A simple control method to avoid overshoot for prosthetic hand control

This paper proposes a novel control method of using the surface electromyogram (sEMG) signals to predict the kinematics of hand and wrist, which will be applied in the prosthetic hand control. Prediction of movement in 3 degree-of-freedoms’ (DoFs’) (wrist flexion/extension (WFE), lateral abduction/adduction (LAA), and hand open/close (HOC)) is investigated in this paper. The proposed control method contains a time-delay recurrent neural network (TDRNN), adopting the previous prediction of the joint angles and the time-delay sEMG signals as the system input. This proposed method uses a batch training based on Levenberg–Marquardt (LM) algorithm to learn the weights of the TDRNN. The trained TDRNN is aimed to achieve simultaneous and proportional regression from human movements of the 3 DoFs to those of the prosthetic hand. Three able-bodied subjects are chosen to participate in the test and demonstrate its feasibility and performance. The offline test result R2 ranges between 0.81 and 0.94. The online test results show that TDRNN reacts faster, which verifies that the method proposed in this paper will be feasible and effective in prosthetic hand control.

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EMG and ENG-envelope pattern recognition for prosthetic hand control

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2018.10.004 ◽

2019 ◽

Vol 311 ◽

pp. 38-46 ◽

Cited By ~ 11

Author(s):

Emiliano Noce ◽

Alberto Dellacasa Bellingegni ◽

Anna Lisa Ciancio ◽

Rinaldo Sacchetti ◽

Angelo Davalli ◽

...

Keyword(s):

Pattern Recognition ◽

Prosthetic Hand ◽

Hand Control ◽

Envelope Pattern

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Comparison of remote pressure and vibrotactile feedback for prosthetic hand control

2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) ◽

10.1109/biorob.2012.6290268 ◽

2012 ◽

Cited By ~ 11

Author(s):

Camilo Tejeiro ◽

Cara E. Stepp ◽

Mark Malhotra ◽

Eric Rombokas ◽

Yoky Matsuoka

Keyword(s):

Prosthetic Hand ◽

Vibrotactile Feedback ◽

Hand Control

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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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