Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.
Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion.
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DOI：
10.1109/tbme.2011.2161671
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
Englehart KB
中科院分区：
文献类型：
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作者：
Huang H;Zhang F;Hargrove LJ;Dou Z;Rogers DR;Englehart KB
在这项研究中，我们开发了一种基于神经肌肉机械融合的算法，以连续识别经股动脉（TF）截肢患者的各种运动模式。肌电（EMG）信号记录臀肌和剩余大腿肌肉和地面反作用力/力矩测量假肢的假肢被用作输入到一个相位相关的模式分类器连续运动模式识别。使用从5例TF截肢患者收集的数据对该算法进行了评价。结果表明，神经肌肉-机械融合优于仅使用EMG信号或机械信息的方法。对于一种行走模式的连续性能（即，静态），基于神经肌肉-机械融合和支持向量机（SVM）算法的接口在站立阶段产生99%或更高的准确度，在摆动阶段产生95%的准确度，用于运动模式识别。在模式转换期间，基于融合的SVM方法正确地识别出具有足够预测时间的所有转换。这些有希望的结果证明了基于神经肌肉-机械融合的连续运动模式分类器在假肢神经控制方面的潜力。
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.