1. GUIDED BY Mr. Chaitanya Srinivas L.V. Sujeet Blessing Assistant Professor 08MBE026 SBSTVIT University VIT UniversityVellore Vellore 2-D Comparative Gait Kinematics Using a Single Video Camera and EMG Signal Analysis

2. SUMMARY OF WORK Acquisition and Processing of EMG for six subjects from nine musclesmuscles Stride analysis for six subjects Kinematics analysis for six subjects Marker based automated video-graphic analysis Marker-less automated video-graphic analysis

3. 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

4. Biceps FemorisVastus MedialisVastus Lateralis Semi TendinosusRectus FemorisMedial Gastrocnemius Lateral GastrocnemiusSoleusTibialis Anterior Linear envelope of EMG during one gait cycle µ volts Normal

5. µ volts MusclesMedial gastrocne mius Lateral gastrocne mius Rectus femoris Vastus lateralis Vastus medialis Biceps Femoris Semi membrano sus SoleusTibialis Anterior Average102.7534102.416969.496290.5123100.000376.9286147.1159108.0622163.433

6. STRIDE ANALYSIS Stride analysis – Paper-Ink Method Step length, Stride length, Cadence, Stride width, Velocity, Foot progression angle

7. KINEMATIC ANALYSIS The motion of objects without consideration of the causes leading to the motion Determinants of position Active – EMG Passive – Force

8. 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 Results Pics

9. 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 transform Results Pics

10. Colour image Binary image Video Frame ‘n’ Blob detection Draw lines Hough’s Transform Draw lines Hough’s Transform Hip angle Knee angle MARKER TECHNIQUE Video

11. Video (in RGB) Silhouette extraction Frame ‘n’ Swing Phase Algorithm Stance Phase Algorithm Segmentation and Detection of Markers Adjusting Leg Shortening using extraction Drawing Lines Angle Detection MARKER-LESS TECHNIQUE Video

12. 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

13. 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

14. 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.

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19. HIP ANGLE KNEE ANGLE MARKER BASED VIDEO-GRAPHIC TECHNIQUE MARKER-LESS VIDEO-GRAPHIC TECHNIQUE HIP ANGLEKNEE ANGLE Back

20. MUSCLES Lateral gastrocnemius, Medial gastrocnemius, Vastus lateralis, Vastus medialis, Rectus femoris, Biceps femoris, Semi tendinosus, Soleus, Tibialis anterior Back

21. LGMG VLRF VM TA BF SOLEUS ST % Stride µ volts Data Taken From Winter (1991) Normal Hip AngleNormal Knee Angle Back

22. From Helen Hayes official website Back

23. 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

24. hi 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