GUIDED BY Mr. Chaitanya Srinivas L.V. Sujeet Blessing Assistant Professor 08MBE026 SBSTVIT University VIT UniversityVellore Vellore 2-D Comparative Gait.

GUIDED BY

Mr. Chaitanya Srinivas L.V. Sujeet Blessing

Assistant Professor 08MBE026

SBST VIT University

VIT University Vellore

Vellore

2-D Comparative Gait Kinematics Using a Single Video Camera and EMG Signal

Analysis

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 acquisitionEMG processing

Linear envelopeNormalization using Maximum

Voluntary ContractionWave rectificationButterworth low pass filter

Integrated EMGOutput from Low pass filter is passed

through an integratorRoot mean square

Biceps Femoris

Vastus Medialis

Vastus Lateralis

Semi Tendinosus

Rectus Femoris

Medial Gastrocnemius

Lateral Gastrocnemius

Soleus Tibialis Anterior

Linear envelope of EMG during one gait cycle

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Normal

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Muscles Medial gastrocnemius

Lateral gastrocnemius

Rectus femoris

Vastus lateralis

Vastus medialis

Biceps Femoris

Semi membranosus

Soleus Tibialis Anterior

Average 102.7534 102.4169 69.4962 90.5123 100.0003 76.9286 147.1159 108.0622 163.433

STRIDE ANALYSIS

Stride analysis – Paper-Ink MethodStep 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

• 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

ResultsPics

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 un-

segmented body• Drawing lines connecting these markers• Detecting lines and angles using Hough’s

transformResultsPics

Colour imageColour image

Binary imageBinary image

VideoVideo

Frame ‘n’Frame ‘n’

Blob detectionBlob detection

Draw linesDraw lines

Hough’s TransformHough’s Transform

Draw linesDraw lines

Hough’s TransformHough’s Transform

Hip angleHip angle Knee angleKnee angle

MARKER TECHNIQUEMARKER TECHNIQUE

Video

Video (in RGB)

Video (in RGB)

Silhouette extractionSilhouette extraction

Frame ‘n’Frame ‘n’

Swing Phase Algorithm

Swing Phase Algorithm

Stance Phase Algorithm

Stance Phase Algorithm

Segmentation and Detection of Markers

Segmentation and Detection of Markers

Segmentation and Detection of

Markers

Segmentation and Detection of

Markers

Adjusting Leg Shortening using extraction

Adjusting Leg Shortening using extraction

Drawing LinesDrawing Lines

Drawing LinesDrawing Lines

Angle DetectionAngle Detection

Angle DetectionAngle Detection

MARKER-LESS TECHNIQUEMARKER-LESS TECHNIQUE

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 video-graphic techniques were developed and the results were compared

REFERENCE• 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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HIP ANGLE KNEE ANGLE

MARKER BASED VIDEO-GRAPHIC TECHNIQUE

MARKER-LESS VIDEO-GRAPHIC TECHNIQUE

HIP ANGLE KNEE ANGLE

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MUSCLES

• Lateral gastrocnemius, Medial gastrocnemius, Vastus lateralis, Vastus medialis, Rectus femoris, Biceps femoris, Semi tendinosus, Soleus, Tibialis anterior

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LG MG

VLRFVM

TA

BF

SOLEUS

ST

% Stride

µ volts

Data Taken From Winter (1991)

Normal Hip Angle

Normal Knee Angle

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From Helen Hayes official websiteFrom 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

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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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