Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, IL, USA; Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL, USA.
Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China.
Med Eng Phys. 2014 Jun;36(6):754-60. doi: 10.1016/j.medengphy.2014.01.005. Epub 2014 Feb 11.
High density surface electromyogram (sEMG) recording and pattern recognition techniques have demonstrated that substantial motor control information can be extracted from neurologically impaired muscles. In this study, a series of pattern recognition parameters were investigated in classification of 20 different movements involving the affected limb of 12 chronic stroke subjects. The experimental results showed that classification performance could be improved with spatial filtering and be maintained with a limited number of electrodes. It was also found that appropriate adjustment of analysis window length, sampling rate, and high-pass cut-off frequency in sEMG conditioning and processing would be potentially useful in reducing computational cost and meanwhile ensuring classification performance. The quantitative analyses are useful for practical myoelectric control toward improved stroke rehabilitation.
高密度表面肌电图（sEMG）记录和模式识别技术已经证明，大量的运动控制信息可以从神经功能障碍的肌肉中提取出来。在这项研究中，我们研究了一系列模式识别参数，以对 12 名慢性中风患者受影响肢体的 20 种不同运动进行分类。实验结果表明，通过空间滤波可以提高分类性能，同时通过使用有限数量的电极也可以保持分类性能。还发现，适当调整 sEMG 调理和处理中的分析窗口长度、采样率和高通截止频率，可能有助于降低计算成本，同时确保分类性能。这些定量分析对于提高中风康复的实用肌电控制是有用的。
