Artificially induced joint movement control with musculoskeletal model-integrated iterative learning algorithm

Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review

Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.

Control strategy for intraspinal microstimulation based on central pattern generator

Intraspinal microstimulation (ISMS) is considered as a special functional electrical stimulation (FES) method. This method can restore the movement of paralyzed limbs in patients with spinal cord injury (SCI) using electrical stimulation of spinal cord. There is a special site for central pattern generator (CPG) in the spinal cord. The ISMS acts on the CPG site, and single electrode stimulation produces alternating motion in the hindlimbs of SCI rats. Based on the long short-term memory network (LSTM), a mapping model was established between the stimulation intensity of specific CPG sites and the angle of the knee joint to reflect the motor characteristics of the rat hindlimb. We proposed an LSTM-iterative learning control (ILC) strategy to form a closed-loop control to accurately control hindlimb movement. The proposed LSTM model fits the actual joint angle curve well, and the LSTM-ILC strategy can accurately regulate the hindlimb movement, allowing rats to perform rehabilitation training based on pre-set knee trajectories.