Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
IEEE Trans Biomed Eng. 2011 Oct;58(10):2867-75. doi: 10.1109/TBME.2011.2161671. Epub 2011 Jul 14.
In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.
在这项研究中，我们开发了一种基于神经肌肉融合的算法，以连续识别各种由股骨截肢（TF）患者执行的运动模式。从臀肌和残肢大腿肌肉记录的肌电图（EMG）信号以及从假肢支柱测量的地面反作用力/力矩被用作相位相关模式分类器的输入，以进行连续运动模式识别。该算法使用来自五名 TF 截肢患者的数据进行了评估。结果表明，神经肌肉融合优于仅使用 EMG 信号或机械信息的方法。对于一种行走模式（即静止状态）的连续性能，基于神经肌肉融合和支持向量机（SVM）算法的接口在站立阶段产生了 99%或更高的准确性，在摆动阶段产生了 95%的准确性，用于运动模式识别。在模式转换期间，基于融合的 SVM 方法以足够的预测时间正确识别了所有转换。这些有希望的结果表明，基于神经肌肉融合的连续运动模式分类器具有神经控制假肢的潜力。
