BACKGROUND FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons' individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps. METHODS The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands. RESULTS In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4circat the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion. CONCLUSIONS Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.

中文翻译：

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具有学习控制和自动步态评估功能的自适应多通道FES神经假体。

背景技术FES（功能性电刺激）神经假体长期以来一直是中风或脊髓损伤（SCI）患者的康复和步态支持的永久特征。随着时间的流逝，著名的脚踏开关触发了脚下肢神经假体，并扩展到多通道全腿支撑神经假体，从而改善了支撑和康复能力。但是，这些神经假体必须手动调整，无法适应患者的个人需求。在最近的研究中，将学习控制器添加到了下肢神经假体，以便可以通过测量先前步骤的关节角度来适应挥杆阶段的完整刺激模式。方法本研究的目的是开始开发学习型全腿支持神经假体，它在所有步态阶段控制拮抗性肌肉对的屈曲和伸展以及踝关节背屈和plant屈。建立了一种方法，可以连续评估每一步的膝盖和脚关节角度。该方法可以使健康受试者的生理关节角度弯曲以匹配受试者的个体病理步态，因此可以直接比较两者。提出了一种新型的迭代学习控制器（ILC），其独立于个体的步长而工作，并使用生理学关节角度参考带。结果在对四个SCI不完整的人进行的首次测试中，结果表明，所提出的神经假体能够为三位参与者产生各自适合的刺激模式。另一个参与者受到的影响更为严重，由于步态相位检测的错误触发而不得不将其排除在外。对于剩下的三个参与者中的两个，可以观察到平均脚角略有改善，而对于一个参与者，平均膝角则略有改善。这些改善在峰值背屈，峰值足底屈肌或峰值膝盖屈曲时间的4倍范围内。结论可以通过所提出的方法直接适应参与者的当前步态。对患有SCI的人的初步首次测试表明，神经假体可以产生单独的刺激模式。对膝关节角度复位的敏感性，步态剧烈波动的参与者的计时问题，和自动ILC增益调整仍然是需要解决的问题。随后，未来的研究应将此处介绍的神经假体与常规多通道FES神经假体的改善的长期康复效果进行比较。