Artificial Neural Network to Detect Human Hand Gestures for a Robotic Arm Control

2019 ◽  
Author(s):  
Bridget Schabron ◽  
Zaid Alashqar ◽  
Nicole Fuhrman ◽  
Khaled Jibbe ◽  
Jaydip Desai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robotic Arm ◽  
Human Hand ◽  
Hand Gestures ◽  
Artificial Neural ◽  
Robotic Arm Control

scholarly journals Classification of EMG signal for multiple hand gestures based on neural network

2020 ◽  
Vol 17 (1) ◽  
pp. 256 ◽  
Author(s):  
Mohd Azlan Abu ◽  
Syazwani Rosleesham ◽  
Mohd Zubir Suboh ◽  
Mohd Syazwan Md Yid ◽  
Zainudin Kornain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Invasive Technique ◽  
Hand Gestures ◽  
Emg Signal ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

scholarly journals Engenharia de Reabilitação: Mão Antropomórfica Controlada por Sinais Eletromiográficos

2016 ◽  
Author(s):  
Gerson Jorge S. Suzart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Upper Limb ◽  
Human Hand ◽  
Robotic Device ◽  
Hand Amputation ◽  
Artificial Neural

This article proposes to initiate studies to create a robotic device for people with hand amputation, to a certain degree of the upper limb at the level of the elbow, controlled by muscle stimuli captured by electromyographic sensors. The main objective of this study was to collect the characteristics of the EMG signals for pre stipulated movements, recognizing the patterns of these movements by Artificial Neural Network, to develop a prosthetic model that intentionally involves the anthropomorphism of the hand, with respect to functional, that is, able to move So that if similar or close to the human hand.

Modelling of Continuum Robotic Arm Using Artificial Neural Network (ANN)

2019 ◽  
Author(s):  
Albert Ashraf Alphonse ◽  
Ahmed Ashraf Abbas ◽  
Amr Medhat Fathy ◽  
Nada Saif Elsayed ◽  
Hossam Hassan Ammar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robotic Arm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Continuum Robotic

scholarly journals A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ahmed R. J. Almusawi ◽  
L. Canan Dülger ◽  
Sadettin Kapucu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Input Pattern ◽  
Robotic Arm ◽  
Joint Angles ◽  
Neural Network Approach ◽  
Position And Orientation ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.

scholarly journals DEVELOPMENT OF 5 DOF ROBOTIC ARM PROTOTYPE CONTROL SYSTEM IN BRACHYTHERAPY PREPARATION BASED ON ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 15 (2) ◽  
pp. 42
Author(s):  
Adi Abimanyu, M.Eng ◽  
Misbah Habib Putra ◽  
Muhtadan Muhtadan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Inverse Kinematic ◽  
Robotic Arm ◽  
Cancer Tissue ◽  
Artificial Neural ◽  
Radioactive Sources

Brachytherapy is a cancer treatment that uses radioactive sources with temporary or permanent implantation in cancer tissue. The theraphy uses a radioactive Ir-192 source wrapped in a stainless steel capsule with a diameter of 0.5 mm and a length of 4 mm. The Center for Radioisotopes and Radiopharmaceutical Technology applies a remote manipulator to manufacture microcapsules, which affects the accuracy and risks of the radiation received by the operator. Therefore, to solve this problem, it is necessary to design a 5 DoF robotic arm based on artificial neural networks as a radioactive source transfer tool to improve the precision and safety of operators in preparing the radioactive sources. In developing the 5 DoF robotic arm control system, the NImyRIO was employed, which can control the servo motor, relay pump and valve reality, image processing, and inverse kinematic. The inverse kinematic uses the neural network method with a forward kinematic validation. The inverse kinematic test obtains the RMSE value of 2.78932 for x, 5.05205 for y, and 12.641 for z in the inverse kinematic test of artificial neural networks. Therefore, the inverse kinematic accuracy of the artificial neural network needs to be redeveloped.

SEMG based classification of hand gestures using artificial neural network

2020 ◽  
Author(s):  
G. Emayavaramban ◽  
S. Divyapriya ◽  
V.M. Mansoor ◽  
A. Amudha ◽  
M. Siva Ramkumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hand Gestures ◽  
Artificial Neural
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