NeuroEngineering And Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Italy; Physical Medicine and Rehabilitation Unit, Scientific Institute of Lissone, Institute of Care and Research, Salvatore Maugeri Foundation IRCCS, Lissone, Italy.
NeuroEngineering And Medical Robotics Laboratory, NearLab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Italy.
J Electromyogr Kinesiol. 2014 Apr;24(2):307-17. doi: 10.1016/j.jelekin.2014.01.006. Epub 2014 Jan 31.
This work aimed at designing a myocontrolled arm neuroprosthesis for both assistive and rehabilitative purposes. The performance of an adaptive linear prediction filter and a high-pass filter to estimate the volitional EMG was evaluated on healthy subjects (N=10) and neurological patients (N=8) during dynamic hybrid biceps contractions. A significant effect of filter (p=0.017 for healthy; p<0.001 for patients) was obtained. The post hoc analysis revealed that for both groups only the adaptive filter was able to reliably detect the presence of a small volitional contribution. An on/off non-linear controller integrated with an exoskeleton for weight support was developed. The controller allowed the patient to activate/deactivate the stimulation intensity based on the residual EMG estimated by the adaptive filter. Two healthy subjects and 3 people with Spinal Cord Injury were asked to flex the elbow while tracking a trapezoidal target with and without myocontrolled-NMES support. Both healthy subjects and patients easily understood how to use the controller in a single session. Two patients reduced their tracking error by more than 60% with NMES support, while the last patient obtained a tracking error always comparable to the healthy subjects performance (<4°). This study proposes a reliable and feasible solution to combine NMES with voluntary effort.
本研究旨在设计一种用于辅助和康复目的的肌控手臂神经假体。在健康受试者（N=10）和神经疾病患者（N=8）进行动态混合二头肌收缩期间，评估了自适应线性预测滤波器和高通滤波器对意愿性肌电图的估计性能。两种滤波器（健康受试者：p=0.017；患者：p<0.001）均有显著效果。事后分析表明，对于两组受试者，只有自适应滤波器能够可靠地检测到较小的自愿性贡献。此外，还开发了一种带有非线性控制器的外骨骼，用于进行体重支撑。该控制器允许患者根据自适应滤波器估计的剩余肌电图来激活/停用刺激强度。两名健康受试者和 3 名脊髓损伤患者被要求在跟踪梯形目标时弯曲肘部，同时有和没有肌控-NMES 支持。两名健康受试者和患者在单次会议中都轻松理解了如何使用控制器。两名患者在 NMES 支持下将跟踪误差降低了 60%以上，而最后一名患者的跟踪误差始终与健康受试者的表现相当（<4°）。这项研究提出了一种可靠且可行的解决方案，将 NMES 与自愿性努力相结合。
