clc;
close all;
clear all;
emg1x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg1y=[0 0 1 2 1 0 0 1 2 -1 -2 1 2 1 0 -1 1 0 0 0 ]
figure(1)
plot(emg1x,emg1y)
emg2x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg2y=[0 0 0 0 0 0 0 1 2 -1 -2 1 2 1 0 -1 1 0 0 0 ]
figure(2)
plot(emg2x,emg2y)
emg3x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg3y=[0 0 1 2 1 0 0 1 2 -1 0 0 0 0 0 0 0 0 0 0 ]
figure(3)
plot(emg3x,emg3y)
s=serial('com6');
set(s,'BaudRate',9600);
fopen(s);
for i=1:1:100
i=rand(1,1);
i=i*3
if(i<1)
figure(4)
plot(emg1x,emg1y)
drawnow
pause(0.1)
fprintf(s,'1')
end
if(i>2)
figure(4)
plot(emg2x,emg2y)
drawnow
fprintf(s,'2')
pause(0.1)
end
if( i>1 && i<2)
figure(4)
plot(emg3x,emg3y)
drawnow
fprintf(s,'3')
pause(0.1)
end
end
fclose(s)
delete(s)
clear s
close all;
clear all;
emg1x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg1y=[0 0 1 2 1 0 0 1 2 -1 -2 1 2 1 0 -1 1 0 0 0 ]
figure(1)
plot(emg1x,emg1y)
emg2x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg2y=[0 0 0 0 0 0 0 1 2 -1 -2 1 2 1 0 -1 1 0 0 0 ]
figure(2)
plot(emg2x,emg2y)
emg3x=[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]
emg3y=[0 0 1 2 1 0 0 1 2 -1 0 0 0 0 0 0 0 0 0 0 ]
figure(3)
plot(emg3x,emg3y)
s=serial('com6');
set(s,'BaudRate',9600);
fopen(s);
for i=1:1:100
i=rand(1,1);
i=i*3
if(i<1)
figure(4)
plot(emg1x,emg1y)
drawnow
pause(0.1)
fprintf(s,'1')
end
if(i>2)
figure(4)
plot(emg2x,emg2y)
drawnow
fprintf(s,'2')
pause(0.1)
end
if( i>1 && i<2)
figure(4)
plot(emg3x,emg3y)
drawnow
fprintf(s,'3')
pause(0.1)
end
end
fclose(s)
delete(s)
clear s
clc
close all
clear all
%%Inactive EMG signal
x1=[1 2 3 4 5];
y1=[1 1 1 2 1];
%Acive EMG small activity servo 1
x2=[1 2 3 4 5];
y2=[1 2 10 -10 1];
%Acive EMG large activity servo 1
x3=[1 2 3 4 5];
y3=[100 -100 149 -79 100];
%Acive EMG small activity servo 2
x4=[1 2 3 4 5];
y4=[1 2 10 -10 1];
%Acive EMG large activity servo 2
x5=[1 2 3 4 5];
y5=[100 -100 149 -79 100];
%Acive EMG small activity servo gripper
x6=[1 2 3 4 5];
y6=[1 2 10 -10 1];
%Acive EMG large activity servo gripper
x7=[1 2 3 4 5];
y7=[100 -100 149 -79 100];
j=1;
for i=1:1:100
b=ceil(7*rand(1,1));
ith=num2str(b);
nogen=strcat('y',ith);
disp(nogen)
y=(eval(nogen));
display(y);
figure(i)
plot(y);
% j=j+1;
% plot(y,j);
% hold on
end
%
% x=[x1 x2 x1 x1 x3 x4 x5 x6 x7];
% y=[y1 y2 y1 y1 y3 y4 y5 y6 y7];
%
% plot(y);
close all
clear all
%%Inactive EMG signal
x1=[1 2 3 4 5];
y1=[1 1 1 2 1];
%Acive EMG small activity servo 1
x2=[1 2 3 4 5];
y2=[1 2 10 -10 1];
%Acive EMG large activity servo 1
x3=[1 2 3 4 5];
y3=[100 -100 149 -79 100];
%Acive EMG small activity servo 2
x4=[1 2 3 4 5];
y4=[1 2 10 -10 1];
%Acive EMG large activity servo 2
x5=[1 2 3 4 5];
y5=[100 -100 149 -79 100];
%Acive EMG small activity servo gripper
x6=[1 2 3 4 5];
y6=[1 2 10 -10 1];
%Acive EMG large activity servo gripper
x7=[1 2 3 4 5];
y7=[100 -100 149 -79 100];
j=1;
for i=1:1:100
b=ceil(7*rand(1,1));
ith=num2str(b);
nogen=strcat('y',ith);
disp(nogen)
y=(eval(nogen));
display(y);
figure(i)
plot(y);
% j=j+1;
% plot(y,j);
% hold on
end
%
% x=[x1 x2 x1 x1 x3 x4 x5 x6 x7];
% y=[y1 y2 y1 y1 y3 y4 y5 y6 y7];
%
% plot(y);
![](http://www.weebly.com/weebly/images/file_icons/xls.png)
emg_controlled_robotic_arm.pptx | |
File Size: | 56 kb |
File Type: | pptx |
Research Papers
![](http://www.weebly.com/weebly/images/file_icons/gz.png)
18.zip | |
File Size: | 2883 kb |
File Type: | zip |
![](http://www.weebly.com/weebly/images/file_icons/file.png)
18.z01 | |
File Size: | 9437 kb |
File Type: | z01 |
Abstract
Explain your project in simplest word 15 lines
Introduction
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles.[1] EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells[2] when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities, activation level, or recruitment order or to analyze the biomechanics of human or animal movement.
Aim of study
Thesis
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In this paper we describe the novel concept of performance-based progressive robot therapy that uses speed, time, or EMG thresholds to initiate robot assistance. We pioneered the clinical application of robot-assisted therapy focusing on stroke—the largest cause of disability in the US. We have completed several clinical studies involving well over 200 stroke patients. Research to date has shown that repetitive task-specific, goal-directed, robot-assisted therapy is effective in reducing motor impairments in the affected arm after stroke. One research goal is to determine the optimal therapy tailored to each stroke patient that will maximize his/her recovery. A proposed method to achieve this goal is a novel performance-based impedance control algorithm, which is triggered via speed, time, or EMG. While it is too early to determine the effectiveness of the algorithm, therapists have already noted one very strong benefit, a significant reduction in arm tone.
Physiological background
Working principals
1) EMG
2) Flemings rule
3) Left rule
4) Electromagneti induction
5) Sensor
Block diagram
Development
stages
1) liter/ block diagram
2) compon
3) pCB
4) Mechanical assembly
5) Testing
6) Coding
7) Debuging
Futre scope
application
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Datasheet:-