Han, Sungmin Chu, Jun-Uk Kim, Hyungmin Park, Jong Woong Youn, Inchan 2024-01-20T02:01:38Z 2024-01-20T02:01:38Z 2021-09-01 2017-03-09 2045-2322 https://pubs.kist.re.kr/handle/201004/122954 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 singleshank 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. English NATURE PUBLISHING GROUP FUNCTIONAL ELECTRICAL-STIMULATION MOVEMENT FEEDBACK NEURONS RESTORATION ENSEMBLES Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings Article 10.1038/srep44197 1 SCIENTIFIC REPORTS, v.7 SCIENTIFIC REPORTS 7 scie scopus 000395975500001 2-s2.0-85014970925 Multidisciplinary Sciences Science & Technology - Other Topics Article FUNCTIONAL ELECTRICAL-STIMULATION MOVEMENT FEEDBACK NEURONS RESTORATION ENSEMBLES dynamic system neural decoding dorsal root ganglion multiunit activity proprioception afferent neural network