Real-time arm motion imitation for human–robot tangible interface

2009 ◽  
Vol 2 (2) ◽  
pp. 61-69 ◽  
Author(s):  
Yukyung Choi ◽  
SyungKwon Ra ◽  
Soowhan Kim ◽  
Sung-Kee Park
Keyword(s):  
Real Time ◽  
Tangible Interface ◽  
Arm Motion

scholarly journals Robotic Arm With Real Time Human Arm Motion Replication Using IoT

2018 ◽  
Vol 455 ◽  
pp. 012060
Author(s):  
Kodidety Solomon Jones ◽  
R. Uday Kumar
Keyword(s):  
Real Time ◽  
Robotic Arm ◽  
Human Arm ◽  
Arm Motion

Using EMG Signals to Remotely Control a 3D Industrial Robotic Arm

2019 ◽  
Author(s):  
Wafa Batayneh ◽  
Ahmad Bataineh ◽  
Samer Abandeh ◽  
Mohammad Al-Jarrah ◽  
Mohammad Banisaeed ◽  
...  
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Control Device ◽  
Robotic Arm ◽  
Measurement Unit ◽  
Software Interface ◽  
Emg Signal ◽  
Hardware Interface ◽  
Gesture Control ◽  
Arm Motion

Abstract In this paper, a muscle gesture computer Interface (MGCI) system for robot navigation Control employing a commercial wearable MYO gesture Control armband is proposed. the motion and gesture control device from Thalamic Labs. The software interface is developed using LabVIEW and Visual Studio C++. The hardware Interface between the Thalamic lab’s MYO armband and the robotic arm has been implemented using a National Instruments My RIO, which provides real time EMG data needed. This system allows the user to control a three Degrees of freedom robotic arm remotely by his/her Intuitive motion by Combining the real time Electromyography (EMG) signal and inertial measurement unit (IMU) signals. Computer simulations and experiments are developed to evaluate the feasibility of the proposed System. This system will allow a person to wear this/her armband and move his/her hand and the robotic arm will imitate the motion of his/her hand. The armband can pick up the EMG signals of the person’s hand muscles, which is a time varying noisy signal, and then process this MYO EMG signals using LabVIEW and make classification of this signal in order to evaluate the angles which are used as feedback to servo motors needed to move the robotic arm. A simulation study of the system showed very good results. Tests show that the robotic arm can imitates the arm motion at an acceptable rate and with very good accuracy.

scholarly journals Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2104 ◽  
Author(s):  
Umme Zakia ◽  
Carlo Menon
Keyword(s):  
Real Time ◽  
Preliminary Investigation ◽  
Support Vector ◽  
Force Estimation ◽  
Motion Patterns ◽  
Interactive Control ◽  
Arm Motions ◽  
Force Myography ◽  
Arm Motion ◽  
Hand Forces

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.

scholarly journals Real Time Computation for Robotic Arm Motion upon a Linear or Circular Trajectory

2018 ◽  
Vol 9 (2) ◽  
Author(s):  
Liliana Marilena ◽  
Cornelia Gyorödi ◽  
Helga Maria ◽  
Simona Veronica
Keyword(s):  
Real Time ◽  
Robotic Arm ◽  
Circular Trajectory ◽  
Arm Motion
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