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Biomechanics And Motor Control Of Human Movement

Formation and control of optimal trajectory in human multijoint arm movement : minimum torque-change model

Robotic and functional electrical stimulation (FES) approaches are used for rehabilitation of walking impairment of spinal cord injured individuals. Although devices are commercially available, there are still issues that remain to be solved. Control of hybrid exoskeletons aims at blending robotic exoskeletons and electrical stimulation to overcome the drawbacks of each approach while preserving their advantages. Hybrid actuation and control have a considerable potential for walking rehabilitation but there is a need of novel control strategies of hybrid systems that adequately manage the balance between FES and robotic controllers. Combination of FES and robotic control is a challenging issue, due to the non-linear behavior of muscle under stimulation and the lack of developments in the field of hybrid control. In this article, a cooperative control strategy of a hybrid exoskeleton is presented. This strategy is designed to overcome the main disadvantages of muscular stimulation: electromechanical delay and change in muscle performance over time, and to balance muscular and robotic actuation during walking. Experimental results in healthy subjects show the ability of the hybrid FES-robot cooperative control to balance power contribution between exoskeleton and muscle stimulation. The robotic exoskeleton decreases assistance while adequate knee kinematics are guaranteed. A new technique to monitor muscle performance is employed, which allows to estimate muscle fatigue and implement muscle fatigue management strategies. Kinesis is therefore the first ambulatory hybrid exoskeleton that can effectively balance robotic and FES actuation during walking. This represents a new opportunity to implement new rehabilitation interventions to induce locomotor activity in patients with paraplegia. Acronym list: 10mWT: ten meters walking test; 6MWT: six minutes walking test; FSM: finite-state machine; t-FSM: time-domain FSM; c-FSM: cycle-domain FSM; FES: functional electrical stimulation; HKAFO: hip-knee-ankle-foot orthosis; ILC: iterative error-based learning control; MFE: muscle fatigue estimator; NILC: Normalized stimulation output from ILC controller; PID: Proportional-Integral-derivative Control; PW: Stimulation pulse width; QUEST: Quebec User Evaluation of Satisfaction with assistive Technology; SCI: Spinal cord injury; TTI: torque-time integral; VAS: Visual Analog Scale.

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Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton.

We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since the parameters are changing over time, a successful estimator needs to perform well for small samples. We propose a kernel normalized-least-squares (kernel-NLS) estimator which has a closed form, and thus outperforms the previously proposed kernel quasi-maximum likelihood (kernel-QML) estimator for small samples. The kernel-NLS estimator is simple, works under mild moment assumptions and avoids some of the parameter space restrictions imposed by the kernel-QML estimator. Theoretical evidence shows that the kernel-NLS estimator has the same rate of convergence as the kernel-QML estimator. Due to the kernel-NLS estimator’s ease of computation, computationally intensive procedures can be used. A prediction-based cross-validation method is proposed for selecting the bandwidth of the kernel-NLS estimator. Also, we use a residual-based bootstrap scheme to bootstrap the tvARCH process. The bootstrap sample is used to obtain pointwise confidence intervals for the kernel-NLS estimator. It is shown that distributions of the estimator using the bootstrap and the “true” tvARCH estimator asymptotically coincide. We illustrate our estimation method on a variety of currency exchange and stock index data for which we obtain both good fits to the data and accurate forecasts.

Normalized least-squares estimation in time-varying ARCH models

In a closed-loop stepping motor system the rotor position is detected and fed back to the control unit. Each step command is issued only when the motor has responded satisfactorily to the previous command and so there is no possibility of the motor losing synchronism. A schematic closed-loop control is shown. Initially the system is stationary with one or more phases excited. The target position is loaded into the downcounter and a pulsed START signal is applied to the control unit, which imme diately passes a step command to the phase sequence generator. Consequently there is a change in excitation and the motor starts to accelerate at a rate dictated by the load parameters.

Closed-loop control

FES induced movement control is a significantly challenging area due to complexity and non-linearity of musculo-skeletal system. The goal of this study is to design a cycle-to-cycle control of FES-induced swinging motion. In this approach only the quadriceps muscle is stimulated by controlling the amount of stimulation pulsewidth. This time dependent behaviour is successfully compensated for using a cycle-to cycle fuzzy controller, which computes the amount of knee extension stimulation on the basis of the achieved flexion angle in previous cycles. The capability of fuzzy control in automatic generation of stimulation burst duration is assessed in computer simulations using a musculo-skeletal model. This paper presents the development of a fuzzy logic control scheme based on discrete-time cycle to cycle control strategies without predefined trajectory. The results show the effectiveness of the approach in controlling FES-induced swinging motion

Fuzzy logic based cycle-to-cycle control of FES-induced swinging motion

Modelling of joint properties of lower limbs in people with spinal cord injury is significantly challenging for researchers due to the complexity of the system. The objective of this study is to develop a knee joint model capable of relating electrical parameters to dynamic joint torque as well as knee angle for functional electrical stimulation application. The joint model consists of a segmental dynamic, time-invariant passive properties and uncertain time-variant active properties. The knee joint model structure comprising optimised equations of motion and fuzzy models to represent the passive viscoelasticity and active muscle properties is formulated. The model thus formulated is optimised using genetic optimization, and validated against experimental data. The developed model can be used for simulation of joint movements as well as for control development. The results show that the model developed gives an accurate dynamic characterisation of the knee joint.

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Fuzzy modelling of knee joint with genetic optimization

Functional Electrical Stimulation requires an accurate
model of electrically stimulated muscles to control the muscle
contraction force. Characterization of electrically stimulated muscle
is complex because of the non-linearity and time-varying nature of
the system with interdependent variables. The muscle model consists
of relatively well known time-invariant passive properties and
uncertain time-variant active properties. In this research a new
approach for estimating nonlinear active properties of the electrically
stimulated quadriceps muscle group is investigated. The objective of
this study is to develop a model that could be used to describe active
joint properties including continuous-time nonlinear activation
dynamics and nonlinear static contraction. As an example, the
modelling of a freely swinging lower leg by electrical
stimulation of the quadriceps is considered.

Identification of active properties of knee joint using GA optimization

Spring brake orthotic swing phase for paraplegic gait is initiated through releasing the brake on the knee mounted with a torsion spring. The stored potential energy in the spring, gained from the previous swing phase, is solely responsible for swing phase knee flexion. Hence the later part of the SBO operation, functional electrical stimulation FES assisted extension movement of the knee has to serve an additional purpose of restoring the spring potential energy on the fly. While control of FES induced movement as such is often a challenging task, a torsion spring, being antagonistically paired up with the muscle actuator, as in spring brake orthosis SBO, only adds to the challenge. Two new schemes are proposed for the control of FES induced knee extension movement in SBO assisted swing phase. Even though the control schemes are closed-loop in nature, special attention is paid to accommodate the natural dynamics of the mechanical combination being controlled the leg segment as a major role playing feature. The schemes are thus found to be immune from some drawbacks associated with both closed-loop tracking as well as open-loop control of FES induced movement. A leg model including the FES knee joint model of the knee extensor muscle vasti along with the passive properties is used in the simulation. The optimized parameters for the SBO spring are obtained from the earlier part of this work. Genetic algorithm GA and multi-objective GA MOGA are used to optimize the parameters associated with the control schemes with minimum fatigue as one of the control objectives. The control schemes are evaluated in terms of three criteria based on their ability to cope with muscle fatigue.

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Genetic algorithms based approach for designing spring brake orthosis --Part II: Control of FES induced movement

© 2012 Ibrahim et al, licensee InTech This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommonsorg/licenses/by/30), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited An Approach for Dynamic Characterisation of Passive Viscoelasticity and Estimation of Anthropometric Inertia Parameters of Paraplegic’s Knee Joint

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M. S. Huq

Papers

Fuzzy logic based cycle-to-cycle control of FES-induced swinging motion

Fuzzy modelling of knee joint with genetic optimization

Identification of active properties of knee joint using GA optimization

Genetic algorithms based approach for designing spring brake orthosis --Part II: Control of FES induced movement

An Approach for Dynamic Characterisation of Passive Viscoelasticity and Estimation of Anthropometric Inertia Parameters of Paraplegic’s Knee Joint