Montgomery Andrew E, Allen John M, Elbasiouny Sherif M
Department of Biomedical, Industrial and Human Factors Engineering, College of Engineering and Computer Science, Wright State University, Dayton, OH, United States.
Department of Neuroscience, Cell Biology and Physiology, Boonshoft School of Medicine and College of Science and Mathematics, Wright State University, Dayton, OH, United States.
Front Neurosci. 2021 Apr 8;15:590775. doi: 10.3389/fnins.2021.590775. eCollection 2021.
The overarching goal was to resolve a major barrier to real-life prosthesis usability-the rapid degradation of prosthesis control systems, which require frequent recalibrations. Specifically, we sought to develop and test a motor decoder that provides (1) highly accurate, real-time movement response, and (2) unprecedented adaptability to dynamic changes in the amputee's biological state, thereby supporting long-term integrity of control performance with few recalibrations. To achieve that, an adaptive motor decoder was designed to auto-switch between algorithms in real-time. The decoder detects the initial aggregate motoneuron spiking activity from the motor pool, then engages the optimal parameter settings for decoding the motoneuron spiking activity in that particular state. "Clear-box" testing of decoder performance under varied physiological conditions and post-amputation complications was conducted by comparing the movement output of a simulated prosthetic hand as driven by the decoded signal vs. as driven by the actual signal. Pearson's correlation coefficient and Normalized Root Mean Square Error were used to quantify the accuracy of the decoder's output. Our results show that the decoder algorithm extracted the features of the intended movement and drove the simulated prosthetic hand accurately with real-time performance (<10 ms) (Pearson's correlation coefficient >0.98 to >0.99 and Normalized Root Mean Square Error <13-5%). Further, the decoder robustly decoded the spiking activity of multi-speed inputs, inputs generated from reversed motoneuron recruitment, and inputs reflecting substantial biological heterogeneity of motoneuron properties, also in real-time. As the amputee's neuromodulatory state changes throughout the day and the electrical properties and ratio of slower vs. faster motoneurons shift over time post-amputation, the motor decoder presented here adapts to such changes in real-time and is thus expected to greatly enhance and extend the usability of prostheses.
总体目标是解决现实生活中假肢可用性的一个主要障碍——假肢控制系统的快速退化，这需要频繁重新校准。具体而言，我们试图开发并测试一种运动解码器，该解码器能够提供：（1）高度准确的实时运动响应；（2）对截肢者生物状态动态变化前所未有的适应性，从而在很少重新校准的情况下支持控制性能的长期完整性。为实现这一目标，设计了一种自适应运动解码器，使其能够在算法之间实时自动切换。该解码器检测运动神经元池最初的总体动作电位发放活动，然后采用最优参数设置来解码该特定状态下的运动神经元动作电位发放活动。通过比较由解码信号驱动的模拟假手的运动输出与由实际信号驱动的模拟假手的运动输出，在不同生理条件和截肢后并发症情况下对解码器性能进行了“透明盒”测试。使用皮尔逊相关系数和归一化均方根误差来量化解码器输出的准确性。我们的结果表明，解码器算法提取了预期运动的特征，并以实时性能（<10毫秒）准确驱动模拟假手（皮尔逊相关系数>0.98至>0.99，归一化均方根误差<13 - 5%）。此外，该解码器还能够稳健地实时解码多速度输入、由反向运动神经元募集产生的输入以及反映运动神经元特性显著生物异质性的输入。由于截肢者的神经调节状态在一天中会发生变化，并且较慢与较快运动神经元的电学特性和比例在截肢后会随时间发生变化，本文提出的运动解码器能够实时适应这些变化，因此有望极大地提高和扩展假肢的可用性。
