Transcript 2-D Comparative Gait Kinematics Using a Single Video

2-D Comparative Gait Kinematics Using a
Single Video Camera and EMG Signal
Analysis
GUIDED BY
Mr. Chaitanya Srinivas L.V.
Assistant Professor
SBST
VIT University
Vellore
Sujeet Blessing
08MBE026
VIT University
Vellore
SUMMARY OF WORK
• Acquisition and Processing of EMG for six
subjects from nine muscles
• Stride analysis for six subjects
• Kinematics analysis for six subjects
• Marker based automated video-graphic
analysis
• Marker-less automated video-graphic
analysis
EMG ANALYSIS
 EMG acquisition
 EMG processing
 Linear envelope
 Normalization using Maximum
Voluntary Contraction
 Wave rectification
 Butterworth low pass filter
 Integrated EMG
 Output from Low pass filter is passed
through an integrator
 Root mean square
µ volts
µ volts
Biceps Femoris
µ volts
µ volts
Vastus Medialis
µ volts
Semi Tendinosus
µ volts
Rectus Femoris
µ volts
µ volts
Lateral Gastrocnemius
Vastus Lateralis
Medial Gastrocnemius
µ volts
Soleus
Linear envelope of EMG during one gait cycle
Tibialis Anterior
Normal
Average RMS for six subjects
500
400
300
µ volts
200
100
0
Average IEMG for six subjects
µ volts
Muscles
Average
Medial
Lateral
Rectus
gastrocne gastrocne femoris
mius
mius
102.7534
102.4169
69.4962
Vastus
lateralis
90.5123
Vastus
medialis
Biceps
Femoris
100.0003
Semi
Soleus
membrano
sus
76.9286
147.1159
108.0622
Tibialis
Anterior
163.433
STRIDE ANALYSIS
Stride analysis – Paper-Ink Method
Step length, Stride length, Cadence, Stride width,
Velocity, Foot progression angle
KINEMATIC ANALYSIS
• The motion of objects without
consideration of the causes leading to the
motion
• Determinants of position
• Active – EMG
• Passive – Force
MARKER TECHNIQUE
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Helen Hayes marker set
Distance from Camera – 9 feet
Camera captures 25 frames/second
Image processing
• Colour image to binary image
• Blob detection
• Drawing line, connecting respective
markers
• Line and angle detection using Hough’s
transform
Pics
Results
MARKER-LESS TECHNIQUE
• Converting into silhouette video
• Extraction of the silhouette
• Segmenting leg into thigh, shin and foot
using manual measurements
• Finding mid points of these segments,
which serves as markers
• Correlating these markers with the unsegmented body
• Drawing lines connecting these markers
• Detecting lines and angles using Hough’s
transform
Pics
Results
Video
MARKER TECHNIQUE
Frame ‘n’
Colour image
Binary image
Blob detection
Draw lines
Hough’s Transform
Hip angle
Draw lines
Hough’s Transform
Knee angle
Video
Video (in
RGB)
MARKER-LESS TECHNIQUE
Silhouette
extraction
Frame ‘n’
Stance Phase
Algorithm
Swing Phase
Algorithm
Segmentation and
Detection of Markers
Adjusting Leg Shortening
using extraction
Drawing Lines
Segmentation and
Detection of
Markers
Angle Detection
Drawing Lines
Angle Detection
Video
COMPARISON
•Marker-less technique has a wide range of hip angle
•Knee flexion angle during heel strike is not clearly seen
in marker-less technique, however, during swing phase, it
has a good range
Normal
CONCLUSION
• Stride analysis was carried out using
paper-ink method
• Emg was acquired from nine muscles from
six subjects, processed and averaged
• Kinematic analysis was done on the same
six subjects
• Marker and Marker-less automated videographic techniques were developed and
the results were compared
REFERENCE
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Richard Baker, “Gait analysis methods in rehabilitation”, Journal of
NeuroEngineering and Rehabilitation, 2006, 3:4.
Mary M. Rodgers, “Dynamic biomechanics of the normal foot and
ankle during walking and running”, Physical Therapy, 1988, 1822-30.
Michela Goffredo, Imed Bouchrika, John N. Carter and Mark S.
Nixon, “Performance analysis for gait in camera networks”,
Association of Computing Machinery, 2008, 73-80.
Y.P. Ivanenko, R.E. Poppele and F. Lacquaniti, “Five basic muscle
activation patterns account for muscle activity during human
locomotion”, American Journal of Physiology, 2004, 267-282.
M.B.I. Reaz, M.S. Hussain and F. Mohd-Yasin, “Techniques of
EMG signal analysis: Detection, processing, classification and
applications”, Biological Procedures, 2006, 8(1): 11-35.
Noraxon EMG and Sensor System, “Clinical SEMG Electrode
Sites.” www.noraxon.com.
Helen Hayes Marker System, www.helenhayeshospital.org.
Queries???
THANK YOU.
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MARKER BASED VIDEO-GRAPHIC TECHNIQUE
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HIP ANGLE
KNEE ANGLE
MARKER-LESS VIDEO-GRAPHIC TECHNIQUE
Back
HIP ANGLE
KNEE ANGLE
MUSCLES
• Lateral gastrocnemius, Medial
gastrocnemius, Vastus lateralis, Vastus
medialis, Rectus femoris, Biceps femoris,
Semi tendinosus, Soleus, Tibialis anterior
Back
LG
SOLEUS
MG
RF
TA
VL
VM
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µ volts
% Stride
BF
ST
Data Taken From Winter
(1991)
Normal Hip Angle
Normal Knee Angle
Back
From Helen Hayes official website
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a – one frame of an original video; b – grey image; c, d – binary image; e – blob
detection; f – for hip angle
estimation; g – for knee angle estimation; h – detected lines by Hough’s transform
for hip angle;
i – detected lines by Hough’s transform for knee angle
Back
h
i
a – Silhouette of a original frame; b – image extracted
from
d – negative image; e – correlating the manual
the hip; c – extracting only the subject from the
background;
measurements with the pixel values; f – shin; g –
upper leg;
h – drawing lines connecting the markers; i – detected
lines using Hough’s transform
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