Evelyn Morin

Sampling, noise-reduction and amplitude estimation issues in surface electromyography

This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

A myoelectric control evaluation and trainer system

A computer program, which was developed to train and assess child upper limb amputees in the use of myoelectric control, is described. The program permits a user to open and close a graphic hand using myoelectric control and automatically saves assessment results. The program was designed to be entertaining for children and easy to use for therapists. Preliminary testing of the program was done with 15 nonamputee adult volunteers. The results indicate that the program is a useful tool for myoelectric training and assessment. All subjects improved at myoelectric control, the improvement being greater at the beginning of a 10 day training period than at the end of it. The use of the dominant versus nondominant arm for control did not have any effect, and the error most commonly produced was undershooting. >

Feature-based classification of myoelectric signals using artificial neural networks.

A pattern classification system, designed to separate myoelectric signal records based on contraction tasks, is described. The amplitude of the myoelectric signal during the first 200 ms following the onset of a contraction has a non-random structure that is specific to the task performed. This permits the application of advanced pattern recognition techniques to separate these signals. The pattern classification system described consists of a spectrographic preprocessor, a feature extraction stage and a classifier stage. The preprocessor creates a spectrogram by generating a series of power spectral densities over adjacent time segments of the input signal. The feature extraction stage reduces the dimensionality of the spectrogram by identifying features that correspond to subtle underlying structures in the input signal data. This is realised by a self-organising artificial neural network (ANN) that performs an advanced statistical analysis procedure known as exploratory projection pursuit. The extracted features are then classified by a supervised-learning ANN. An evaluation of the system, in terms of system performance and the complexity of the ANNs, is presented.

A suite of objective biomechanical measurement tools for personal load carriage system assessment

For application to military and civilian needs, Defence Research and Development Canada—Toronto contracted Queens University, Kingston to develop a suite of biomechanical assessment and analytical tools to supplement human-based load carriage system assessment methods. This suite of tools permitted efficient objective evaluation of biomechanical aspects of load-bearing webbing, vests, packs and their components, and therefore contributed to early system assessment and a rapid iterative design process. This paper is a summary of five assessment and analytical tools. A dynamic load carriage simulator was developed to simulate cadence of walking, jogging and running. The simulator comprised a computer-controlled pneumatic platform that oscillated anthropometrically weighted mannequins of varying dimensions from which measures of skin contact pressure, hip reaction forces and moments and relative pack-person displacements were taken. A stiffness tester for range of motion provided force-displacement data on pack suspension systems. A biomechanical model was used to determine forces and moments on the shoulders and hips, and validated using a static load distribution mannequin. Subjective perceptual rating systems were used gather soldier feedback during a standardized mobility circuit. Objective outcome measures were validated by means of other objective measures (e.g., Optotrak®, video, Instron®, etc.) and then compared to subjective ratings. This approach led to development of objective performance criteria for load carriage systems and to improvements in load carriage designs that could be used both in the military and in general.

EMG–force modeling using parallel cascade identification

Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation. In this study, parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface EMG recordings from upper-arm muscles to the induced force at the wrist. PCI mapping involves generating a parallel connection of a series of linear dynamic and nonlinear static blocks. The PCI model parameters were initialized to obtain the best force prediction. A comparison between PCI and a previously published Hill-based orthogonalization scheme, that captures physiological behaviour of the muscles, has shown 44% improvement in force prediction by PCI (averaged over all subjects in relative-mean-square sense). The improved performance is attributed to the structural capability of PCI to capture nonlinear dynamic effects in the generated force.

Optimal Electrode Configurations for Finger Movement Classification using EMG

The myoelectric signal has played a major role in the development of prosthesis control technology. A myoelectric classification system has the ability to determine a prosthesis users intent based solely on his or her muscle activity, thereby allowing for more intuitive prosthetic control. Much work has been done on the recognition of upper arm and gross hand movement tasks, but it was not until accuracy levels approached 100% [3] that more attention was given to specific finger movements. In this study, the effect of electrode array size and arrangement on classification accuracy is investigated for a four-finger typing task. This follows from previous work [1] in which the classification system itself was optimized. Unique advantages were found using array sizes of three and seven electrodes; classification accuracy of 92.7±3.9% was found in the latter case across twelve subjects.

Myoelectric signal characteristics from muscles in residual upper limbs

Myoelectric signal (MES) data were obtained from remnant muscles in residual upper limbs and analogous intact muscles of thirty-two upper-limb deficient subjects. Spectral parameters (mean frequency, median frequency, and equivalent statistical bandwidth) of the MES were calculated and examined for significant differences between the remnant muscle data and intact muscle data. Other factors were examined for possible significant effect on the spectral content of the MES. Although no pattern of spectral difference between the MES of residual versus intact limb muscles was found, spectral differences were apparent by visual inspection in most cases. Level of amputation (above elbow or below elbow) was the sole factor found to have a significant effect on the MES spectral content. Data were separated based on level of amputation; no statistically significant difference between the spectral parameters of residual versus intact muscle MES was found. However, greater variation in the spectral parameters of the remnant muscle data was observed. The results of this study add to ones knowledge of the MES characteristics of remnant muscles, with implications for the design of myoelectric controllers. >

Use of the Fast Orthogonal Search Method to Estimate Optimal Joint Angle For Upper Limb Hill-Muscle Models

An important aspect of accurate representation of human movement is the ability to account for differences between individuals. The following paper proposes a methodology using Hill-based candidate functions in the fast orthogonal search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force-prediction framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface electromyography data from three muscles of the upper arm (biceps brachii, brachioradialis, and triceps brachii) were recorded from ten subjects, as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint angle were utilized as inputs to the FOS model. Subject-specific estimates of optimal joint angles for the three muscles were determined via frequency analysis of the selected FOS candidate functions.

Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling

To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process. SEMG data recorded during constant force, constant velocity, and varying force, varying velocity flexion and extension trials are calibrated. The calibration values are obtained at specific elbow joint angles and interpolated to cover a continuous range of joint angles. The calibrated data are used in PCI models to estimate the force induced at the wrist. The experimental results show the effectiveness of the calibration scheme, combined with PCI modeling. For the constant force, constant velocity trials, minimum %RMSE of 8.3% is achieved for concentric contractions, 10.3% for eccentric contractions and 33.3% for fully dynamic contractions. Force estimation accuracy is superior in concentric contractions in comparison to eccentric contractions , which may be indicative of more nonlinearity in the eccentric SEMG-force relationship.

Operator error in a level coded myoelectric control channel

Two forms of error exist in the level coded myoelectric control channel: system error and operator error. Currently, in level coded (three-state) myoelectric prostheses, target and switching level settings are optimized for the presence of system error only. In this study, system error was minimized in order to examine operator error. The magnitude of the operator error was found to exceed the magnitude of the experimental system error as well as the system error associated with a typical prosthesis control unit. These findings suggest that operator error should be considered when optimizing target levels and decision boundaries for level coded myoelectric prosthesis controllers. Since the operator response was estimated to be normally distributed, it is described by its mean and standard deviation. This information can be used to determine the desired optimal settings.<<ETX>>