Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
IEEE Trans Neural Syst Rehabil Eng. 2013 Jan;21(1):104-11. doi: 10.1109/TNSRE.2012.2218286. Epub 2012 Sep 27.
It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.
可以使用机电假体来替代截肢的肢体，但是它们的运行需要检测到使用者的意图，并将其转换为对执行器的控制信号。幸运的是，控制截肢肌肉的运动神经元（MNs）仍然完好无损，并且能够产生电信号，但这些信号很难被记录下来。即使是最新的微电极阵列技术和有针对性的运动神经再支配，也只能对构成每个肌肉运动池的数百个运动单位中的少数（2-10 个）进行稀疏采样。对这些记录进行简单的整流和积分，很可能会产生用户实际意图的嘈杂和延迟估计。我们已经开发了一种基于少数（2-10）个可分辨运动单位的募集和放电率的电机池激励的最优估计的新算法。我们首先根据先前发表的单个 MN 募集和异步频率调制模型，从调制 MN 活动的正常模式中推导出电机估计算法。然后，该算法在使用肌内细电线记录获得单个运动单位的目标运动神经再支配受试者上进行了验证。
