Robust foot clearance estimation based on the integration of foot-mounted IMU acceleration data

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject's foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15 % under the various walking conditions

Robust Compensation of Electromechanical Delay during Neuromuscular Electrical Stimulation of Antagonistic Muscles

Neuromuscular electrical stimulation (NMES) can potentially be used to restore the limb function in persons with neurological disorders, such as spinal cord injury (SCI), stroke, etc. Researches on control system design has so far focused on relatively simple unidirectional NMES applications requiring stimulation of single muscle group. However, for some advanced tasks such as pedaling or walking, stimulation of multiple muscles is required. For example, to extend as well as flex a limb joint requires electrical stimulation of an antagonistic muscle pair. This is due to the fact that muscles are unidirectional actuators. The control challenge is to allocate control inputs to antagonist muscles based on the system output, usually a limb angle error to achieve a smooth and precise transition between antagonistic muscles without causing discontinuities. Furthermore, NMES input to each muscle is delayed by an electromechanical delay (EMD), which arises due to the time lag between the electrical excitation and the force development in muscle. And EMD is known to cause instability or performance loss during closed-loop control of NMES. In this thesis, a robust delay compensation controller for EMDs in antagonistic muscles is presented. A Lyapunov stability analysis yields uniformly ultimately bounded tracking for a human limb joint actuated by antagonistic muscles. The simulation results indicate that the controller is robust and effective in switching between antagonistic muscles and compensating EMDs during a simulated NMES task. Further experiments on a dual motor testbed shows its feasibility as an NMES controller for human antagonistic muscles

Nonlinear model predictive control of joint ankle by electrical stimulation for drop foot correction

International audienceIn this paper we investigate the use of optimal control techniques to improve Functional Electrical Stimulation (FES) for drop foot correction on hemiplegic patients. A model of the foot and the tibialis anterior muscle, the contraction of which is controlled by electrical stimulation has been established and is used in the optimal control problem. The novelty in this work is the use of the ankle accelerations and shank orientations (so-called external states) in the model, which have been measured on hemiplegic patients in a previous experiment using Inertial Measurement Units (IMUs). The optimal control problem minimizes the square of muscle excitations which serves the overall goal of reducing energy consumption in the muscle. In a first step, an offline optimal control problem is solved for test purposes and shows the efficiency of the FES optimal control for drop foot correction. In a second step, a Nonlinear Model Predictive Control (NMPC) problem - or online optimal control problem, is solved in a simulated environment. While the ulitmate goal is to use NMPC on the real system, i.e. directly on the patient, this test in simulation was meant to show the feasibility of NMPC for online drop foot correction. In the optimization problem, a set of fixed constraints of foot orientation was applied. Then, an original adaptive constraint taking into account the current ankle height, was introduced and tested. Comparisons between results under fixed and adaptive constraints highlight the advantage of the adaptive constraints in terms of energy consumption, where its quadratic sum of controls, obtained by NMPC, was three times lower than with the fixed constraint. This feasibility study was a first step in application of NMPC on real hemiplegic patients for online FES-based drop foot correction. The adaptive constraints method presents a new and efficient approach in terms of muscular energy consumption minimization

簡易的パラメータ推定を用いたモデル予測制御に基づく閉ループ型機能的電気刺激制御器の計算機シミュレーション研究

Tohoku University渡邉高志課

Control Methods for Compensation and Inhibition of Muscle Fatigue in Neuroprosthetic Devices

For individuals that suffer from paraplegia activities of daily life are greatly inhibited. With over 5,000 new cases of paraplegia each year in the United States alone there is a clear need to develop technologies to restore lower extremity function to these individuals. One method that has shown promise for restoring functional movement to paralyzed limbs is the use of functional electrical stimulation (FES), which is the application of electrical stimulation to produce a muscle contraction and create a functional movement. This technique has been shown to be able to restore numerous motor functions in persons with disability; however, the application of the electrical stimulation can cause rapid muscle fatigue, limiting the duration that these devices may be used. As an alternative some research has developed fully actuated orthoses to restore motor function via electric motors. These devices have been shown to be capable of achieving greater walking durations than FES systems; however, these systems can be significantly larger and heavier. To develop smaller and more efficient systems some research has explored hybrid neuroprostheses that use both FES and electric motors. However, these hybrid systems present new research challenges. In this dissertation novel control methods to compensate/inhibit muscle fatigue in neuroprosthetic and hybrid neuroprosthetic devices are developed. Some of these methods seek to compensate for the effects of fatigue by using fatigue dynamics in the control development or by minimizing the amount of stimulation used to produce a desired movement. Other control methods presented here seek to inhibit the effects of muscle fatigue by adding an electric motor as additional actuation. These control methods use either switching or cooperative control of FES and an electric motor to achieve longer durations of use than systems that strictly use FES. Finally, the necessity for the continued study of hybrid gait restoration systems is facilitated through simulations of walking with a hybrid neuroprosthesis. The results of these simulations demonstrate the potential for hybrid neuroprosthesis gait restoration devices to be more efficient and achieve greater walking durations than systems that use strictly FES or strictly electric motors