Functional Electrical Stimulation (FES) Control for Restoration and Rehabilitation of Motor Function

2012 ◽  
pp. 80-93
Author(s):  
Takashi Watanabe ◽  
Naoto Miura
Keyword(s):  
Electrical Stimulation ◽  
Motor Function ◽  
Functional Electrical Stimulation ◽  
Wearable Sensors ◽  
Integrated System ◽  
Rehabilitation Training ◽  
Cord Injury ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Functional Movements

Functional electrical stimulation (FES) has been studied and clinically applied to restoring or assisting motor functions lost due to spinal cord injury or cerebrovascular disease. Electrical stimulation without control of functional movements is also used for therapy or in rehabilitation training. In recent years, one of the main focuses of FES studies has been its application for rehabilitation of motor function. In this review, the authors first present the basics of applying electrical stimulation to the neuromuscular system for motor control. Then, two methods of FES control are discussed: controllers for FES based on feedback error learning (FEL) and on cycle-to-cycle control of limb movements. The FEL-FES controller can be practical in FES applications that need to control the musculoskeletal system that involves various nonlinear characteristics and delay in its responses to electrical stimulation. The cycle-to-cycle control is expected to be effective in controlling repetitive movements for rehabilitation training. Finally, a study on ankle dorsiflexion control during the swing phase using an integrated system of FES control and motion measurement with wearable sensors for rehabilitation is presented.

scholarly journals Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Takashi Watanabe ◽  
Yoshihiro Sugi
Keyword(s):  
Computer Simulation ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Wrist Joint ◽  
Training Data ◽  
Joint Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Feedforward Controller ◽  
Error Learning

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device.

scholarly journals An Approach to Applying Feedback Error Learning for Functional Electrical Stimulation Controller: Computer Simulation Tests of Wrist Joint Control

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Takashi Watanabe ◽  
Keisuke Fukushima
Keyword(s):  
Computer Simulation ◽  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Inverse Dynamics ◽  
Wrist Joint ◽  
Open Loop ◽  
Joint Control ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning

Feedback error-learning (FEL) controller that consists of a proportional-integral-derivative (PID) controller and an artificial neural network (ANN) had applicability to functional electrical stimulation (FES). Because of the integral (reset) windup, however, delay or overshoot sometimes occurred in feedback FES control, which was considered to cause inappropriate ANN learning and to limit the feasibility of the FEL controller for FES to controlling 1-DOF movements stimulating 2 muscles. In this paper, an FEL-FES controller was developed applying antireset windup (ARW) scheme that worked based on total controller output. The FEL-FES controller with the ARW was examined in controlling 2-DOF movements of the wrist joint stimulating 4 muscles through computer simulation. The developed FEL-FES controller was found to realize appropriately inverse dynamics model and to have a possibility of being used as an open-loop controller. The developed controller would be effective in multiple DOF movement control stimulating several muscles.

scholarly journals Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

2016 ◽  
Vol 26 (3) ◽  
Author(s):  
Francisco Resquín ◽  
Jose Gonzalez-Vargas ◽  
Jaime Ibáñez ◽  
Fernando Brunetti ◽  
José Luis Pons
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Control Strategy ◽  
Motor Impairment ◽  
Research Field ◽  
Robotic System ◽  
Feedback Error Learning ◽  
Feedback Error ◽  
Error Learning ◽  
Passive Exoskeleton

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL) control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.

Sign in / Sign up

Export Citation Format

Share Document