scholarly journals Hand Sign Classification Employing Myoelectric Signals of Forearm

10.5772/51080 ◽  
2012 ◽  
Author(s):  
Takeshi Tsujimura ◽  
Sho Yamamoto ◽  
Kiyotaka Izumi
Keyword(s):  
Myoelectric Signals

Myoelectric signals of levator palpebrae superioris as a trigger for FES to restore the paralyzed eyelid

2012 ◽  
Vol 78 (5) ◽  
pp. 559-561 ◽  
Author(s):  
Simin Deng ◽  
Xin Yi ◽  
Pengfei Xin ◽  
Dedong Yu ◽  
Guoxing Wang ◽  
...  
Keyword(s):  
Myoelectric Signals ◽  
Levator Palpebrae

Characterization of Myoelectric Signals Using System Identification Techniques

2004 ◽  
Author(s):  
Jeffrey T. Bingham ◽  
Marco P. Schoen
Keyword(s):  
System Identification ◽  
Motor Neurons ◽  
Current System ◽  
Electrical Potential ◽  
Computer Software ◽  
The Body ◽  
Myoelectric Signals ◽  
Hand Motion ◽  
The Central Nervous System

Human muscle motion is initiated in the central nervous system where a nervous signal travels through the body and the motor neurons excite the muscles to move. These signals, termed myoelectric signals, can be measured on the surface of the skin as an electrical potential. By analyzing these signals it is possible to determine the muscle actions the signals elicit, and thus can be used in manipulating smart prostheses and teleoperation of machinery. Due to the randomness of myoelectric signals, identification of the signals is not complete, therefore the goal of this project is to complete a study of the characterization of one set of hand motions using current system identification methods. The gripping motion of the hand and the corresponding myoelectric signals are measured and the data captured with a personal computer. Using computer software the captured data are processed and finally subjected to several system identification routines. Using this technique it is possible to construct a mathematical model that correlates the myoelectric signals with the matching hand motion.

Myoelectric signals for multimodal speech recognition

2005 ◽  
Author(s):  
Raghunandan S. Kumaran ◽  
Karthik Narayanan ◽  
John N. Gowdy
Keyword(s):  
Speech Recognition ◽  
Myoelectric Signals

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.

scholarly journals Identification of myoelectric signals of pregnant rat uterus: new method to detect myometrial contraction

2017 ◽  
Vol 58 (2) ◽  
pp. 141-148 ◽  
Author(s):  
Kálmán F. Szűcs ◽  
György Grosz ◽  
Miklós Süle ◽  
Anikó Nagy ◽  
Zita Tiszai ◽  
...  
Keyword(s):  
New Method ◽  
Rat Uterus ◽  
Myoelectric Signals ◽  
Pregnant Rat
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