Toward design of an environment-aware adaptive locomotion-mode-recognition system.
Toward design of an environment-aware adaptive locomotion-mode-recognition system.
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DOI：
10.1109/tbme.2012.2208641
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发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Huang H
中科院分区：
文献类型：
--
作者：
Du L;Zhang F;Liu M;Huang H
在这项研究中，我们的目的是提高性能的运动模式识别系统的神经肌肉机械融合的基础上，通过引入额外的信息，步行环境。基于线性判别分析的分类器首先被设计成基于从残余腿部肌肉记录的肌电信号和从假肢塔架测量的地面反作用力来识别下肢假肢用户的运动模式。9名穿戴被动液压膝关节或动力膝关节假体的经股截肢者参加了这项研究。该方法利用最大熵原理模拟出人体行走的地形信息，并将其建模为先验概率，融入到分类器的判别函数中。当正确的步行地形的先验知识被模拟，每个运动模式的分类精度显着增加，没有任务转换错过。此外，模拟不正确的先验知识并没有显着降低系统性能，表明我们的设计是强大的噪声和不完美的先验信息。此外，这些观察结果与应用的假体类型无关。在这项研究中有前途的结果可能有助于进一步发展的环境感知自适应系统的运动模式识别动力下肢假肢或矫形器。
In this study, we aimed to improve the performance of a locomotion-mode-recognition system based on neuromuscular-mechanical fusion by introducing additional information about the walking environment. Linear-discriminant-analysis-based classifiers were first designed to identify a lower limb prosthesis user’s locomotion mode based on electromyographic signals recorded from residual leg muscles and ground reaction forces measured from the prosthetic pylon. Nine transfemoral amputees who wore a passive hydraulic knee or powered prosthetic knee participated in this study. Information about the walking terrain was simulated and modeled as prior probability based on the principle of maximum entropy and integrated into the discriminant functions of the classifier. When the correct prior knowledge of walking terrain was simulated, the classification accuracy for each locomotion mode significantly increased and no task transitions were missed. In addition, simulated incorrect prior knowledge did not significantly reduce system performance, indicating that our design is robust against noisy and imperfect prior information. Furthermore, these observations were independent of the type of prosthesis applied. The promising results in this study may assist the further development of an environment-aware adaptive system for locomotion-mode recognition for powered lower limb prostheses or orthoses.