Electromyogram-based neural network control of transhumeral prostheses.
Electromyogram-based neural network control of transhumeral prostheses.
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DOI：
10.1682/jrrd.2010.12.0237
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发表时间：
2011
影响因子：
--
通讯作者：
Kirsch RF
中科院分区：
文献类型：
--
作者：
Pulliam CL;Lambrecht JM;Kirsch RF
上肢截肢会给患者带来很大的功能障碍，特别是对于肘部或肘部以上截肢的患者。我们的长期目标是通过将完全植入的肌电图 (EMG) 记录系统与与患者假肢通信的无线遥测系统相集成，改善截肢患者的功能结果。我们相信，这应该会产生一种方案，使患者能够同时稳健地控制多个自由度。本研究的目的是评估基于来自经肱骨截肢患者可能完好的一组肌肉的肌电图信号来预测动态手臂运动（屈曲/伸展和旋前/旋后）的可行性。我们记录了不同复杂程度的各种运动期间七块肌肉的运动运动学和肌电图信号。然后，对延时人工神经网络进行离线训练，以根据从测量的肌电图信号中提取的特征来预测测量的手臂轨迹。我们评估了各种肌肉子集的相对有效性。预测的运动轨迹的平均均方根误差约为 15.7° 和 24.9°，肘部屈曲/伸展和前臂旋前/旋后的平均 R2 值分别约为 0.81 和 0.46。
Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG) recording system with a wireless telemetry system that communicates with the patient’s prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination) based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.
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DOI：
10.1109/tnsre.2008.922681
发表时间：
2008-06-01
影响因子：
4.9
作者：
Hincapie, Juan Gabriel;Blana, Dimitra;Kirsch, Robert F.
通讯作者：
Kirsch, Robert F.
DOI：
10.1115/1.3086356
发表时间：
2009-08-01
影响因子：
1.7
作者：
Dutta, Anirban;Kobetic, Rudi;Triolo, Ronald J.
通讯作者：
Triolo, Ronald J.
影响因子：
1.5
作者：
Biddiss, Elaine A.;Chau, Tom T.
通讯作者：
Chau, Tom T.
DOI：
10.1109/tbme.2008.2005942
发表时间：
2009-01
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Weir RF;Troyk PR;DeMichele GA;Kerns DA;Schorsch JF;Maas H
通讯作者：
Maas H
影响因子：
4.6
作者：
Englehart, K;Hudgins, B;Parker, PA
通讯作者：
Parker, PA