Numerous researchers have investigated the relationship between EMG and joint torque. Most of these studies use some conventional filtering (i.e. rectification followed by low pass filtering) to estimate the electromyogram (EMG) amplitude and then relate it to the joint torque. Currently some advanced pre-processing techniques (i.e. signal whitening) are also used to estimate the EMG amplitude and then relate it to joint torque. In this study we apply some pre-processing techniques like DC offset removal, noise filtering followed by rectification and then we calculate the moving average of the EMG signal. Thus we get a linear envelope (muscle activation) of the EMG signal and use that linear envelope to estimate the joint torque. To map the EMG to joint torque we propose a new mathematical model. This model has some unknown adjustable parameters, and the values of these parameters are obtained using nonlinear regression. Five subjects took part in the experiments. They were asked to perform non-fatiguing and variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC), and the resulting elbow joint torque and EMG signals were recorded. This recorded data was entered to the model, to estimate best fit values for the unknown parameters. Once these values of the parameters were obtained they were put into the model and thus joint torque was estimated. Predictions made by our model are well correlated with experimental data in both MVC and SMVC, the correlation coefficient and mean square error obtained for experimental data during MVC are 0.998 and 0.056 Nm respectively. The results of this new model were compared with other existing models and some new models and it was found that our model has greater correlation and least mean square error with experimental data. This model may be helpful in the control systems for recognition systems, robot manipulators, exoskeletons, EMG prosthesis and electric stimulators.

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Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state-space model is developed to estimate joint angular velocities and angles throughout elbow flexion/extension. The state equation of the model combines the Hill-based muscle model with the forward dynamics of joint movement, and expresses the kinematic variables as a function of neural activation levels. Then, EMG features including integral of absolute value and waveform length are extracted, and two quadratic equations which associate the kinematic variables with EMG features are fitted to represent the measurement equation. Based on the proposed model, the joint angular velocities and angles are estimated just using the EMG signals with the Extended Kalman Filter (EKF), and the estimation results are used to control a manipulator. The experimental results demonstrate the efficiency of EMG-based motion control with the proposed model.

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Virtual reality (VR) lets people act within and upon computer-generated environments, making it ideal for exposure therapy and some other forms of mental health treatment. In addition to representing stimuli with some degree of realism, a virtual environment (VE) lets users look at and interact with these things much as they would in the real world, using primarily their eyes and hands. This gives users a sense of physical as well as mental control over the things around them in the VE. SpiderWorld is one of a growing number of VEs that psychologists and VR researchers have begun to use to treat phobias and other anxiety disorders. SpiderWorld immerses the patient in a routine environmen-like a home kitchen-and introduces realistic-looking spiders that the patient can observe, manipulate or even squash as part of exposure therapy. Therapists who treat phobic patients often try to reduce anxiety by exposing a patient to the stimuli or situations that provoke the phobic reaction. Generating these as part of a VE promises a new approach to treatment-not to mention sparing some spiders their exoskeletons!

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Robot V has been constructed with inspiration from the death head cockroach, Blaberus discoidalis. Its relative leg segment lengths, joint degrees of freedom, exoskeleton structure, relatively light legs, and location of its center of mass are all similar to those of the cockroach. In an attempt to take further advantage of the neuromechanics of the animal, actuators with muscle-like properties have been employed. The robot’s controller includes biologically inspired gait generation and inverse kinematics components. An actuator tensioning reflex which approximates the function of muscle tone is introduced, and resulting improvements to system response are shown

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Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice.

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