A REAL-TIME EMG-DRIVEN ARM WRESTLING ROBOT CONSIDERING MOTION CHARACTERISTICS OF HUMAN UPPER LIMBS

2007 ◽  
Vol 04 (04) ◽  
pp. 645-670 ◽  
Author(s):  
QUANJUN SONG ◽  
YONG YU ◽  
YUNJIAN GE ◽  
ZHEN GAO ◽  
HUANGHUAN SHEN ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Wavelet Packet ◽  
Neuromuscular Control ◽  
Joint Force ◽  
Mems Accelerometers ◽  
Usb Camera ◽  
Motion Characteristics

An EMG-driven Arm Wrestling Robot (AWR) is developed for the purposes of studying neuromuscular control of human elbow movements. The AWR arm has two degrees-of-freedom, integrated with mechanical arm, elbow/wrist force sensors, servo motors, encoders, MEMS accelerometers, and a USB camera, and is used to estimate tension generated by individual muscles from recorded electromyograms (EMG). The surface electromyography signal from the upper limb is sampled from a real player in the same conditions. By using the method of wavelet packet transformation (WPT) and autoregressive model (AR), the characteristics of EMG signals can be extracted. Then, an artificial neural network is adopted to estimate the elbow joint force. The effectiveness of the control method using force control estimated via neural network using WPT and AR as inputs is confirmed by experiments. The purpose of this paper is to describe the design objectives, fundamental components, and implementation of our real-time, EMG-driven AWR arm.

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

CNN architecture for robotic arm control in a 3D virtual environment by means of by means of EMG signals

2017 ◽  
Vol 10 (28) ◽  
pp. 1377-1390 ◽  
Author(s):  
Natalie Segura Velandia ◽  
Robinson Jimenez Moreno ◽  
Ruben Dario Hernandez
Keyword(s):  
Neural Network ◽  
Virtual Environment ◽  
Degrees Of Freedom ◽  
Wavelet Packet ◽  
Robotic Arm ◽  
Surface Electrodes ◽  
Average Accuracy ◽  
3D Virtual Environment ◽  
Robotic Arm Control ◽  
Electromyography Signals

This paper presents the development of a 3D virtual environment to validate the effectiveness of a Convolutional Neural Network (CNN) in a virtual application, controlling the movements of a manipulator or robotic arm through commands recognized by the network. The architecture of the CNN network was designed to recognize five (5) gestures by means of electromyography signals (EMGs) captured by surface electrodes located on the forearm and processed by the Wavelet Packet Transform (WPT). In addition to this, the environment consists of a manipulator of 3 degrees of freedom with a final effector type clamp and three objects to move from one place to another. Finally, the network reaches a degree of accuracy of 97.17% and the tests that were performed reached an average accuracy of 98.95%.

Four-channel sEMG for Elbow Load Recognition

2021 ◽  
Author(s):  
Jiabin Cai ◽  
Junjun Song ◽  
Yuanqiang Long
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Control Mode ◽  
Support Vector ◽  
Semg Signal ◽  
Joint Force ◽  
Emg Signal ◽  
The Relationship ◽  
Rehabilitation Exercise

Abstract In order to help patients after surgery to carry out reasonable rehabilitation training, avoid joint adhesions and movement disorders, the relationship between surface electromyograph (sEMG) signal changes and the size of the patient ' s joint force in the process of rehabilitation exercise was studied, hoping to use the relationship between them to redesign the control mode of the rehabilitation robot, and a method was proposed to identify the size of the elbow load based on wavelet packet. Firstly, s EMG signals of human elbow joint during stretching and bending under different loads were collected by 4-channel surface electromyography. Then, the wavelet packet decomposition method was used to obtain the feature vector composed of energy(E), variance(VAR) and mean absolute value(MAV) of wavelet packet coefficient. Finally, the improved support vector machine ( ISVM), BP neural network and RBF neural network were used for pattern recognition of three different forces. The experimental results show that the change of sEMG signal is indeed related to the size of joint force. It is feasible to identify the load of s EMG signal.

Neural Network Compensation Method for Path Tracking Control of a Spherical Mobile Robot

2014 ◽  
Vol 635-637 ◽  
pp. 1325-1328
Author(s):  
Yao Cai ◽  
Feng Gao ◽  
Ze Ning Liu
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Tracking Control ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Path Tracking ◽  
Dynamics Model ◽  
Compensation Strategy ◽  
Spherical Mobile Robot ◽  
Dynamics Models

This paper presents a neural network compensation strategy for the path tracking control of a spherical mobile robot BHQ-2 including a pendulum with two degrees of freedom. Based on our previous work, we propose a simplified method to decompose the dynamics model of BHQ-2 to be two sub-dynamics models. Applying the fuzzy guidance control method and a neural network compensation strategy, a path tracking controller for robot BHQ-2 is designed.

A Master–Slave Control Method for Dexterous Hands with Shaking Elimination Strategy

2017 ◽  
Vol 14 (01) ◽  
pp. 1650016
Author(s):  
Wenzhen Yang ◽  
Xinli Wu ◽  
Shiguang Yu
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Integrated Control ◽  
Open Loop ◽  
Loop Control ◽  
Data Glove ◽  
Motion Data ◽  
Hand Joints ◽  
Dexterous Hand

With the limitations of the artificial intelligence, automatic control and sensor technologies, the dexterous hand in unstructured environments to achieve fully autonomous operations is still very difficult. This paper proposed a master–slave control method for dexterous hands with the combination of the data glove and the micro-stepper motor. The hardware of this method included CyberGloveII device, personal computer (PC), integrated control board (ICB), and YWZ dexterous hand (a multi-fingered robot hand with 20 active degrees of freedom (DOFs)). By the CyberGloveII device, we gained human finger joints motion data in real-time firstly, which were preprocessed by a shaking elimination algorithm to ensure the motion stability of the dexterous hand. Then, the motion data were mapped to the dexterous hand joints, respectively. A communication protocol was designed to transfer the motion data between the PC and the ICB. The motion data were transmitted into the ICB through a serial interface driving the corresponding dexterous hand joints. The experimental results showed that this method is feasible, can achieve the open-loop control of dexterous hands, and has excellent movement accuracy, real-time and stability.

scholarly journals Design of bionic active–passive hybrid-driven prosthesis based on gait analysis and simulation of compound control method

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Xinsheng Xu ◽  
Xiaoli Xu ◽  
Ying Liu ◽  
Kai Zhong ◽  
Haowei Zhang
Keyword(s):  
Real Time ◽  
Ankle Joint ◽  
Control Algorithm ◽  
Control Method ◽  
Real Time Control ◽  
Outer Loop ◽  
Time Control ◽  
Human Ankle ◽  
Motion Characteristics ◽  
Joint Prostheses

Abstract Purpose The purpose of this paper is to design a prosthetic limb that is close to the motion characteristics of the normal human ankle joint. Methods In this study, combined with gait experiments, based on a dynamic ankle joint prosthesis, an active–passive hybrid-driven prosthesis was designed. On this basis, a real-time control algorithm based on the feedforward compensation angle outer loop is proposed. To test the effectiveness of the control method, a multi-body dynamic model and a controller model of the prosthesis were established, and a co-simulation study was carried out. Results A real-time control algorithm based on the feedforward compensation angle outer loop can effectively realize the gait angle curve measured in the gait test, and the error is less than the threshold. The co-simulation result and the test result have a high close rate, which reflects the real-time nature of the control algorithm. The use of parallel springs can improve the energy efficiency of the prosthetic system. Conclusions Based on the motion characteristics of human ankle joint prostheses, this research has completed an effective and feasible design of active and passive ankle joint prostheses. The use of control algorithms improves the controllability of the active and passive ankle joint prostheses.

scholarly journals Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiguang Liu ◽  
Jianhong Hao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Real Time ◽  
Basis Function ◽  
Control Method ◽  
Contact Point ◽  
Estimation Method ◽  
Human Robot Interaction ◽  
Data Matching ◽  
Radial Basis

To solve synchronization movement problem in human-robot haptic collaboration, the robot is often required to recognize intention of the cooperator. In this paper, a method based on radial basis function neural network (RBFNN) model is presented to identify the motion intention of collaborator. Here, the human intention is defined as the desired velocity in human limb model, of which the estimation is obtained in real time based on interaction force and the contact point movement characteristics (current position and velocity of the robot) by the trained RBFNN model. To obtain training samples, adaptive impedance control method is used to control the robot during the data acquisition process, and then the data matching is executed due to the phase delay of the impedance function. The advantage of proposed intention estimation method according to the system real-time status is that the model overcomes the shortcoming of difficult estimating the human body impedance parameters. The experimental results show that this proposed method improves the synchronization of human-robot collaboration and reduces the force of the collaborator.

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