Myoelectric Hand Research Articles

An implementation of movement classification for prosthesis control using custom-made EMG system

Electromyography (EMG) is a well known technique used for recording electrical activity produced by human muscles. In the last few decades, EMG signals are used as a control input for prosthetic hands. There are several multifunctional myoelectric prosthetic hands for amputees on the market, but so forth, none of these devices permits the natural control of more than two degrees of freedom. In this paper we present our implementation of the pattern classification using custom made components (electrodes and an embedded EMG amplifier). The components were evaluated in offline and online tests, in able bodied as well as amputee subjects. This type of control is based on computing the time domain features of the EMG signals recorded from the forearm and using these features as input for a Linear Discriminant Analysis (LDA) classifier estimating the intention of the prosthetic user.

Development of a Flexible Socket Integrated with Array Electrodes for a Myoelectric Prosthetic Hand

Development of a Flexible Socket Integrated with Array Electrodes for a Myoelectric Prosthetic Hand

力覚と温度覚を提示する筋電義手用感覚フィードバック装置の開発

力覚と温度覚を提示する筋電義手用感覚フィードバック装置の開発

高装飾性筋電義手の設計と製作

高装飾性筋電義手の設計と製作

感覚フィードバック機能を備えた空気圧ベローズ式筋電義手の開発

感覚フィードバック機能を備えた空気圧ベローズ式筋電義手の開発

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.

SEMG based Classification of Hand Gestures using Artificial Neural Network

Objectives: Study of sEMG signals of the hand gestures is important in designing hand prosthesis. Designing a sEMG pattern recognition system to control a myoelectric hand using neural networks is the objective of this study. Methods/ Statictical Analysis: The sEMG signal was acquired from flexor digitorumsuperficialis and extensor digitorum muscle of the ten healthy subjects by performing 12 different hand gestures. Six parametric feature extraction algorithms were applied to derive the prominent information from sEMG such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. Recognition of the 12 gestures is accomplished using General Regression Neural Network, Probabilistic Neural Network and Radial Basis Function Neural Network. Findings: From the empirical results it was observed that the AR Burg and RBFNN combination had the highest recognition accuracy rate of 94.04%. Investigation also proved that recognition accuracy of sEMG signals were better for females when compare to males. It was also observed from the results that subjects in the age of 26-30 years had better muscle flexion compared to the other age groups studied. Application/ Improvements: In this paper the feasibility of recognizing twelve hand gestures from the sEMG of the hand and finger movements using neural networks was investigated. Six feature extraction algorithms and three neural networks were used to design algorithms for recognizing the twelve hand gestures

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

Cerebral blood perfusion changes in amputees with myoelectric hands after rehabilitation: a SPECT computer-aided analysis.

BackgroundRehabilitation, which is essential for amputees with myoelectric hands, can improve the quality of daily life by remodeling the neuron network. In our study, we aim to develop a cerebral blood perfusion (CBF) single-photon emission computed tomography computer-aided (SPECT-CA) detection scheme to automatically locate the brain’s activated regions after rehabilitation.ResultsFive participants without forearms (three male, two female, mean age 51 ± 12.89 years, two missing the right side, and three missing the left side) were included in our study. In the clinical assessment, all of the participants received higher scores after training. The results of the SPM analysis indicated that CBF in the precentral gyrus, postcentral gyrus, frontal lobe, temporal lobe and cerebellum was significantly different among the five participants (P < 0.05). Moreover, SPECT-CA showed that the activated brain areas mainly included the precentral gyrus, postcentral gyrus, cerebellum and extensive cerebral cortex.ConclusionOur study demonstrated that the CBF SPECT-CA method can detect the brain blood perfusion changes induced by rehabilitation with high sensitivity and accuracy. This method has great potential for locating the remodeled neuron regions of amputees with myoelectric hands after rehabilitation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12868-016-0294-3) contains supplementary material, which is available to authorized users.

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 intuitive control and transcutaneous electrical stimulation of the forearm for vibrotactile sensation feedback applied to a 3D printed prosthetic hand.

This paper presents the development of a myoelectric prosthetic hand based on a 3D printed model. A myoelectric control strategy based on artificial neural networks is implemented on a microcontroller for online position estimation. Position estimation performance achieves a correlation index of 0.78. Also a study involving transcutaneous electrical stimulation was performed to provide tactile feedback. A series of stimulations with controlled parameters were tested on five able-body subjects. A single channel stimulator was used, positioning the electrodes 8 cm on the wrist over the ulnar and median nerve. Controlling stimulation parameters such as intensity, frequency and pulse width, the subjects were capable of distinguishing different sensations over the palm of the hand. Three main sensations where achieved: tickling, pressure and pain. Tickling and pressure were discretized into low, moderate and high according to the magnitude of the feeling. The parameters at which each sensation was obtained are further discussed in this paper.

Myoelectric control of prosthetic hands: state-of-the-art review.

Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy.

New developments in prosthetic arm systems.

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold – insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods.

Development of Sensory Feedback Device for Myoelectric Prosthetic Hand to Provide Hardness of Objects to Users

[abstFig src='/00280003/12.jpg' width=""300"" text='Sensory feedback device for myoelectric hand' ] In this paper, a sensory feedback device was developed to improve the operability of a myoelectric prosthetic hand. The device is worn on the user’s upper arm and provides object hardness feedback to by winding a belt onto the upper arm using a motor. When the finger of the myoelectric prosthetic hand grabs the object, the contact force on the object is detected by a pressure sensor attached to a finger cushion on the myoelectric prosthetic hand. Based on the sensor’s input, the hardness of the object is calculated. According to the hardness of the object, a reference input to realize the corresponding winding speed of the belt is generated by a reference input generator. Then, the motor of the feedback device is controlled to track the reference input by using the self-tuning PID control technique, taking parameter variation into account. Thus, the belt of the feedback device is wound by the motor and tightens the user’s upper arm, thereby enabling the user to receive tactile feedback. Finally, confirmation tests are conducted based on a psychophysical method to verify the effectiveness of the feedback device and its control system. As a result, the difference threshold of the sensory feedback device was 0.59 N/mm.

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.

SEMG-Based Identification of Hand Motion Commands Using Wavelet Neural Network Combined With Discrete Wavelet Transform

Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic, and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by the sEMG signals of the amputee’s residual muscles. To improve the dexterity of the myoelectric prosthetic hand, additional hand motion commands need to be classified. The more sEMG sensors are used, the more hand motion commands can be classified. However, the amputee’s residual muscles are limited. In order to improve the practicability of the myoelectric prosthetic hand, it is critical to investigate the effective pattern recognition algorithms to deal with the sEMG signals detected by fewer sensors, while identifying as many hand motion commands as possible. Current pattern recognition algorithms for sEMG signals are challenged by limited recognition patterns and unsteady classification accuracy rates. To solve these dilemmas, we employed discrete wavelet transform (DWT) and wavelet neural network (WNN) algorithms to improve the pattern recognition effects of sEMG signals. In addition, the back propagation and gradient descent algorithms were utilized to train WNN. In this work, we only used three sEMG sensors to classify and recognize six kinds of hand motion commands. The maximum identification accuracy rate is 100%, and an average classification accuracy rate of the proposed WNN is 94.67%, which is substantially better than the artificial neural network (ANN) algorithm.

筋電義手のための振動による触感フィードバック装置の開発

In this study, a tactile feeling feedback device for myoelectric prosthetic hand users has been developed, in which the tactile feeling is conveyed to users by vibration. A miniature track ball (MTB) is attached to the fingertip of the myoelectric prosthetic hand and is used as a roughness sensor. Surface roughness is divided into 3 levels, which are rough, middle and smooth. Surface roughness of the object is measured by moving the MTB to a certain direction, and is identified as one of the roughness levels. In accordance with the identified roughness level, magnitude of vibration presenting to the user and presenting time are determined. In addition, direction to present vibration is calculated based on rotated direction of the MTB. The tactile feeling feedback device is mounted on an upper arm of the user, which has 9 vibration motors in cross alignment. Then, the tactile feeling is conveyed through vibration corresponding to the identified roughness by driving the vibration motors to the derived direction in turn. Finally, roughness identification and vibration distinction experiment was carried out to verify an effectiveness of the proposed tactile feeling feedback device.

上肢切断者の最先端リハビリ支援 筋電義手(解説 リハビリのメカトロニクス,リハビリ・介護とメカトロニクス)

上肢切断者の最先端リハビリ支援 筋電義手(解説 リハビリのメカトロニクス,<特集>リハビリ・介護とメカトロニクス)

筋電義手を利用した小児リハビリテーション

欧米では広く市民権を得て普及している成人の筋電義手において，ようやく日本で普及の兆しが見え始めている段階である．小児の筋電義手普及は成人と比べてほど遠く，医学の歴史から忘れ去られたかのようである．筋電義手を使用することによる大きな利点の1つは，児の両手動作が促進されることである．このことによって，片手では不可能な動作やすることを諦めていた動作を遂行できる可能性がもたらされる．筋電義手を「使うことができる」と普段の生活で「使う」は大きな隔たりがある．児が年長期，さらには思春期，成人期へと切れ目なく筋電義手を使用していくことは容易ではない．いかにわれわれ医療機関が支援できるかが大きな課題である．

対立把持可能な筋電義手

対立把持可能な筋電義手