Independence of myoelectric control signals examined using a surface emg model

2003 ◽  
Vol 50 (6) ◽  
pp. 789-793 ◽  
Author(s):  
M.M. Lowery ◽  
N.S. Stoykov ◽  
T.A. Kuiken
Keyword(s):  
Surface Emg ◽  
Myoelectric Control ◽  
Control Signals

The Design and Initial Experimental Validation of an Active Myoelectric Transfemoral Prosthesis

2012 ◽  
Vol 6 (1) ◽  
Author(s):  
Carl D. Hoover ◽  
George D. Fulk ◽  
Kevin B. Fite
Keyword(s):  
Neuromuscular Control ◽  
Surface Emg ◽  
Myoelectric Control ◽  
Control Architecture ◽  
Single Subject ◽  
Test Bed ◽  
Real Time Control ◽  
Joint Power ◽  
Time Control ◽  
Transfemoral Prosthesis

This paper describes a single degree-of-freedom active-knee transfemoral prosthesis to be used as a test bed for the development of architectures for myoelectric control. The development of an active-knee transfemoral prosthesis is motivated by the inability of passive commercial prostheses to provide the joint power required at the knee for many activities of daily living such as reciprocal stair ascent, which requires knee power outputs of up to 4 W/kg. Study of myoelectric control based on surface electromyogram (EMG) measurements of muscles in the residual limb is motivated by the desire to restore direct volitional control of the knee using a minimally-invasive neuromuscular control interface. The presented work describes the design of a transfemoral prosthesis prototype including the structure, actuation, instrumentation, electronics, and real-time control architecture. The performance characteristics of the prototype are discussed in the context of the requisite knee energetics for a variety of common locomotive functions. This paper additionally describes the development of a single-subject diagnostic socket with wall-embedded surface EMG electrodes and the implementation of a control architecture for myoelectric modulation of knee impedance. Experimental results of level walking for a single subject with unilateral transfemoral amputation demonstrate the potential for direct EMG-based control of locomotive function.

scholarly journals Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control

Entropy ◽  
2016 ◽  
Vol 18 (4) ◽  
pp. 106 ◽  
Author(s):  
Xu Zhang ◽  
Xiaoting Ren ◽  
Xiaoping Gao ◽  
Xiang Chen ◽  
Ping Zhou
Keyword(s):  
Complexity Analysis ◽  
Surface Emg ◽  
Myoelectric Control ◽  
Ecg Interference

Knit Band Sensor for Myoelectric Control of Surface EMG-Based Prosthetic Hand

2018 ◽  
Vol 18 (20) ◽  
pp. 8578-8586 ◽  
Author(s):  
Seulah Lee ◽  
Myoung-Ok Kim ◽  
Taeho Kang ◽  
Junho Park ◽  
Youngjin Choi
Keyword(s):  
Surface Emg ◽  
Myoelectric Control ◽  
Prosthetic Hand

scholarly journals Efficient Control System Based on Hand Nerve Signals

2019 ◽  
pp. 27-39
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Skin Surface ◽  
Surface Emg ◽  
Processing Unit ◽  
Classification Rate ◽  
Analog To Digital ◽  
Precise Control ◽  
Control Signals ◽  
Smart Control

Recent advances in the control applications based on hand nerve signals are able to meet the needs of users who suffer from restrictions in limb movement and also provide high performance control for those paralyzed people. These signals are represented as Electromyography (EMG) signals, which are biomedical ones, used for clinical/biomedical applications. In this work, a control signals generation system is proposed based on hand EMG measurements. The process of acquisition and processing of EMG signals is performed by only one channel surface EMG electrodes with one EMG processing unit as a muscle sensor. In this work, Arduino UNO is adopted as an analog to digital converter for these hand nerve signals to be easily analyzed in the classification process. These signals are measured from the skin surface of forearm and biceps muscles in two suggested case studies to be used in generating signals based on ten muscles movements. The main features that crystallized this research is building a smart control algorithm which increases the flexibility of generating precise control signals based on contracted hand movements with high simplicity of use and the low cost. The obtained results are compared to other systems results to show the ability of achieving 93.81% classification rate or accuracy among other systems.

Sign in / Sign up

Export Citation Format

Share Document