Skavhaug Ida-Maria, Lyons Kenneth R, Nemchuk Anna, Muroff Shira D, Joshi Sanjay S
Dept. of Mechanical and Aerospace Eng., University of California, Davis 1 Shields Avenue, Davis, CA 95616, United States.
Dept. of Psychology, University of California, Davis 1 Shields Avenue, Davis, CA 95616, United States.
Hum Mov Sci. 2016 Jun;47:60-69. doi: 10.1016/j.humov.2015.12.003. Epub 2016 Feb 12.
New human-computer interfaces that use bioelectrical signals as input are allowing study of the flexibility of the human neuromuscular system. We have developed a myoelectric human-computer interface which enables users to navigate a cursor to targets through manipulations of partial powers within a single surface electromyography (sEMG) signal. Users obtain two-dimensional control through simultaneous adjustments of powers in two frequency bands within the sEMG spectrum, creating power profiles corresponding to cursor positions. It is unlikely that these types of bioelectrical manipulations are required during routine muscle contractions. Here, we formally establish the neuromuscular ability to voluntarily modulate single-site sEMG power profiles in a group of naïve subjects under restricted and controlled conditions using a wrist muscle. All subjects used the same pre-selected frequency bands for control and underwent the same training, allowing a description of the average learning progress throughout eight sessions. We show that subjects steadily increased target hit rates from 48% to 71% and exhibited greater control of the cursor's trajectories following practice. Our results point towards an adaptable neuromuscular skill, which may allow humans to utilize single muscle sites as limited general-purpose signal generators. Ultimately, the goal is to translate this neuromuscular ability to practical interfaces for the disabled by using a spared muscle to control external machines.
以生物电信号作为输入的新型人机界面,使得对人类神经肌肉系统的灵活性研究成为可能。我们开发了一种肌电人机界面,用户可通过操控单个表面肌电图(sEMG)信号中的部分功率,将光标移动到目标位置。用户通过同时调整sEMG频谱中两个频段的功率来实现二维控制,从而创建与光标位置相对应的功率分布图。在常规肌肉收缩过程中,不太可能需要进行这类生物电操控。在此,我们正式确定了在受限且可控的条件下,一组未经过训练的受试者利用腕部肌肉自主调节单部位sEMG功率分布图的神经肌肉能力。所有受试者都使用相同的预先选定频段进行控制,并接受相同的训练,从而能够描述整个八次训练过程中的平均学习进展情况。我们发现,受试者的目标命中率从48%稳步提高到71%,并且在练习后对光标的轨迹有了更好的控制。我们的研究结果表明存在一种可适应的神经肌肉技能,这可能使人类能够将单个肌肉部位用作有限的通用信号发生器。最终目标是通过利用一块备用肌肉来控制外部机器,将这种神经肌肉能力转化为适用于残疾人的实用界面。