Design and fabrication of a myoelectric prosthesis for trans-radial amputees

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

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Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees

Scientific Reports ◽

10.1038/s41598-021-84795-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Eric J. Earley ◽

Reva E. Johnson ◽

Jonathon W. Sensinger ◽

Levi J. Hargrove

Keyword(s):

Sensory Feedback ◽

Feedforward Control ◽

Feedback System ◽

Augmented Feedback ◽

Myoelectric Prosthesis ◽

Reaching Task ◽

Prosthesis Control ◽

High Uncertainty ◽

Relevant Factors ◽

Positional Control

AbstractAccurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision; some studies showed benefits, while others did not. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty (e.g. joint speed). In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found that overall reaching errors were reduced after perturbed control, but did not significantly improve steady-state reaches. Furthermore, we found that feedback about the joint speed of the myoelectric prosthesis control improved the adaptation rate of biological limb movements, which may have resulted from high prosthesis control noise and strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

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Real time ECG artifact removal for myoelectric prosthesis control

Physiological Measurement ◽

10.1088/0967-3334/28/4/006 ◽

2007 ◽

Vol 28 (4) ◽

pp. 397-413 ◽

Cited By ~ 46

Author(s):

Ping Zhou ◽

Blair Lock ◽

Todd A Kuiken

Keyword(s):

Real Time ◽

Artifact Removal ◽

Myoelectric Prosthesis ◽

Prosthesis Control

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An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-015-0098-1 ◽

2015 ◽

Vol 12 (1) ◽

Cited By ~ 20

Author(s):

Jeremy D. Brown ◽

Andrew Paek ◽

Mashaal Syed ◽

Marcia K. O’Malley ◽

Patricia A. Shewokis ◽

...

Keyword(s):

Grip Force ◽

Haptic Feedback ◽

Myoelectric Prosthesis

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INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519421500433 ◽

2021 ◽

pp. 2150043

Author(s):

SIDHARTH PANCHOLI ◽

AMIT M. JOSHI

Keyword(s):

Pattern Recognition ◽

Upper Limb ◽

Classification Accuracy ◽

Time Derivative ◽

Machine Learning Algorithms ◽

Prosthetic Control ◽

Myoelectric Prosthesis ◽

Feature Extraction Method ◽

Similar Work ◽

Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

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Fabric Vest Socket with Embroidered Electrodes for Control of Myoelectric Prosthesis

Sensors ◽

10.3390/s20041196 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1196 ◽

Cited By ~ 1

Author(s):

Seulah Lee ◽

Babar Jamil ◽

Sunhong Kim ◽

Youngjin Choi

Keyword(s):

Signal To Noise Ratio ◽

Cost Effective ◽

Electrode Impedance ◽

Rigid Support ◽

Daily Lives ◽

Myoelectric Prosthesis ◽

Myoelectric Prostheses ◽

Average Classification Accuracy ◽

High Level ◽

Semg Signals

Myoelectric prostheses assist users to live their daily lives. However, the majority of users are primarily confined to forearm amputees because the surface electromyography (sEMG) that understands the motion intents should be acquired from a residual limb for control of the myoelectric prosthesis. This study proposes a novel fabric vest socket that includes embroidered electrodes suitable for a high-level upper amputee, especially for shoulder disarticulation. The fabric vest socket consists of rigid support and a fabric vest with embroidered electrodes. Several experiments were conducted to verify the practicality of the developed vest socket with embroidered electrodes. The sEMG signals were measured using commercial Ag/AgCl electrodes for a comparison to verify the performance of the embroidered electrodes in terms of signal amplitudes, the skin-electrode impedance, and signal-to-noise ratio (SNR). These results showed that the embroidered electrodes were as effective as the commercial electrodes. Then, posture classification was carried out by able-bodied subjects for the usability of the developed vest socket. The average classification accuracy for each subject reached 97.92%, and for all the subjects it was 93.2%. In other words, the fabric vest socket with the embroidered electrodes could measure sEMG signals with high accuracy. Therefore, it is expected that it can be readily worn by high-level amputees to control their myoelectric prostheses, as well as it is cost effective for fabrication as compared with the traditional socket.

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Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

Mechanical Sciences ◽

10.5194/ms-12-69-2021 ◽

2021 ◽

Vol 12 (1) ◽

pp. 69-83

Author(s):

Saygin Siddiq Ahmed ◽

Ahmed R. J. Almusawi ◽

Bülent Yilmaz ◽

Nuran Dogru

Keyword(s):

Neural Network ◽

Control Method ◽

Low Cost ◽

Hand Movement ◽

Cost Effective ◽

Network Control ◽

Human Hand ◽

Prosthetic Hand ◽

Myoelectric Prosthesis ◽

System File

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.

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Game-Based Rehabilitation for Myoelectric Prosthesis Control

JMIR Serious Games ◽

10.2196/games.6026 ◽

2017 ◽

Vol 5 (1) ◽

pp. e3 ◽

Cited By ~ 20

Author(s):

Cosima Prahm ◽

Ivan Vujaklija ◽

Fares Kayali ◽

Peter Purgathofer ◽

Oskar C Aszmann

Keyword(s):

Myoelectric Prosthesis ◽

Prosthesis Control

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