Shrirao Nikhil A, Reddy Narender P, Kosuri Durga R
Department of Biomedical Engineering, University of Akron, Akron, OH 44325-0302, USA.
Biomed Eng Online. 2009 Jan 20;8:2. doi: 10.1186/1475-925X-8-2.
In virtual reality (VR) systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.
SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects.
There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 +/- 0.036 for fast speed finger-extension to 0.147 +/- 0.026 for slow speed finger extension, and from 0.098 +/- 0.023 for the fast speed finger flexion to 0.163 +/- 0.054 for slow speed finger flexion.
Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals.
在虚拟现实（VR）系统中，用户的手指和手部位置会被感知并用于控制虚拟环境。使用表面肌电图（SEMG）信号对VR环境进行直接生物控制可能对用户更具协同性且限制更少。本研究的目的是开发一种利用神经网络模型从伸肌的表面肌电图测量中预测手指关节角度的技术。
在受试者以三种速度进行食指屈伸旋转时，获取表面肌电图以及实际关节角度测量值。训练了几个神经网络，以根据从表面肌电图信号中提取的参数预测关节角度。选择最佳的网络组成六个委员会。使用来自新受试者的数据对神经网络委员会进行评估。
在屈伸周期中，测量的表面肌电图信号存在滞后现象。然而，神经网络委员会能够以合理的准确度预测关节角度。均方根误差范围从快速手指伸展时的0.085±0.036到慢速手指伸展时的0.147±0.026，以及从快速手指屈曲时的0.098±0.023到慢速手指屈曲时的0.163±0.054。
尽管在测量的表面肌电图信号中观察到滞后现象，但神经网络委员会能够从表面肌电图信号预测手指关节角度。
