Computer-aided generation of stimulation data and model identification for functional electrical stimulation (FES) control of lower extremities.
Computer-aided generation of stimulation data and model identification for functional electrical stimulation (FES) control of lower extremities.
复制标题
计算机辅助生成刺激数据和模型识别，用于下肢功能性电刺激 (FES) 控制。
DOI：
10.1163/15685570052062693
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Y. Handa
中科院分区：
文献类型：
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作者：
G. Eom;T. Watanabe;R. Futami;N. Hoshimiy;Y. Handa
通过与模型模拟相结合的动态优化，生成截瘫患者无辅助站立的标准刺激数据，以克服现有基于肌电图（EMG）的方法的困难。生成的刺激数据与正常受试者的肌电图大致一致。由此看来，“基于模型”的方法可以作为“基于肌电图的方法”的替代方法。一旦正确识别了患者的肌肉骨骼系统，相同的技术就可以应用于生成患者特定的刺激数据。肌肉骨骼系统必须根据简单且非侵入性实验获得的数据进行识别，以使识别方法实际上可以接受。为此，我们开发了肌肉骨骼模型和系统识别协议。它们针对膝关节处的股外侧肌进行了验证。识别成功，预测的关节角度轨迹与实验数据非常吻合。这意味着基于模型生成患者特定刺激数据是可能的。
Standard stimulation data for unassisted standing up of paraplegic patients was generated by dynamic optimization linked with model simulation, to overcome the difficulties in the present electromyogram (EMG)-based method. The generated stimulation data were roughly in agreement with the normal subjects' EMG. From these, it is suggested that the 'model-based' method is useful as an alternative of the 'EMG-based method'. The same technique can be applied to generation of patient-specific stimulation data once the musculoskeletal system of a patient is properly identified. The musculoskeletal system must be identified from data taken from simple and noninvasive experiments for the identification method to be practically acceptable. We developed a musculoskeletal model and systematic identification protocols for this purpose. They were validated for the vastus lateralis muscle at the knee joint. The identification was successful and the predicted joint angle trajectories closely matched the experimental data. This implies that the model-based generation of patient-specific stimulation data is possible.