A denoising algorithm for surface EMG decomposition

The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs

Experimental Investigations of EMG-Torque Modeling for the Human Upper Limb

The electrical activity of skeletal muscle—the electromyogram (EMG)—is of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications, the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system (as evidenced in the surface EMG) and peripheral joints and muscles. This thesis work concentrated on experimental investigations of EMG-torque modeling. My contributions include: 1) continuing to evaluate the advantage of advanced EMG amplitude estimators, 2) studying system identification techniques (regularizing the least squares fit and increasing training data duration) to improve EMG-torque model performance, and 3) investigating the influence of joint angle on EMG-torque modeling. Results show that the advanced EMG amplitude estimator reduced the model error by 21%—71% compared to conventional estimators. Use of the regularized least squares fit with 52 seconds of training data reduced the model error by 20% compared to the least squares fit without regulation when using 26 seconds of training data. It is also demonstrated that the influence of joint angle can be modeled as a multiplicative factor in slowly force-varying and force-varying contractions at various, fixed angles. The performance of the models that account for the joint angle are not statistically different from a model that was trained at each angle separately and thus does not interpolate across angles. The EMG-torque models that account for joint angle and utilize advanced EMG amplitude estimation and system identification techniques achieved an error of 4.06±1.2% MVCF90 (i.e., error referenced to maximum voluntary contraction at 90° flexion), while models without using these advanced techniques and only accounting for a joint angle of 90° generated an error of 19.15±11.2% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) EMG-force modeling at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (2) Investigation of EMG bandwidth requirements for whitening for real-time applications of EMG whitening techniques [with WPI colleagues]. (3) Investigation of the ability of surface EMG to estimate joint torque at future times [with WPI colleagues]. (4) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]

Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue

In a broad view, fatigue is used to indicate a degree of weariness. On a muscular level, fatigue posits the reduced capacity of muscle fibres to produce force, even in the presence of motor neuron excitation via either spinal mechanisms or electric pulses applied externally. Prior to decreased force, when sustaining physically demanding tasks, alterations in the muscle electrical properties take place. These alterations, termed myoelectric manifestation of fatigue, can be assessed non-invasively with a pair of surface electrodes positioned appropriately on the target muscle; traditional approach. A relatively more recent approach consists of the use of multiple electrodes. This multi-channel approach provides access to a set of physiologically relevant variables on the global muscle level or on the level of single motor units, opening new fronts for the study of muscle fatigue; it allows for: (i) a more precise quantification of the propagation velocity, a physiological variable of marked interest to the study of fatigue; (ii) the assessment of regional, myoelectric manifestations of fatigue; (iii) the analysis of single motor units, with the possibility to obtain information about motor unit control and fibre membrane changes. This review provides a methodological account on the multi-channel approach for the study of myoelectric manifestation of fatigue and on the experimental conditions to which it applies, as well as examples of their current applications

Studies of the relationship between the surface electromyogram, joint torque and impedance

This compendium-format dissertation (i.e., comprised mostly of published and in-process articles) primarily reports on system identification methods that relate the surface electromyogram (EMG)—the electrical activity of skeletal muscles—to mechanical kinetics. The methods focus on activities of the elbow and hand-wrist. The relationship between the surface EMG and joint impedance was initially studied. My work provided a complete second-order EMG-based impedance characterization of stiffness, viscosity and inertia over a complete range of nominal torques, from a single perturbation trial with slowly varied torque. A single perturbation trial provides a more convenient method for impedance evaluation. The RMS errors of the EMG-based method were 20.01% for stiffness and 7.05% for viscosity, compared with the traditional mechanical measurement. Three projects studied the relationship between EMG and force/torque, a topic that has been studied for a number of years. Optimal models use whitened EMG amplitude, combining multiple EMG channels and a polynomial equation to describe this relationship. First, we used three techniques to improve current models at the elbow joint. Three more features were extracted from the EMG (waveform length, slope sign change rate and zero crossing rate), in addition to EMG amplitude. Each EMG channel was used separately, compared to previous studies which combined multiple channels from biceps and, separately, from triceps muscles. Finally, an exponential power law model was used. Each of these improvement techniques showed better performance (P\u3c0.05 and ~0.7 percent maximum voluntary contraction (%MVC) error reduction from a nominal error of 5.5%MVC) than the current “optimal” model. However, the combination of pairs of these techniques did not further improve results. Second, traditional prostheses only control 1 degree of freedom (DoF) at a time. My work provided evidence for the feasibility of controlling 2-DoF wrist movements simultaneously, with a minimum number of electrodes. Results suggested that as few as four conventional electrodes, optimally located about the forearm, could provide 2-DoF simultaneous, independent and proportional control with error ranging from 9.0–10.4 %MVC, which is similar to the 1-DoF approach (error from 8.8–9.8 %MVC) currently used for commercial prosthesis control. The third project was similar to the second, except that this project studied controlling a 1-DoF wrist with one hand DoF simultaneously. It also demonstrated good performance with the error ranging from 7.8-8.7 %MVC, compared with 1-DoF control. Additionally, I participated in two team projects—EMG decomposition and static wrist EMG to torque—which are described herein