Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity

Inverse Modelling to Obtain Head Movement Controller Signal

Experimentally obtained dynamics of time-optimal, horizontal head rotations have previously been simulated by a sixth order, nonlinear model driven by rectangular control signals. Electromyography (EMG) recordings have spects which differ in detail from the theoretical rectangular pulsed control signal. Control signals for time-optimal as well as sub-optimal horizontal head rotations were obtained by means of an inverse modelling procedures. With experimentally measured dynamical data serving as the input, this procedure inverts the model to produce the neurological control signals driving muscles and plant. The relationships between these controller signals, and EMG records should contribute to the understanding of the neurological control of movements

Effect of Computer Keyboard Slope on Wrist Position and Forearm Electromyography of Typists Without Musculoskeletal Disorders

Positioning a computer keyboard with a downward slope reduces wrist extension needed to use the keyboard and has been shown to decrease pressure in the carpal tunnel. However, whether a downward slope of the keyboard reduces electromyographic (EMG) activity of the forearm muscles, in particular the wrist extensors, is not known. Subjects and Methods. Sixteen experienced typists participated in this study and typed on a conventional keyboard that was placed on slopes. Electromyographic activity of the extensor carpi ulnaris (ECU), flexor carpi ulnaris (FCU), and flexor carpi radialis (FCR) muscles was measured with surface electrodes, while the extension and ulnar deviation angles of the right and left wrists were measured with electrogoniometers. Results. Wrist extension angle decreased from approximately 12 degrees of extension while typing on a keyboard with a 7.5-degree slope to 3 degrees of flexion with the keyboard at a slope of –15 degrees. Although the differences were in the range of 1% to 3% of maximum voluntary contraction (MVC), amplitude probability distribution function (APDF) of root-mean-square EMG data points from the ECU, FCU, and FCR muscles varied across keyboard slopes. Discussion and Conclusion. Wrist extension decreased as the keyboard slope decreased. Furthermore, a slight decrease in percentage of MVC of the ECU muscle was noted as the keyboard slope decreased. Based on biomechanical modeling and published work on carpal tunnel pressure, both of these findings appear to be positive with respect to comfort and fatigue, but the exact consequences of these findings on the reduction or prevention of injuries have yet to be determined. The results may aid physical therapists and ergonomists in their evaluations of computer keyboard workstations and in making recommendations for interventions with regard to keyboard slope angle. [Simoneau GG, Marklin RW, Berman JE. Effect of computer keyboard slope on wrist position and forearm electromyography of typists without musculoskeletal disorders. Phys Ther. 2003;83:816–830.

Use of MMG signals for the control of powered orthotic devices: Development of a rectus femoris measurement protocol

Copyright © 2009 Rehabilitation Engineering and Assistive Technology Society (RESNA). This is an Author's Accepted Manuscript of an article published in Assistive Technology, 21(1), 1 - 12, 2009, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/10400430902945678.A test protocol is defined for the purpose of measuring rectus femoris mechanomyographic (MMG) signals. The protocol is specified in terms of the following: measurement equipment, signal processing requirements, human postural requirements, test rig, sensor placement, sensor dermal fixation, and test procedure. Preliminary tests of the statistical nature of rectus femoris MMG signals were performed, and Gaussianity was evaluated by means of a two-sided Kolmogorov-Smirnov test. For all 100 MMG data sets obtained from the testing of two volunteers, the null hypothesis of Gaussianity was rejected at the 1%, 5%, and 10% significance levels. Most skewness values were found to be greater than 0.0, while all kurtosis values were found to be greater than 3.0. A statistical convergence analysis also performed on the same 100 MMG data sets suggested that 25 MMG acquisitions should prove sufficient to statistically characterize rectus femoris MMG. This conclusion is supported by the qualitative characteristics of the mean rectus femoris MMG power spectral densities obtained using 25 averages

Advances in surface EMG signal simulation with analytical and numerical descriptions of the volume conductor

Surface electromyographic (EMG) signal modeling is important for signal interpretation, testing of processing algorithms, detection system design, and didactic purposes. Various surface EMG signal models have been proposed in the literature. In this study we focus on 1) the proposal of a method for modeling surface EMG signals by either analytical or numerical descriptions of the volume conductor for space-invariant systems, and 2) the development of advanced models of the volume conductor by numerical approaches, accurately describing not only the volume conductor geometry, as mainly done in the past, but also the conductivity tensor of the muscle tissue. For volume conductors that are space-invariant in the direction of source propagation, the surface potentials generated by any source can be computed by one-dimensional convolutions, once the volume conductor transfer function is derived (analytically or numerically). Conversely, more complex volume conductors require a complete numerical approach. In a numerical approach, the conductivity tensor of the muscle tissue should be matched with the fiber orientation. In some cases (e.g., multi-pinnate muscles) accurate description of the conductivity tensor may be very complex. A method for relating the conductivity tensor of the muscle tissue, to be used in a numerical approach, to the curve describing the muscle fibers is presented and applied to representatively investigate a bi-pinnate muscle with rectilinear and curvilinear fibers. The study thus propose an approach for surface EMG signal simulation in space invariant systems as well as new models of the volume conductor using numerical methods

Influence of Baseline Fluctuation Cancellation on Automatic Measurement of Motor Unit Action Potential Duration

The aim of this work is to analyze the influence of a method for baseline fluctuation (BLF) cancellation for electromyographic (EMG) signals on automatic methods for measurement of the motor unit action potential (MUAP) duration. These methods include four conventional automatic methods (CAMs) and a recently published wavelet transform method (WTM). A set of 182 MUAPs from 170 EMG recordings were studied. The CAMs and the WTM were applied to the MUAPs before and after applying BLF cancellation to the recordings. A gold standard of duration marker positions (GSP) ws manually established. The accuracy of each algorithm was estimated as the dfference between its positions and the GSP. Accuracies were compared for the 5 methods and for each method before and after BLF cancellation. A significant difference between accuracy pre- and post-BLF removal was found in two CAMs; markers were closer to the GSP after BLF removal. For all MUAPs, the differences between WTM markers and the GSP were the smallest, and significant differences were not found for the WTM before and after BLF cancellation. The management of BLF is an important issue in EMG signal processing and BLF removal must be considered in extraction and analyse of MUAP waveforms. The BLF removal method improved the performance of two CAMs for MUAP duration measurement. The WTM was the most accurate and was not affected by BLF.

A Biomechanical Model for the Development of Myoelectric Hand Prosthesis Control Systems

Advanced myoelectric hand prostheses aim to reproduce as much of the human hand's functionality as possible. Development of the control system of such a prosthesis is strongly connected to its mechanical design; the control system requires accurate information on the prosthesis' structure and the surrounding environment, which can make development difficult without a finalized mechanical prototype. This paper presents a new framework for the development of electromyographic hand control systems, consisting of a prosthesis model based on the biomechanical structure of the human hand. The model's dynamic structure uses an ellipsoidal representation of the phalanges. Other features include underactuation in the fingers and thumb modeled with bond graphs, and a viscoelastic contact model. The model's functions are demonstrated by the execution of lateral and tripod grasps, and evaluated with regard to joint dynamics and applied forces. Finally, additions are suggested with which this model can be of use in mechanical design and patient training as well