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Stimulation Pattern-Free Control of FES Cycling: Simulation Study
Chul-Seung Kim,Gwang-Moon Eom,Hase, K.,Gon Khang,Gye-Rae Tack,Jeong-Han Yi,Jae-Hoon Jun IEEE 2008 IEEE Transactions on Human-Machine Systems Vol.38 No.1
<P>The aim of this paper is to investigate control strategies for functional electrical simulation (FES) cycling, with particular focus on the generation of stimulation intensities for multiple muscles, without any predetermined stimulation pattern. The control system is developed by imitating the biological neuronal control system. Specifically, the control signal on the level of joint torque (quasi-joint torque) is generated from the feedback information of lower extremities. The quasi-joint torque is then distributed to each muscle and the muscle delay is compensated, and finally, the stimulation intensity is determined. Parameters of the control system are optimized by the genetic algorithm with cost function of energy consumption and cadence error. The proposed control system is evaluated by computer simulation. The controller generates efficient stimulation even during the muscle fatigue process and successfully continues cycling without any predetermined stimulation pattern. Moreover, the controller is robust to the parameter error in the muscle delay compensator and also to the disturbances. It is expected that the proposed method would improve the FES cycling performance and relieve patients by eliminating the experimental determination of the stimulation patterns.</P>
孔世鎭(Se-Jin Kong),嚴光文(Gwang-Moon Eom),金哲承(Chul-Seung Kim),朴寬龍(Kwan-Yong Park) 대한전기학회 2006 전기학회논문지 D Vol.55 No.5
The purpose of this study is to develop a portable gait-event detection system which is necessary for the cycle-to-cycle FES(functional electrical stimulation) control of locomotion. To make the system portable, we made following modifications in the gait signal measurement system. That is, 1) to make the system wireless using Bluetooth communication, 2) to make the system small-sized and battery-powered by using low power consumption μP(ATmega8535L). The gait-events were analyzed in off-line at the main computer using ANN(Artificial Neural Network). The Proposed system showed no mis-detection of the gait-events of normal subject and hemiplegia subjects. The performance of the system was better than the previous wired-system.