EMG-driven control in lower limb prostheses: a topic-based systematic review (original) (raw)
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Electromyography-based control of lower limb prostheses: a systematic review
Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched from July-September 2020: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,327 papers were initially assessed and 117 were finally included in this review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Improving the intuitiveness of the interaction between human and machine is an important issue for powered lower-limb prosthesis control. In this research, we aimed to evaluate the potential of using surface electromyography (EMG) signals measured from transtibial amputees' residual muscles to directly control the position of prosthetic ankle. In this research, one transtibial amputee subject and five able-bodied subjects were recruited. They were asked to control a virtual ankle to reach different target positions. The amputee subject finished these tasks in an average time of 1.29 seconds for different target positions with the residual limb, which was comparable with that using the amputee's sound limb and those with able-bodied subjects' dominant legs. Due to human's strong adaptability, the amputee subject was able to adapt to the control model trained one day before or trained in a posture which was different from that during performing control tasks. These res...
Upper Limb Prosthesis Using EMG Signal: Review
The International Journal Of Science & Technoledge , 2014
Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. We propose the use of EMG signal whitening as a pre-processing step in EMG-based motion classification. Whitening decor relates the EMG signal and has been shown to be advantageous in other EMG applications including EMG amplitude estimation and EMG-force processing. Drawbacks of using whitening include its substantial added computation and memory requirements, the need to collect calibration data, and possible robustness issues in the presence of high frequency noise. This draw backs can be overcome by the degrees of freedom (DOFs). DOFs implements pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using non amputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for non-amputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less than a single linear discriminant analysis (LDA) classifier or a parallel approach. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements. The current statistics includes average of 18,496 upper-extremity amputations every year, compared to 113,702 of the lower extremity. Of those, only 1900 are above the wrist. Among upper-limb amputees, typically fewer than half wear prosthetic arms. An estimated number of 541,000 Americans were living with some form of upper limb loss in 2005 and this number is projected to more than double with an aging and growing population by 2050.
Design and Development of an EMG Driven Microcontroller Based Prosthetic Leg
Bangladesh Journal of Medical Physics, 2013
Over the past years prosthetic legs have become much improved although complex. However their costs are high and are not within the reach of most people in the Third World. Low-cost fixed prostheses are available in the Third world countries of Asia and Africa, but these offer very basic movement with unnatural gait; climbing stairs gets quite difficult. The prosthesis being worked upon in the present work are for amputees with legs removed above the knee, and would offer a limited rotational movement of the knee joint under voluntary control of the wearer, driven by the EMG signals extracted from thigh muscles. The aim is to make it at a low cost, may be at a cost slightly higher than the passive ones, but allowing a better gait in walking, and in climbing stairs. An initial work was done in this direction by our extended group earlier; the present work gives further improvements. This involves redesigning of the motor and the gear system and that of the electronic circuitry for pr...
An EMG-controlled neuroprosthesis for daily upper limb support: A preliminary study
2011
MUNDUS is an assistive platform for recovering direct interaction capability of severely impaired people based on upper limb motor functions. Its main concept is to exploit any residual control of the end-user, thus being suitable for long term utilization in daily activities. MUNDUS integrates multimodal information (EMG, eye tracking, brain computer interface) to control different actuators, such as a passive exoskeleton for weight relief, a neuroprosthesis for arm motion and small motors for grasping. Within this project, the present work integreted a commercial passive exoskeleton with an EMG-controlled neuroprosthesis for supporting hand-to-mouth movements. Being the stimulated muscle the same from which the EMG was measured, first it was necessary to develop an appropriate digital filter to separate the volitional EMG and the stimulation response. Then, a control method aimed at exploiting as much as possible the residual motor control of the end-user was designed. The controller provided a stimulation intensity proportional to the volitional EMG. An experimental protocol was defined to validate the filter and the controller operation on one healthy volunteer. The subject was asked to perform a sequence of hand-to-mouth movements holding different loads. The movements were supported by both the exoskeleton and the neuroprosthesis. The filter was able to detect an increase of the volitional EMG as the weight held by the subject increased. Thus, a higher stimulation intensity was provided in order to support a more intense exercise. The study demonstrated the feasibility of an EMG-controlled neuroprosthesis for daily upper limb support on healthy subjects, providing a first step forward towards the development of the final MUNDUS platform.
IEEE Access
Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR) based control in upper-limb prostheses is considered an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, but their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this article firstly explored the principles and dynamics of the existing intelligently driven EMG-PR based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this study reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR based prostheses in the near future.
Sensors, 2019
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the ...
Feasibility of EMG-Based Neural Network Controller for an Upper Extremity Neuroprosthesis
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2000
The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 spinal cord injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from "voluntary" activations with less than a 3.6% RMS prediction error.
Computer Methods and Programs in Biomedicine, 2019
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Development of a sustainable and ergonomic interface for the EMG control of prosthetic hands
6th EAI International Conference on Wireless Mobile Communication and Healthcare, November 14–16, 2016, Milano, Italy, 2016
Most of the interfaces of the current upper limb prosthetic device are rigid. However, human limbs and body are a combination of rigid and soft parts. Such a combination inherently suggests to implement soft ergonomic interfaces between the human body and such prosthetic devices. To this aim we have developed a novel set of wearable solutions, including a textile sleeve embedding EMG electrodes for the control of hand prosthesis. This interface has been integrated and preliminary tested in order to control a 5 d.o.f. low cost robotic hand.