2 -D Comparative Gait Kinematics Using a Single

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 08 MBE 026 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 Biceps Femoris µ volts Vastus Medialis µ volts Semi Tendinosus µ volts Rectus Femoris µ volts Lateral Gastrocnemius Vastus Lateralis Medial Gastrocnemius µ volts Soleus Linear envelope of EMG during one gait cycle Tibialis Anterior Normal

500 Average RMS for six subjects 450 400 350 300 250 µ volts 200 150 100 50 0 MGC LGC 180 160 140 120 100 80 60 40 20 0 Average BF ST 102. 4169 Soleus TA rio r te Se m Ti b ia lis An So le us s im em br an em or os u is lis ce ps F Bi m us st Va us la te ed ia ra lis or st Va ct us f em em Re cn ro ga st al er La t Medial Lateral Rectus gastrocne femoris mius 102. 7534 VM is s iu iu ne m oc tr as ia lg ed M Muscles VL Average IEMG for six subjects s µ volts RF 69. 4962 Vastus lateralis 90. 5123 Vastus medialis 100. 0003 Biceps Femoris 76. 9286 Semi Soleus membrano sus 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 • • 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 • • Richard Baker, “Gait analysis methods in rehabilitation”, Journal of Neuro. Engineering 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.

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MARKER BASED VIDEO-GRAPHIC TECHNIQUE Back 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 VM TA VL Back µ volts BF % Stride ST Data Taken From Winter (1991) Normal Hip Angle Normal Knee Angle Back

From Helen Hayes official website Back

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 BACK