Huang He, Kuiken Todd A, Lipschutz Robert D
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
IEEE Trans Biomed Eng. 2009 Jan;56(1):65-73. doi: 10.1109/TBME.2008.2003293.
This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% +/- 5.0%, 6.0% +/- 4.7%, 7.5% +/- 5.1%, and 5.2% +/- 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
本研究探讨了使用表面肌电图（EMG）结合模式识别（PR）来识别用户的运动模式。由于运动过程中腿部EMG信号具有非平稳特性，因此提出了一种新的基于相位的EMG PR策略来对用户的运动模式进行分类。研究了系统变量以实现准确分类和及时的系统响应。所开发的PR系统在八名健全受试者和两名经大腿（TF）截肢的受试者行走在不同地形或路径时采集的EMG数据上进行了测试。结果表明对七种测试模式的分类可靠。对于八名健全受试者，使用位于膝盖上方肌肉上的十个电极（模拟TF截肢者残肢的EMG）在四个定义阶段的平均分类误差分别为12.4%±5.0%、6.0%±4.7%、7.5%±5.1%和5.2%±3.7%。在我们对TF截肢受试者的初步研究中也观察到了类似结果。本研究结果可推动神经控制假腿的未来设计。
