Polo-Hortigüela Cristina, Maximo Miriam, Jara Carlos A, Ramon Jose L, Garcia Gabriel J, Ubeda Andres
Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, 03202 Elche, Spain.
Engineering Research Institute of Elche-I3E, Miguel Hernández University of Elche, 03202 Elche, Spain.
Bioengineering (Basel). 2024 May 9;11(5):473. doi: 10.3390/bioengineering11050473.
In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.
在本文中，我们提出了一种日常生活场景，即使用由不同肌电控制模式操作的虚拟机器人手臂，在厨房中抓取物体并将其存放在特定容器中。本研究的主要目标是证明通过表面肌电图控制虚拟环境的可行性，该环境可用于未来对使用假肢或有上肢运动障碍的人进行训练。我们提出，与复杂的多用途机器学习方法相比，简单的控制算法通常是与假肢和辅助机器人进行交互的更自然、更可靠的方式。此外，我们还讨论了在设置中添加智能以自动辅助抓取活动的优缺点。结果表明，所有参与者在对每种提议的控制模式的执行方面都有相似的看法，并且表现都非常出色。
