The purpose of this investigation was to assess whether mental practice facilitates the development of voluntary control over the recruitment of the abductor hallucis muscle to produce isolated big toe abduction. A sample of convenience of 15 women and 20 men with a mean age of 28.8 yr. (SD=5.7) and healthy feet, who were unable voluntarily to abduct the big toe, were randomly assigned to one of three groups, a mental practice group, a physical practice group, and a group who performed a control movement during practice. Each subject received neuromuscular electrical stimulation to introduce the desired movement prior to each of five practice bouts over a single session lasting 2 hr. Big toe abduction active range of motion and surface electromyographic (EMG) output of the abductor hallucis and extensor digitorum brevis muscles were measured prior to the first practice bout and following each practice bout, yielding seven acquisition trials. Acquisition is defined as an improvement in both active range of motion and in the difference between the integrated EMG of the abductor hallucis and extensor digitorum brevis muscles during successive acquisition trials. Seven members of both the mental and physical practice groups and one member of the control group met the acquisition criteria. Chi-square analysis indicated the group difference was statistically significant, suggesting mental practice was effective for this task.
Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.
Precise and effective prosthetic control is important for its applicability. Two desired objectives of the prosthetic control are finger position and force control. Variation in skeletal muscle force results in corresponding change of surface electromyographic (sEMG) signals. sEMG signals generated by skeletal muscles are temporal and spatially distributed that result in cross talk between adjacent sEMG signal sensors. To address this issue, an array of nine sEMG sensors is used with a force sensing resistor to capture muscle dynamics in terms of sEMG and skeletal muscle force. sEMG and skeletal muscle force are filtered with a nonlinear Teager-Kaiser Energy (TKE) operator based nonlinear spatial filter and Chebyshev type-II filter respectively. Multiple Takagi-Sugeno-Kang Adaptive Neuro Fuzzy Inference Systems (ANFIS) are obtained using sEMG as input and skeletal muscle force as output. Outputs of these ANFIS systems are fitted with smoothing spline curve fitting. To achieve better estimate of the skeletal muscle force, an adaptive probabilistic Kullback Information Criterion (KIC) for model selection based data fusion algorithm is applied to the smoothing spline curve fitting outputs. Final fusion based output of this approach results in improved skeletal muscle force estimates.
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