Biomedical Research Institute, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02791, Korea.
Department of Biomedical Science, Korea University College of Medicine, 73, Inchan-ro, Seongbuk-gu, Seoul, 02841, Korea.
Sci Rep. 2017 Mar 9;7:44197. doi: 10.1038/srep44197.
Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems.
本体感觉传入活动可用于为功能性电刺激（FES）期间的闭环控制提供感觉反馈信号。然而，大多数先前的研究使用单个神经元的单单元活动从本体感觉传入中提取感觉信息。本研究提出了一种新的解码方法，使用多单元活动数据估计踝关节和膝关节角度。在踝关节和膝关节的被动运动期间，使用单叉微电极从背根神经节记录本体感觉传入信号，并测量运动学数据作为关节角度。从多单元活动数据中提取均方根值（MAV），并使用动态驱动的递归神经网络（DDRNN）估计踝关节和膝关节角度。基于多单元活动的 MAV 特征足以提供信息来估计肢体状态，并且 DDRNN 显示出比传统线性估计器更好的解码性能。此外，处理时间延迟满足实时约束。这些结果表明，所提出的方法可适用于在闭环 FES 系统中提供实时感觉反馈信号。
