具有完整参数的高质量数据集是一项巨大的挑战:本文介绍了一种基于人体三维(3D)表示的仿生人体模型。通过将合成心电信号和人体非自主运动整合到三维模型中,使用五种著名的传统方法和四种深度学习 iPPG(成像心动图)提取方法对渲染视频进行测试:为了与现实世界中的不同情况进行比较,在每个三维人体上创建了四种常见场景(静止、表情/说话、光源变化和身体活动)。三维人体可以有任何外观和不同的肤色。从合成人和真人视频中提取的信号具有很高的一致性--所选 iPPG 方法的性能优缺点在真人和三维人身上都是一致的:这项技术能够利用精确控制的参数和干扰,在各种场景下生成合成人。此外,它还具有相当大的潜力,可用于测试和优化基于图像的生命体征方法,以应对难以获得可靠地面实况测量结果的挑战性情况,例如无人机救援。
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引用次数: 0
Sound masking by a low-pitch speech-shaped noise improves a social robot’s talk in noisy environments 低音调语音状噪声的声音掩蔽改善了社交机器人在嘈杂环境中的谈话效果
Pub Date : 2024-01-09 DOI: 10.3389/frobt.2023.1205209
Hamed Pourfannan, Hamed Mahzoon, Y. Yoshikawa, Hiroshi Ishiguro
Introduction: There has been a surge in the use of social robots for providing information, persuasion, and entertainment in noisy public spaces in recent years. Considering the well-documented negative effect of noise on human cognition, masking sounds have been introduced. Masking sounds work, in principle, by making the intrusive background speeches less intelligible, and hence, less distracting. However, this reduced distraction comes with the cost of increasing annoyance and reduced cognitive performance in the users of masking sounds.Methods: In a previous study, it was shown that reducing the fundamental frequency of the speech-shaped noise as a masking sound significantly contributes to its being less annoying and more efficient. In this study, the effectiveness of the proposed masking sound was tested on the performance of subjects listening to a lecture given by a social robot in a noisy cocktail party environment.Results: The results indicate that the presence of the masking sound significantly increased speech comprehension, perceived understandability, acoustic satisfaction, and sound privacy of the individuals listening to the robot in an adverse listening condition.Discussion: To the knowledge of the authors, no previous work has investigated the application of sound masking technology in human-robot interaction designs. The future directions of this trend are discussed.
简介近年来,在嘈杂的公共场所使用社交机器人提供信息、劝说和娱乐的情况激增。考虑到噪音对人类认知的负面影响,人们引入了掩蔽声音。从原理上讲,掩蔽声音的作用是降低干扰性背景声音的可懂度,从而减少干扰。然而,减少干扰的代价是增加烦扰和降低掩蔽声音使用者的认知能力:方法:先前的一项研究表明,降低作为掩蔽声音的言语状噪音的基频可显著降低其烦扰性并提高其效率。在这项研究中,对在嘈杂的鸡尾酒会环境中聆听社交机器人演讲的受试者的表现进行了测试,检验了所提议的掩蔽声音的有效性:结果表明,在不利的听力条件下,掩蔽声音的存在明显提高了听讲者的语音理解能力、感知可理解性、声音满意度和声音隐私性:据作者所知,以前还没有研究过声音掩蔽技术在人机交互设计中的应用。本文讨论了这一趋势的未来发展方向。
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引用次数: 0
Using skeletal position to estimate human error rates in telemanipulator operators 利用骨骼位置估算遥控机械手操作员的人为错误率
Pub Date : 2024-01-09 DOI: 10.3389/frobt.2023.1287417
Thomas Piercy, Guido Herrmann, Angelo Cangelosi, Ioannis Dimitrios Zoulias, Erwin Lopez
In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator’s skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.
在当前的远程机器人和远程机械手应用中,操作员必须执行各种各样的任务,而这些任务往往具有很高的失败风险。利用观测到的操作员特征生成基于数据的行为估计的系统,可用于降低工业远程操作中的风险。本文介绍了一种非侵入式远程操作员生物机械特征捕捉方法,用于试用新型人为错误率估算器,在未来的工作中,该估算器将通过向操作员提供行为和姿势反馈来提高操作安全性。操作员监控研究是在英国原子能机构 RACE 使用 MASCOT 远程操作系统在现场进行的;在观察过程中,操作员要完成受控任务。在现有的车辆驾驶员意图估计和机器人手术操作员分析工作的基础上,我们使用市售的深度摄像头采集三维点云数据,以估计操作员的骨骼姿势。我们对总共 14 名操作员进行了长达约 8 小时的观察和记录,每个人都完成了一项基线任务和一项旨在诱发可检测但安全的碰撞的任务。我们对骨骼姿势进行了估计,记录了碰撞统计数据,并进行了基于问卷的心理评估,从而提供了一个定性和定量数据的数据库。然后,我们尝试使用统计和机器学习回归技术(SVR)进行数据驱动分析,以估算碰撞率。我们还进一步对所选特征进行了输入变量敏感性分析,并将分析结果提交给大家。
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引用次数: 0
Editorial: Language, affordance and physics in robot cognition and intelligent systems 社论:机器人认知和智能系统中的语言、承受力和物理学
Pub Date : 2024-01-09 DOI: 10.3389/frobt.2023.1355576
Nutan Chen, Walterio W. Mayol-Cuevas, Maximilian Karl, Elie Aljalbout, Andy Zeng, Aurelio Cortese, Wolfram Burgard, Herke van Hoof
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引用次数: 0
Comparison of strength profile representations using musculoskeletal models and their applications in robotics 比较使用肌肉骨骼模型的力量曲线表示法及其在机器人技术中的应用
Pub Date : 2024-01-09 DOI: 10.3389/frobt.2023.1265635
Sheila Sutjipto, Marc G. Carmichael, Gavin Paul
Musculoskeletal models provide an approach towards simulating the ability of the human body in a variety of human-robot applications. A promising use for musculoskeletal models is to model the physical capabilities of the human body, for example, estimating the strength at the hand. Several methods of modelling and representing human strength with musculoskeletal models have been used in ergonomic analysis, human-robot interaction and robotic assistance. However, it is currently unclear which methods best suit modelling and representing limb strength. This paper compares existing methods for calculating and representing the strength of the upper limb using musculoskeletal models. It then details the differences and relative advantages of the existing methods, enabling the discussion on the appropriateness of each method for particular applications.
肌肉骨骼模型为模拟人体在各种人机应用中的能力提供了一种方法。肌肉骨骼模型的一个有前途的用途是模拟人体的体能,例如,估算手部力量。在人体工程学分析、人机交互和机器人辅助方面,已经使用了几种用肌肉骨骼模型建模和表示人体力量的方法。然而,目前还不清楚哪种方法最适合模拟和表示肢体力量。本文比较了使用肌肉骨骼模型计算和表示上肢力量的现有方法。然后详细介绍了现有方法的差异和相对优势,以便讨论每种方法是否适合特定应用。
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引用次数: 0
Model-free control for autonomous prevention of adverse events in robotics 机器人自主预防不良事件的无模型控制
Pub Date : 2024-01-05 DOI: 10.3389/frobt.2023.1271748
Meenakshi Narayan, Ann Majewicz Fey
Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ.Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances.Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.
引言预防性控制是自主技术中确保系统安全运行的关键功能。安全性最重要的应用之一是机器人辅助针介入。在对组织进行切口时,如果遇到坚硬的薄膜,可能会发生针轴机械弯曲和组织位移等不良事件,从而对器官造成潜在损害:为了防患于未然,我们提出了一个新的控制子程序,该子程序可自主选择:a)当预测到针屈曲或严重组织位移事件时,停止插入程序的反应机制;b)当检测到轻微组织位移时,通过针转向控制继续插入程序的自适应机制。由于未知针-组织动力学的非线性,该子程序采用无模型控制技术开发。首先,通过分析计算动态线性化方程中的快速时变偏假导数,开发了改进版的无模型自适应控制(IMFAC),以提高输出收敛性和对外部干扰的鲁棒性:在 MATLAB 中模拟的非线性系统上比较 IMFAC 和 MFAC 算法,IMFAC 在任意干扰下的输出收敛速度提高了 20%。接下来,IMFAC 与先前工作中的事件预测算法相结合,实时防止针插入过程中的不良事件。在已知环境的明胶组织中插针,成功防止了针弯曲和组织位移事件。在未知环境的生物组织中插针时,使用实时透视成像作为地面实况,以验证能否及时防止不良事件的发生。最后,对所有插入数据进行统计方差分析,显示了预防算法对各种针头和组织环境的稳健性。总体而言,通过自适应和反应式控制防止插针不良事件的成功率为 95%,这对实现机器人插针干预的安全性非常重要。
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引用次数: 0
Event-triggered robot self-assessment to aid in autonomy adjustment 事件触发的机器人自我评估有助于自主调整
Pub Date : 2024-01-04 DOI: 10.3389/frobt.2023.1294533
Nicholas Conlon, Nisar Ahmed, D. Szafir
Introduction: Human–robot teams are being called upon to accomplish increasingly complex tasks. During execution, the robot may operate at different levels of autonomy (LOAs), ranging from full robotic autonomy to full human control. For any number of reasons, such as changes in the robot’s surroundings due to the complexities of operating in dynamic and uncertain environments, degradation and damage to the robot platform, or changes in tasking, adjusting the LOA during operations may be necessary to achieve desired mission outcomes. Thus, a critical challenge is understanding when and how the autonomy should be adjusted.Methods: We frame this problem with respect to the robot’s capabilities and limitations, known as robot competency. With this framing, a robot could be granted a level of autonomy in line with its ability to operate with a high degree of competence. First, we propose a Model Quality Assessment metric, which indicates how (un)expected an autonomous robot’s observations are compared to its model predictions. Next, we present an Event-Triggered Generalized Outcome Assessment (ET-GOA) algorithm that uses changes in the Model Quality Assessment above a threshold to selectively execute and report a high-level assessment of the robot’s competency. We validated the Model Quality Assessment metric and the ET-GOA algorithm in both simulated and live robot navigation scenarios.Results: Our experiments found that the Model Quality Assessment was able to respond to unexpected observations. Additionally, our validation of the full ET-GOA algorithm explored how the computational cost and accuracy of the algorithm was impacted across several Model Quality triggering thresholds and with differing amounts of state perturbations.Discussion: Our experimental results combined with a human-in-the-loop demonstration show that Event-Triggered Generalized Outcome Assessment algorithm can facilitate informed autonomy-adjustment decisions based on a robot’s task competency.
前言人类-机器人团队需要完成越来越复杂的任务。在执行过程中,机器人可能以不同的自主级别(LOA)运行,从完全机器人自主到完全人类控制。由于各种原因,如机器人在动态和不确定环境中工作的复杂性导致周围环境的变化、机器人平台的退化和损坏,或任务分配的变化等,为了实现预期的任务成果,可能有必要在操作过程中调整 LOA。因此,一个关键的挑战是了解何时以及如何调整自主性:我们根据机器人的能力和局限性(即机器人的胜任能力)来确定这一问题的框架。有了这个框架,就可以根据机器人的操作能力赋予其一定程度的自主权。首先,我们提出了模型质量评估指标,该指标显示了自主机器人的观察结果与其模型预测结果相比的(非)预期程度。接下来,我们提出了一种事件触发的广义结果评估(ET-GOA)算法,该算法利用模型质量评估中超过阈值的变化,有选择地执行并报告对机器人能力的高级评估。我们在模拟和实时机器人导航场景中验证了模型质量评估指标和 ET-GOA 算法:我们的实验发现,模型质量评估能够对意想不到的观察结果做出反应。此外,我们对完整的 ET-GOA 算法进行了验证,探索了在不同的模型质量触发阈值和不同的状态扰动量下,该算法的计算成本和准确性会受到怎样的影响:我们的实验结果结合人在回路中的演示表明,事件触发的通用结果评估算法可以根据机器人的任务能力促进做出明智的自主调整决策。
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引用次数: 0
Versatile vacuum-powered artificial muscles through replaceable external reinforcements 通过可更换的外部加固装置实现多功能真空供电人工肌肉
Pub Date : 2024-01-04 DOI: 10.3389/frobt.2023.1289074
Mijaíl Jaén Mendoza, Sergio Cancán, Steve Surichaqui, Esteban Centeno, Ricardo Vilchez, Katia Bertoldi, E. Vela
Soft pneumatic artificial muscles are a well actuation scheme in soft robotics due to its key features for robotic machines being safe, lightweight, and conformable. In this work, we present a versatile vacuum-powered artificial muscle (VPAM) with manually tunable output motion. We developed an artificial muscle that consists of a stack of air chambers that can use replaceable external reinforcements. Different modes of operation are achieved by assembling different reinforcements that constrain the output motion of the actuator during actuation. We designed replaceable external reinforcements to produce single motions such as twisting, bending, shearing and rotary. We then conducted a deformation and lifting force characterization for these motions. We demonstrated sophisticated motions and reusability of the artificial muscle in two soft machines with different modes of locomotion. Our results show that our VPAM is reusable and versatile producing a variety and sophisticated output motions if needed. This key feature specially benefits unpredicted workspaces that require a soft actuator that can be adjusted for other tasks. Our scheme has the potential to offer new strategies for locomotion in machines for underwater or terrestrial operation, and wearable devices with different modes of operation.
软气动人工肌肉是软机器人技术中一种很好的驱动方案,其主要特点是安全、轻便、可适应机器人机器。在这项工作中,我们提出了一种具有手动可调输出运动的多功能真空供电人工肌肉(VPAM)。我们开发的人工肌肉由一叠气室组成,可以使用可更换的外部加固件。通过组装不同的加固件,可实现不同的工作模式,从而在驱动过程中限制致动器的输出运动。我们设计的可更换外部加固件可产生扭转、弯曲、剪切和旋转等单一运动。然后,我们对这些运动进行了变形和提升力鉴定。我们在两台具有不同运动模式的软机器上演示了人工肌肉的复杂运动和可重复使用性。我们的结果表明,我们的 VPAM 可重复使用且用途广泛,可根据需要产生各种复杂的输出运动。这一关键特性特别有利于那些需要可调整用于其他任务的软执行器的未预测工作空间。我们的方案有望为水下或陆地操作机器的运动以及具有不同操作模式的可穿戴设备提供新的策略。
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引用次数: 0
Reactive optimal motion planning for a class of holonomic planar agents using reinforcement learning with provable guarantees 利用可证明保证的强化学习,对一类整体平面代理进行反应式优化运动规划
Pub Date : 2024-01-03 DOI: 10.3389/frobt.2023.1255696
Panagiotis Rousseas, C. Bechlioulis, K. Kyriakopoulos
In control theory, reactive methods have been widely celebrated owing to their success in providing robust, provably convergent solutions to control problems. Even though such methods have long been formulated for motion planning, optimality has largely been left untreated through reactive means, with the community focusing on discrete/graph-based solutions. Although the latter exhibit certain advantages (completeness, complicated state-spaces), the recent rise in Reinforcement Learning (RL), provides novel ways to address the limitations of reactive methods. The goal of this paper is to treat the reactive optimal motion planning problem through an RL framework. A policy iteration RL scheme is formulated in a consistent manner with the control-theoretic results, thus utilizing the advantages of each approach in a complementary way; RL is employed to construct the optimal input without necessitating the solution of a hard, non-linear partial differential equation. Conversely, safety, convergence and policy improvement are guaranteed through control theoretic arguments. The proposed method is validated in simulated synthetic workspaces, and compared against reactive methods as well as a PRM and an RRT⋆ approach. The proposed method outperforms or closely matches the latter methods, indicating the near global optimality of the former, while providing a solution for planning from anywhere within the workspace to the goal position.
在控制理论中,反应式方法成功地为控制问题提供了稳健、可证明收敛的解决方案,因而广受赞誉。尽管这类方法很早就被用于运动规划,但最优性问题在很大程度上仍未通过反应式方法得到解决,业界主要关注基于离散/图的解决方案。虽然后者具有一定的优势(完整性、复杂的状态空间),但最近兴起的强化学习(RL)为解决反应式方法的局限性提供了新的途径。本文的目标是通过 RL 框架来处理反应式最优运动规划问题。本文以与控制理论结果一致的方式制定了策略迭代 RL 方案,从而以互补的方式利用了每种方法的优势;采用 RL 构建最优输入,无需求解困难的非线性偏微分方程。相反,通过控制理论的论证,安全性、收敛性和策略改进都得到了保证。提出的方法在模拟合成工作空间中得到了验证,并与反应式方法以及 PRM 和 RRT⋆ 方法进行了比较。提出的方法优于或接近后者,表明前者接近全局最优,同时提供了从工作区内任何位置到目标位置的规划解决方案。
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引用次数: 0
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Frontiers in Robotics and AI
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