Functional Electrical Stimulation for Gait Rehabilitation

Abstract

Conditions that can lead to a full or partial motor function loss, such as stroke or multiple sclerosis, leave people with disabilities that may interfere severely with lower body movements, such as gait. Drop Foot (DF) is a gait disorder that results in a reduced ability or total inability to contract the Tibialis Anterior (TA) muscle, causing an inability to raise the foot during gait. One of the most effective methods to correct DF is Functional Electrical Stimulation (FES). FES is a technique used to reproduce the activation patterns of functional muscles, in order to create muscular contractions through electrical stimulation of the muscle's nervous tissue.FES has first been introduced in 1961. However, the available commercial FES systems still do not take into account the fact that the gait differs from subject to subject, depending on their physical condition, muscular fatigue and rehabilitation stage. Therefore, they are unable to provide a personalized assistance to the user, delivering constant stimulation pulses that are only based on gait events. Consequently, they promote the early onset of fatigue and generate coarse movements. This dissertation aims to tackle the aforementioned issues by developing a FES system for personalized DF correction, tailored to each individual user's needs through the use of a Neural Network (NN).A Non-Linear Autoregressive Neural Network with Exogenous inputs (NARX Neural Network) was used to model the dynamics of the electrically stimulated TA muscle, in a novel approach that uses both the foot angle and the foot velocity. The model was combined with a Proportional Derivative controller to help compensate for any external disturbances. In order to create more natural movements, reference trajectories were obtained by recording the foot angle and velocity of healthy subjects walking at different speeds.The system has been validated with a healthy subject walking at 3 different speeds on a treadmill: 1 km/h, 1.5 km/h and 2 km/h. It was able

Functional Electrical Stimulation System for Drop Foot Correction Using a Dynamic NARX Neural Network

“…FES systems are meant to be small-sized and practical to actively restore motor function in daily living and allow the execution of everyday-tasks that otherwise would not be completed. In this sense, we proposed a wearable system for DF correction, extending our previous work [29]. As demonstrated in Figure 1, it is composed by the STM NUCLEO-32F303K8 ® processing unit to execute the control strategy and gait event detection algorithm; a modular stimulation unit from the ISTim Modular Stimulation System [28] to deliver the stimulation pulses to the TA muscle according to the desired trajectory; and, the MPU6050 wearable Inertial Measurement Unit (IMU) that acquires the foot kinematics in real-time.…”

Multi-Channel FES Gait Rehabilitation Assistance System Based on Adaptive sEMG Modulation

“…Previous studies have used the EMG signals to predict a gait pattern employing different prediction models, such as BP neural network (BPNN) and support vector machine (SVR) [14], [15], [16]. Although the achieved results are acceptable, human gait is a periodic movement, so the EMG data have time-series characteristics.…”