Tomislav Bazina, T. Schnurrer-Luke-Vrbanić, N. Črnjarić-Žic, S. Zelenika, E. Kamenar
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The implementation of the model, based on the median male bones dimensions, is made available in the open-source Robot Operating System (ROS) framework. Results: By including additional four inclination angles per finger, the devised kinematic hand model encompasses also finger curvatures, resulting in significant positioning accuracy improvements compared to the conventional model. The used 3D spatial position improvement metrics are the mean absolute (MAE) and mean relative errors (MRE). The dependent joint position MAEs range from 0.22 to 0.34 cm, while MREs range from 2.8 and 3.5 %, whereas the highest absolute and relative errors during fingertip positioning can reach 0.5 cm and 10.5 %, respectively. Conclusion: The performed investigation allowed establishing that by modelling finger curvature and assuring the adaptability of the model to a variety of human hands and rehabilitation modalities through joint dependency, represents the best approach towards a relatively simple and applicable rehabilitation model with functional human-like hand movements.","PeriodicalId":39071,"journal":{"name":"Medicina Fluminensis","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hand model with dependency constrained joints for applications in rehabilitation robotics\",\"authors\":\"Tomislav Bazina, T. Schnurrer-Luke-Vrbanić, N. Črnjarić-Žic, S. Zelenika, E. Kamenar\",\"doi\":\"10.21860/medflum2022_284696\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim: This work presents a method for developing a simplified but efficient model of the complex human hand kinematics with the aim of its implementation in rehabilitation robotics. Material and methods: The approach incorporates modularity by simplifying the available model comprising 24 degrees of freedom (DOFs) to 9 DOFs, with the introduction of additional joint coupling parameters specific to different grasp types. The effect of dependent joints to the ranges-of-motion (ROMs) of the model is investigated and compared to the anatomical one. The index, middle, ring and little finger solutions to forward and inverse kinematics problems are then acquired. The implementation of the model, based on the median male bones dimensions, is made available in the open-source Robot Operating System (ROS) framework. Results: By including additional four inclination angles per finger, the devised kinematic hand model encompasses also finger curvatures, resulting in significant positioning accuracy improvements compared to the conventional model. The used 3D spatial position improvement metrics are the mean absolute (MAE) and mean relative errors (MRE). The dependent joint position MAEs range from 0.22 to 0.34 cm, while MREs range from 2.8 and 3.5 %, whereas the highest absolute and relative errors during fingertip positioning can reach 0.5 cm and 10.5 %, respectively. 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引用次数: 0
摘要
目的:本工作提出了一种方法来开发一个简单而有效的复杂人手运动学模型,目的是在康复机器人中实现。材料和方法:该方法通过将由24个自由度(dof)组成的可用模型简化为9个自由度,并引入针对不同抓握类型的附加关节耦合参数,从而实现模块化。研究了依赖关节对模型运动范围(ROMs)的影响,并与解剖模型进行了比较。然后获得了正解和逆解问题的食指、中指、无名指和小指解。该模型的实现基于男性骨骼的中位数尺寸,可在开源机器人操作系统(ROS)框架中使用。结果:通过包括每个手指额外的四个倾角,设计的运动学手部模型也包括手指曲率,与传统模型相比,定位精度显着提高。使用的三维空间位置改进度量是平均绝对误差(MAE)和平均相对误差(MRE)。依赖关节位置的MAEs范围为0.22 ~ 0.34 cm, MREs范围为2.8%和3.5%,而指尖定位的绝对误差和相对误差最高分别为0.5 cm和10.5%。结论:通过对手指曲率进行建模,并通过关节依赖确保模型对各种人手和康复方式的适应性,是构建具有类似人手功能运动的相对简单和适用的康复模型的最佳途径。
Hand model with dependency constrained joints for applications in rehabilitation robotics
Aim: This work presents a method for developing a simplified but efficient model of the complex human hand kinematics with the aim of its implementation in rehabilitation robotics. Material and methods: The approach incorporates modularity by simplifying the available model comprising 24 degrees of freedom (DOFs) to 9 DOFs, with the introduction of additional joint coupling parameters specific to different grasp types. The effect of dependent joints to the ranges-of-motion (ROMs) of the model is investigated and compared to the anatomical one. The index, middle, ring and little finger solutions to forward and inverse kinematics problems are then acquired. The implementation of the model, based on the median male bones dimensions, is made available in the open-source Robot Operating System (ROS) framework. Results: By including additional four inclination angles per finger, the devised kinematic hand model encompasses also finger curvatures, resulting in significant positioning accuracy improvements compared to the conventional model. The used 3D spatial position improvement metrics are the mean absolute (MAE) and mean relative errors (MRE). The dependent joint position MAEs range from 0.22 to 0.34 cm, while MREs range from 2.8 and 3.5 %, whereas the highest absolute and relative errors during fingertip positioning can reach 0.5 cm and 10.5 %, respectively. Conclusion: The performed investigation allowed establishing that by modelling finger curvature and assuring the adaptability of the model to a variety of human hands and rehabilitation modalities through joint dependency, represents the best approach towards a relatively simple and applicable rehabilitation model with functional human-like hand movements.