scholarly journals Facilitative Exercise for Surface Myoelectric Activity Using Robot Arm Control System – Training Scheme with Gradually Increasing Difficulty Level –

2021 ◽  
Vol 33 (4) ◽  
pp. 851-857
Author(s):  
Ryota Hayashi ◽  
Naoki Shimoda ◽  
Tetsuya Kinugasa ◽  
Koji Yoshida ◽  
◽  
...  
Keyword(s):  
Control Systems ◽  
Human Motion ◽  
Difficulty Level ◽  
Myoelectric Activity ◽  
Robot Arm ◽  
Myoelectric Signals ◽  
Wrist Motion ◽  
Pattern Recognition Techniques ◽  
Training Scheme ◽  
Robot Arm Control

Various control systems for robot arms using surface myoelectric signals have been developed. Abundant pattern-recognition techniques have been proposed to predict human motion intent based on these signals. However, it is laborious for users to train the voluntary control of myoelectric signals using those systems. In this research, we aim to develop a rehabilitation support system for hemiplegic upper limbs with a robot arm controlled by surface myoelectric signals. In this study, we construct a simple one-link robot arm that is controlled by estimating the wrist motion from the surface myoelectric signals on the forearm. We propose a training scheme with gradually increasing difficulty level for robot arm manipulation to evoke surface myoelectric signals. Subsequently, we investigate the possibility of facilitative exercise for the voluntary surface myoelectric activity of the desired muscles through trial experiments.

scholarly journals Improving Gaussianity of EMG Envelope for Myoelectric Robot Arm Control

2021 ◽  
Vol 18 ◽  
pp. 106-112
Author(s):  
Sandra Marquez-Figueroa ◽  
Yuriy S. Shmaliy ◽  
Oscar Ibarra-Manzano
Keyword(s):  
Kalman Filter ◽  
Finite Impulse Response ◽  
Human Motion ◽  
Measurement Noise ◽  
Biomedical Signal Processing ◽  
Robot Arm ◽  
Biomedical Signal ◽  
Emg Signal ◽  
Signal Features ◽  
Robot Arm Control

Several methods have been developed in biomedical signal processing to extract the envelope and features of electromyography (EMG) signals and predict human motion. Also, efforts were made to use this information to improve the interaction of a human body and artificial protheses. The main operations here are envelope acquiring, artifacts filtering, estimate smoothing, EMG value standardizing, feature classifying, and motion recognizing. In this paper, we employ EMG data to extract the envelope with a highest Gaussianity using the rectified signal, where we deal with the absolute EMG signals so that all values become positive. First, we remove artifacts from EMG data by using filters such as the Kalman filter (KF), H1 filter, unbiased finite impulse response (UFIR) filter, and the cKF, cH1 filter, and cUFIR filter modified for colored measurement noise. Next, we standardize the EMG envelope and improve the Gaussianity. Finally, we extract the EMG signal features to provide an accurate prediction.

scholarly journals Intuitive Kinematic Control of a Robot Arm via Human Motion

2014 ◽  
Vol 79 ◽  
pp. 411-416 ◽  
Author(s):  
Hsien-I Lin ◽  
Yu-Cheng Liu ◽  
Yu-Hsiang Lin
Keyword(s):  
Human Motion ◽  
Robot Arm ◽  
Kinematic Control

Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

2021 ◽  
Author(s):  
Ali Nasr ◽  
Brokoslaw Laschowski ◽  
John McPhee
Keyword(s):  
Control Systems ◽  
Myoelectric Control ◽  
Machine Learning Algorithms ◽  
Torque Control ◽  
Future Research ◽  
Reactive Control ◽  
Rhythmic Movements ◽  
Myoelectric Signals ◽  
Joint Torques ◽  
Robotic Leg

Abstract Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

Development of the Robot Arm Control System Using Forearm SEMG

2009 ◽  
pp. 710-713
Author(s):  
Yusuke Wakita ◽  
Noboru Takizawa ◽  
Kentaro Nagata ◽  
Kazushige Magatani
Keyword(s):  
Control System ◽  
Robot Arm ◽  
Robot Arm Control

Human Motion Mapping to a Robot Arm with Redundancy Resolution

2014 ◽  
pp. 193-201 ◽  
Author(s):  
Fanny Ficuciello ◽  
Amedeo Romano ◽  
Vincenzo Lippiello ◽  
Luigi Villani ◽  
Bruno Siciliano
Keyword(s):  
Human Motion ◽  
Robot Arm ◽  
Redundancy Resolution

Gradient Optimization of Inverse Dynamics for Robotic Manipulator Motion Planning Using Combined Optimal Control

2017 ◽  
Author(s):  
Shangdong Gong ◽  
Redwan Alqasemi ◽  
Rajiv Dubey
Keyword(s):  
Optimal Control ◽  
Motion Planning ◽  
Inverse Dynamics ◽  
Null Space ◽  
Optimal Solution ◽  
Human Motion ◽  
Optimization Techniques ◽  
Redundant Manipulators ◽  
Robot Arm ◽  
Gradient Optimization

Motion planning of redundant manipulators is an active and widely studied area of research. The inverse kinematics problem can be solved using various optimization methods within the null space to avoid joint limits, obstacle constraints, as well as minimize the velocity or maximize the manipulability measure. However, the relation between the torques of the joints and their respective positions can complicate inverse dynamics of redundant systems. It also makes it challenging to optimize cost functions, such as total torque or kinematic energy. In addition, the functional gradient optimization techniques do not achieve an optimal solution for the goal configuration. We present a study on motion planning using optimal control as a pre-process to find optimal pose at the goal position based on the external forces and gravity compensation, and generate a trajectory with optimized torques using the gradient information of the torque function. As a result, we reach an optimal trajectory that can minimize the torque and takes dynamics into consideration. We demonstrate the motion planning for a planar 3-DOF redundant robotic arm and show the results of the optimized trajectory motion. In the simulation, the torque generated by an external force on the end-effector as well as by the motion of every link is made into an integral over the squared torque norm. This technique is expected to take the torque of every joint into consideration and generate better motion that maintains the torques or kinematic energy of the arm in the safe zone. In future work, the trajectories of the redundant manipulators will be optimized to generate more natural motion as in humanoid arm motion. Similar to the human motion strategy, the robot arm is expected to be able to lift weights held by hands, the configuration of the arm is changed along from the initial configuration to a goal configuration. Furthermore, along with weighted least norm (WLN) solutions, the optimization framework will be more adaptive to the dynamic environment. In this paper, we present the development of our methodology, a simulated test and discussion of the results.

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