New control strategies for neuroprosthetic systems

The availability of techniques to artificially excite paralyzed muscles opens enormous potential for restoring both upper and lower extremity movements with\ud neuroprostheses. Neuroprostheses must stimulate muscle, and control and regulate the artificial movements produced. Control methods to accomplish these tasks include feedforward (open-loop), feedback, and adaptive control. Feedforward control requires a great deal of information about the biomechanical behavior of the limb. For the upper extremity, an artificial motor program was developed to provide such movement program input to a neuroprosthesis. In lower extremity control, one group achieved their best results by attempting to meet naturally perceived gait objectives rather than to follow an exact joint angle trajectory. Adaptive feedforward control, as implemented in the cycleto-cycle controller, gave good compensation for the gradual decrease in performance observed with open-loop control. A neural network controller was able to control its system to customize stimulation parameters in order to generate a desired output trajectory in a given individual and to maintain tracking performance in the presence of muscle fatigue. The authors believe that practical FNS control systems must\ud exhibit many of these features of neurophysiological systems

Multifingered robot hand robot operates using teleoperation

The purpose of research on anthropomorphic dextrous manipulation is to develop anthropomorphic dextrous robot hand which approximates the versatility and sensitivity of the human hand by teleoperation methods that will communicate in master– slave manners. Glove operates as master part and multi-fingered hand as slave. The communication medium between operator and multi-fingered hand is via KC-21 Bluetooth wireless modules. Multi-fingered hand developed using 5 volt, 298:1 gear ratio micro metal dc motors which controlled using L293D motor drivers and actuator controlled the movement of robot hand combined with dextrous human ability by PIC18F4520 microcontroller. The slave components of 5 fingers designed with 15 Degree of Freedom (DOF) by 3 DOF for each finger. Fingers design, by modified IGUS 07-16-038-0 enclosed zipper lead E-Chain® Cable Carrier System, used in order to shape mimic as human size. FLEX sensor, bend sensing resistance used for both master and slave part and attached as feedback to the system, in order to control position configuration. Finally, the intelligence, learning and experience aspects of the human can be combined with the strength, endurance and speed of the robot in order to generate proper output of this project

Comparison of filtering methods for real-time extraction of the volitional EMG component in electrically stimulated muscles

Objective Recorded electromyograms (EMG) of electrically stimulated muscles can contain both an exogenous-evoked potential (M-wave) and an endogenous, or volitional, component. This study evaluated the effectiveness of three filtering methods (i.e., high-pass, adaptive, and comb), commonly used in neurorehabilitation, in extracting the volitional component of simulated and experimental EMG during upper-limb tasks. Methods Volitional EMG and M-wave were simulated through a physiological model of muscle recruitment, comprising of a motor neuron pool and associated muscle fibres, superimposed to a stimulation artefact. Experimental EMG data during different levels of volitional muscle contraction in isometric and dynamic tasks were recorded from five unimpaired individuals. Electrical stimulation artefact was removed with different techniques (i.e., none, removing samples, blanking, and interpolation) to assess filter performance across time and frequency domains, and information content (i.e., Kolmogorov-Smirnov D-value). Results The experimental results agreed with the simulations, wherein the adaptive filter outperformed the other filters when using no artefact removal or removing artefact samples from the signal, while for the blanking and interpolation artefact removal methods, the adaptive and comb filters outperformed the high-pass filter. Conclusion The adaptive and comb filters best estimated volitional muscle activity in electrically stimulated muscles. Significance Results from this study will enable the enhanced design of real-time neuroprosthesis control.This study was supported by the Motor Accident Insurance Commission, Queensland, Australia (BioSpine project) and by a Griffith University Postgraduate Research Scholarship. The authors would like to thank Prof David Lloyd for the stimulating discussions that allowed us to refine our study design and analyses.Peer ReviewedPostprint (published version

Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI). A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment

Tahap kepuasan bekerja dan motivasi kerja di kalangan pekerja industri pelancongan

Kajian ini berkaitan dengan tahap Kepuasan Bekeija dan Motivasi di kalangan pekeija di dalam Industri Pelancongan yang berbentuk kajian deskriptif. Teori Kepuasan Bekeija dan Teori Motivasi Herzberg telah digunakan sebagai asas kepada kajian ini. Dua agensi telah diambil sebagai tempat kajian. Sampel telah diambil dari keseluruhan populasi untuk pengumpulan data. Borang soal selidik digunakan sebagai instrumen untuk pengumpulan data. Kajian ini menggunakan perisian Microsoft Excel untuk analisis data. Dapatan kajian menunjukkan skor min kepuasan bekeija berada di tahap sederhana (min = 2.97). Melalui kajian yang dijalankan pengkaji mendapati keadaan penyelenggaraan memperolehi tahap kepuasan yang rendah. Oleh itu beberapa cadangan telah diusulkan untuk memperbaiki tahap yang lemah ini

Nonlinear Model-Based Control for Neuromuscular Electrical Stimulation

Neuromuscular electrical stimulation (NMES) is a technology where skeletal muscles are externally stimulated by electrodes to help restore functionality to human limbs with motor neuron disorder. This dissertation is concerned with the model-based feedback control of the NMES quadriceps muscle group-knee joint dynamics. A class of nonlinear controllers is presented based on various levels of model structures and uncertainties. The two main control techniques used throughout this work are backstepping control and Lyapunov stability theory. In the first control strategy, we design a model-based nonlinear control law for the system with the exactly known passive mechanical that ensures asymptotical tracking. This first design is used as a stepping stone for the other control strategies in which we consider that uncertainties exist. In the next four control strategies, techniques for adaptive control of nonlinearly parameterized systems are applied to handle the unknown physical constant parameters that appear nonlinearly in the model. By exploiting the Lipschitzian nature or the concavity/convexity of the nonlinearly parameterized functions in the model, we design two adaptive controllers and two robust adaptive controllers that ensure practical tracking. The next set of controllers are based on a NMES model that includes the uncertain muscle contractile mechanics. In this case, neural network-based controllers are designed to deal with this uncertainty. We consider here voltage inputs without and with saturation. For the latter, the Nussbaum gain is applied to handle the input saturation. The last two control strategies are based on a more refined NMES model that accounts for the muscle activation dynamics. The main challenge here is that the activation state is unmeasurable. In the first design, we design a model-based observer that directly estimates the unmeasured state for a certain activation model. The second design introduces a nonlinear filter with an adaptive control law to handle parametric uncertainty in the activation dynamics. Both the observer- and filter-based, partial-state feedback controllers ensure asymptotical tracking. Throughout this dissertation, the performance of the proposed control schemes are illustrated via computer simulations