Myoelectric Prosthesis Control Research Articles

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control.

Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications.

Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human–robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the highest coherence with hand movements. Our results represent a first step toward a more effective and intuitive control of myoelectric hand prostheses.

Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm**This work was supported by the National Nature Science Foundation of China under Grant 61431017 and 61271138.

Objective. Some skeletal muscles can be subdivided into smaller segments called muscle-tendon units (MTUs). The purpose of this paper is to propose a framework to locate the active region of the corresponding MTUs within a single skeletal muscle and to analyze the activation level varieties of different MTUs during a dynamic motion task. Approach. Biceps brachii and gastrocnemius were selected as targeted muscles and three dynamic motion tasks were designed and studied. Eight healthy male subjects participated in the data collection experiments, and 128-channel surface electromyographic (sEMG) signals were collected with a high-density sEMG electrode grid (a grid consists of 8 rows and 16 columns). Then the sEMG envelopes matrix was factorized into a matrix of weighting vectors and a matrix of time-varying coefficients by nonnegative matrix factorization algorithm. Main results. The experimental results demonstrated that the weightings vectors, which represent invariant pattern of muscle activity across all channels, could be used to estimate the location of MTUs and the time-varying coefficients could be used to depict the variation of MTUs activation level during dynamic motion task. Significance. The proposed method provides one way to analyze in-depth the functional state of MTUs during dynamic tasks and thus can be employed on multiple noteworthy sEMG-based applications such as muscle force estimation, muscle fatigue research and the control of myoelectric prostheses.

Abstract: Validation of Regenerative Peripheral Nerve Interfaces for Control of a Myoelectric Hand by Macaques and Human

INTRODUCTION: Regenerative Peripheral Nerve Interfaces (RPNIs) are promising for interfacing human intentions to myoelectric prostheses. Rat studies led to proof of internal RPNI long-term function and high signal to noise ratio with no adverse biological effects.1 Validation of voluntary and independent RPNI function has also been determined.2 However, true voluntary fine control of fingers and hand myoelectric prostheses requires RPNI real time validation with voluntary control. Our purpose was to validate voluntary RPNI control of a myoelectric hand in both macaque and human models. METHODS: The RPNI consists of a free muscle graft implanted on the end of a transected nerve fascicle. RPNIs were implanted in the forearms of two macaques (n=3/macaque). Intramuscular electromyography (EMG) electrodes were also implanted in each macaque RPNI. Macaques were trained to perform index finger movements to acquire virtual targets on a computer screen.3 The human RPNIs were implanted to treat neuroma pain. The human had fine wire electrodes temporarily placed in the RPNIs. Voluntary RPNI EMG that represented control was recorded. RESULTS: With continuous EMG decode using 10-fold cross-validation, the resulting predicted finger position had a, correlation coefficient ρ=0.82 between predicted and true finger positions for Macaques. The EMG decode correctly classified 97.7% of movements. The human ulnar nerve RPNIs were able to control thumb key pinch to first finger and thumb to little finger. Thumb abduction trials were 96% correct. At harvest macaque RPNIs were well vascularized; RPNI muscle fibers continued to regeneration after 1 year. CONCLUSIONS: Macaques voluntarily controlled virtual finger movements with nerve signals transferred through implanted RPNIs. The human controlled an advanced myoelectric prosthetic thumb with RPNIs implanted in the ulnar nerve RPNI. Voluntary RPNI control of a myoelectric hand was validated by both macaque and human models. DISCLOSURE/FINANCIAL SUPPORT:This work was sponsored by the Defense Advanced Research Projects Agency (DARPA) MTO under the auspices of Dr. Jack Judy through the Space and Naval Warfare Systems Center, Pacific Grant/Contract No. N66001-11-C-4190. None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript. REFERENCES: 1. Kung T, Langhals N, Martin D, Cederna P, Urbanchek M. Regenerative Peripheral Nerve Interface Viability and Signal Transductions with an Implanted Electrode. Plast Rec Surg. 2014; 133(6): 1380–94 2. Ursu D, Urbanchek MG, Nedic A, Cederna P, Gillespie R. In Vivo Characterization of Regenerative Peripheral Nerve Interface Function. J Neuro Engin. 2016 April;13(2):026012 3. Irwin Z, Thompson D, Schroeder K, et al. IEEE Enabling Low-power, Multi-modal Neural Interfaces through a Common, Low-bandwidth Feature Space. Trans Neural Sys & Rehab Engin. 2015 DOI 10.1109/TNSRE.2015.2501752

Exoprosthetic Replacement of the Upper Extremity

During the last years, the prosthetic replacement in upper limb amputees has undergone different developments. The use of new nerve surgical concepts improved the control strategies tremendously, especially for high-level amputees. Technological innovation in the field of pattern recognition enables the control of multifunctional myoelectric hand prostheses in a natural and intuitive manner. However, the different levels of amputation pose different challenges for the therapeutic team which concern not only the prosthetic attachment; also the expected functional outcome of prosthetic limb replacement differs greatly between the individual levels of amputation. Therefore, especially in partial hand amputations the indication for prosthetic fitting has to be evaluated critically, as these patients may benefit more from biologic reconstructive concepts. The value of the upper extremity, in particular of the hand, is undisputable and, as such represents the driving force for the technological and surgical developments within the exoprosthetic replacement. This article discusses the possibilities and limitations of exoprosthetic limb replacement on the different amputation levels and explores new developments.

Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control.

We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods.

Limb-position robust classification of myoelectric signals for prosthesis control using sparse representations.

The fundamental objective in non-invasive myoelectric prosthesis control is to determine the user's intended movements from corresponding skin-surface recorded electromyographic (sEMG) activation signals as quickly and accurately as possible. Linear Discriminant Analysis (LDA) has emerged as the de facto standard for real-time movement classification due to its ease of use, calculation speed, and remarkable classification accuracy under controlled training conditions. However, performance of cluster-based methods like LDA for sEMG pattern recognition degrades significantly when real-world testing conditions do not resemble the trained conditions, limiting the utility of myoelectrically controlled prosthesis devices. We propose an enhanced classification method that is more robust to generic deviations from training conditions by constructing sparse representations of the input data dictionary comprised of sEMG time-frequency features. We apply our method in the context of upper-limb position changes to demonstrate pattern recognition robustness and improvement over LDA across discrete positions not explicitly trained. For single position training we report an accuracy improvement in untrained positions of 7.95%, p ≪ .001, in addition to significant accuracy improvements across all multiposition training conditions, p <; .001.

Integrated and flexible multichannel interface for electrotactile stimulation

Objective. The aim of the present work was to develop and test a flexible electrotactile stimulation system to provide real-time feedback to the prosthesis user. The system requirements were to accommodate the capabilities of advanced multi-DOF myoelectric hand prostheses and transmit the feedback variables (proprioception and force) using intuitive coding, with high resolution and after minimal training. Approach. We developed a fully-programmable and integrated electrotactile interface supporting time and space distributed stimulation over custom designed flexible array electrodes. The system implements low-level access to individual stimulation channels as well as a set of high-level mapping functions translating the state of a multi-DoF prosthesis (aperture, grasping force, wrist rotation) into a set of predefined dynamic stimulation profiles. The system was evaluated using discrimination tests employing spatial and frequency coding (10 able-bodied subjects) and dynamic patterns (10 able-bodied and 6 amputee subjects). The outcome measure was the success rate (SR) in discrimination. Main results. The more practical electrode with the common anode configuration performed similarly to the more usual concentric arrangement. The subjects could discriminate six spatial and four frequency levels with SR >90% after a few minutes of training, whereas the performance significantly deteriorated for more levels. The dynamic patterns were intuitive for the subjects, although amputees showed lower SR than able-bodied individuals (86% ± 10% versus 99% ± 3%). Significance. The tests demonstrated that the system was easy to setup and apply. The design and resolution of the multipad electrode was evaluated. Importantly, the novel dynamic patterns, which were successfully tested, can be superimposed to transmit multiple feedback variables intuitively and simultaneously. This is especially relevant for closing the loop in modern multifunction prostheses. Therefore, the proposed system is convenient for practical applications and can be used to implement sensory perception training and/or closed-loop control of myoelectric prostheses, providing grasping force and proprioceptive feedback.

Flexible Inkjet-Printed Multielectrode Arrays for Neuromuscular Cartography.

Flexible Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) ( PSS) conductive-polymer multielectrode arrays (MEAs) are fabricated without etching or aggressive lift-off processes, only by additive solution processes. Inkjet printing technology has several advantages, such as a customized design and a rapid realization time, adaptability to different patients and to different applications. In particular, inkjet printing technology, as additive and "contactless" technology, can be easily inserted into various technological fabrication steps on different substrates at low cost. In vivo electrochemical impedance spectroscopy measurements show the time stability of such MEAs. An equivalent circuit model is established for such flexible cutaneous MEAs. It is shown that the charge transfer resistance remains the same, even two months after fabrication. Surface electromyography and electrocardiography measurements show that the PSS MEAs record electrophysiological activity signals that are comparable to those obtained with unitary Ag/AgCl commercial electrodes. Additionally, such MEAs offer parallel and simultaneous recordings on multiple locations at high surface density. It also proves its suitability to reconstruct an innervation zone map and opens new perspectives for a better control of amputee's myoelectric prostheses. The employment of additive technologies such as inkjet printing suggests that the integration of multifunctional sensors can improve the performances of ultraflexible brain-computer interfaces.

An Empirical Evaluation of Force Feedback in Body-Powered Prostheses.

Myoelectric prostheses have many advantages over body-powered prostheses, yet the absence of sensory feedback in myoelectric devices is one reason body-powered devices are often preferred by amputees. While considerable progress has been made in the mechanical design and control of myoelectric prostheses, research on haptic feedback has not had a similar impact. In this study, we seek to develop a fundamental understanding of the utility of force feedback and vision in the functional operation of a body-powered upper-limb prosthesis. Using a custom body-powered prosthesis in which force feedback can be conditionally removed, we asked N=10 non-amputee participants to identify objects based on stiffness in four separate conditions with and without visual and/or force feedback. Results indicate that the combination of visual and force feedback allows for the best accuracy, followed by force feedback only, then visual feedback only. In addition, combining force feedback with visual feedback does not significantly affect identification timing compared to visual feedback alone. These findings suggest that consideration should be given to the development of force feedback displays for myoelectric prostheses that function like a Bowden cable, coupling the amputee's control input to the resulting feedback.

Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control

Electromyographic (EMG) signals from muscles in the body torso are often contaminated by electrocardiography (ECG) interferences, which consequently compromise EMG intensity estimation. The ECG interference has become a barrier to proportional control of myoelectric prosthesis using a neural machine interface called targeted muscle reinnervation (TMR), which involves transferring the residual amputated nerves to nonfunctional muscles (typically pectoralis muscles for high level amputations). This study investigates a novel approach toward implementation of proportional myoelectric control by applying sample entropy (SampEn) analysis of surface EMG signals for robust intensity estimation in the presence of significant ECG interference. Surface EMG data from able-bodied and TMR amputee subjects with different degrees of ECG interference were used for performance evaluation. The results showed that the SampEn analysis had high correlation with surface EMG amplitude measurement but low sensitivity to different degrees of ECG interference. Taking this advantage, SampEn analysis of surface EMG signal can be used to facilitate implementation of proportional myoelectric control against ECG interference. This is particularly important for TMR prosthesis users.

Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses.

Human grasping and manipulation control critically depends on tactile feedback. Without this feedback, the ability for fine control of a prosthesis is limited in upper limb amputees. Although various approaches have been investigated in the past, at present there is no commercially available device able to restore tactile feedback in upper limb amputees. Based on the Discrete Event-driven Sensory feedback Control (DESC) policy we present a device able to deliver short-lasting vibrotactile feedback to transradial amputees using commercially available myoelectric hands. The device (DESC-glove) comprises sensorized thimbles to be placed on the prosthesis digits, a battery-powered electronic board, and vibrating units embedded in an arm-cuff being transiently activated when the prosthesis makes and breaks contact with objects. The consequences of using the DESC-glove were evaluated in a longitudinal study. Five transradial amputees were equipped with the device for one month at home. Through a simple test proposed here for the first time-the virtual eggs test-we demonstrate the effectiveness of the device for prosthetic control in daily life conditions. In the future the device could be easily exploited as an add-on to complement myoelectric prostheses or even embedded in prosthetic sockets to enhance their control by upper limb amputees.

Real-Time Classification of Hand Motions Using Ultrasound Imaging of Forearm Muscles

Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle-computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle-computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Regenerative Peripheral Nerve Interface for Prostheses Control: Electrode Comparison.

This study compared epimysial patch electrodes with intramuscular hook electrodes using monopolar and bipolar recording configurations. The purpose was to determine which strategy transduced muscle signals with better fidelity for control of myoelectric prostheses. One of the two electrode styles, patch (n = 4) or hook (n = 6) was applied to the left extensor digitorum longus muscle in rats. Electrodes were evaluated at the time of placement and at monthly intervals for 4 months. Evaluations consisted of evoked electromyography signals from stimulation pulses applied to the peroneal and tibial nerves in both monopolar and bipolar recording configurations. Compared with hook electrodes, patch electrodes recorded larger signals of interest and minimized muscle tissue injury. A bipolar electrode configuration significantly reduced signal noise when compared with a monopolar configuration. Epimysial patch electrodes outperform intramuscular hook electrodes during chronic skeletal muscle implantation.

A structurally decoupled mechanism for measuring wrist torque in three degrees of freedom.

The wrist joint is a critical part of the human body for movement. Measuring the torque of the wrist with three degrees of freedom (DOFs) is important in some fields, including rehabilitation, biomechanics, ergonomics, and human-machine interfacing. However, the particular structure of the wrist joint makes it difficult to measure the torque in all three directions simultaneously. This work develops a structurally decoupled instrument for measuring and improving the measurement accuracy of 3-DOF wrist torque during isometric contraction. Three single-axis torque sensors were embedded in a customized mechanical structure. The dimensions and components of the instrument were designed based on requirement of manufacturability. A prototype of the instrument was machined, assembled, integrated, and tested. The results show that the structurally decoupled mechanism is feasible for acquiring wrist torque data in three directions either independently or simultaneously. As a case study, we use the device to measure wrist torques concurrently with electromyography signal acquisition in preparation for simultaneous and proportional myoelectric control of prostheses.

Task discrimination for non-weight-bearing movements using muscle synergies.

Myoelectric control of lower limb prostheses requires discrimination of task-specific muscle patterns. In this paper we present a method based on the notion of muscle synergies to discriminate between various non-weight-bearing movements such as knee extension/flexion, femur rotation in/out, tibia rotation in/out and ankle dorsiflexion/plantarflexion. Data is recorded from eight targeted muscle sites on the thigh. Non-negative matrix factorization is used to identify the muscle synergies using multiple features and estimation of electromyographic (EMG) patterns is done using non-negative least squares (NNLS). Classification accuracy for the movements involving the knee joint was higher than the movements involving the ankle joint. The proposed algorithm performs at par with the common machine learning algorithm Linear Discriminant Analysis (LDA) in offline analysis.

New Control Strategies for Multifunctional Prostheses that Combine Electromyographic and Speech Signals

The control of multifunctional myoelectric prostheses is commonly limited due to the lack of sufficient electromyography (EMG) signals after amputation. With a goal of developing easy-to-use and flexibly controlled multifunctional prostheses, the authors propose two control strategies that fuse EMG and speech signals. In the first, speech signals switch the joint-mode, and EMG signals determine a motion class to actuate the target movement. In the second, speech signals select a motion class directly, and EMG signals actuated the movement. Experimental results showed that the proposed strategies could improve control performance and enhance the operational efficiency significantly, suggesting that signal fusion is a feasible way to effectively strengthen interactions between humans and machines.

Hand and Wrist Movement Control of Myoelectric Prosthesis Based on Synergy

This study proposes a method to control a prosthetic hand by EMG signals based on muscle synergies. The muscle synergy model suggests a framework to transform commands of the central nervous system to a set of complex muscular movements. Using this method, we have tried to realize the proportional control of multiple degrees of freedom (DOF). This study focuses on controlling four kinds of hand/wrist movements of the prosthesis: open, close, pronate, and supinate. The nonnegative matrix factorization (NMF) algorithm is used to map muscle activities into these four movements through the calculation of a muscle synergy matrix. An EMG feature selection process along with a control scheme has been added, which smooths the output thereby stabilizing the movements. Ten healthy subjects performed an online experiment comprised of two tests: 1) proportional control on single DOF, and 2) simultaneous control of multiple DOFs. The results indicate that fluid hand/wrist movements could be estimated from EMG. The average $R^2$ values achieved by all subjects for the single-DOF test and the multiple-DOF test are 0.97 and 0.93, respectively.

A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier

Since surface electromyography is an electrical activity of superficial muscles and is an essential tool to investigate assessments protocols to be required for prosthetic design, so here, the wavelet transforms based interpretation of Surface Electromyogram signal for classifications of upper arm operations were investigated. The study presented methods of processing and analyzing Surface Electromyogram signal for upper arm motions for extracting accurate patterns of the signal. From these recorded signals, amplitude estimated features were extracted and explored significantly. Then a comparative study to evaluate the wavelet denoising for optimal motor unit action potential detection through the decomposition based on the different wavelet functions of Daubechies, Coiflet and Symmlets families were investigated and tabulated. Thereafter linear discriminating analysis pattern classifier approach was employed to analyze classification performance for different upper arm movements. Results inferred that Daubechies wavelet families were more suitable for the analysis of surface electromyogram signals of different upper arm motions and a classification accuracy of 85.0% was achieved. Finally data projection method of analysis of variance technique was implemented for the effectiveness of recorded surface electromyogram signals for class separability of upper arm motions.

Exploring the mechanism of neural-function reconstruction by reinnervated nerves in targeted muscles

A lack of myoelectric sources after limb amputation is a critical challenge in the control of multifunctional motorized prostheses. To reconstruct myoelectric sources physiologically related to lost limbs, a newly proposed neural-function construction method, targeted muscle reinnervation (TMR), appears promising. Recent advances in the TMR technique suggest that TMR could provide additional motor command information for the control of multifunctional myoelectric prostheses. However, little is known about the nature of the physiological functional recovery of the reinnervated muscles. More understanding of the underlying mechanism of TMR could help us fine tune the technique to maximize its capability to achieve a much higher performance in the control of multifunctional prostheses. In this study, rats were used as an animal model for TMR surgery involving transferring a median nerve into the pectoralis major, which served as the target muscle. Intramuscular myoelectric signals reconstructed following TMR were recorded by implanted wire electrodes and analyzed to explore the nature of the neural-function reconstruction achieved by reinnervation of targeted muscles. Our results showed that the active myoelectric signal reconstructed in the targeted muscle was acquired one week after TMR surgery, and its amplitude gradually became stronger over time. These preliminary results from rats may serve as a basis for exploring the mechanism of neural-function reconstruction by the TMR technique in human subjects.