ࡱ> t5xy Oh+'0 $ @ L X dpx esb_abs.docsb_Dr A. J. Carror Ar ANormal. Vladan Papico9adMicrosoft Word 8.0@O @fk X@5j`W@@EX*  ՜.+,D՜.+,8 hp|  UCD"   esb_abs.doc Title 6> _PID_GUIDAN{CFE3B1C0-50CD-11D3-A07D-0080C8654875} 3D Human Gait Analysis using Software Synchronised Cameras V.Zanchi1, V.Papi1 and A.Despalatovi1 1Department of Electronics, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia. Introduction Although 2D-gait analysis can be useful for some observations and applications, observing the human gait only in one plane (saggital) cant be satisfactory for the more serious and comprehensive analysis. For example, pelvic obliquity and pelvic rotation as well as hip adduction and rotation are the angles that are observed in frontal and transverse plane. These joint rotation angles are standard angles presented in clinical gait analysis reports [1]. 3D Systems In the gait laboratories throughout the world there are already installed 3D Gait Analysis Systems such as Vicon (Oxford Metrics Ltd.), Elite (Bioengineering Technology & Systems), Optotrak (Northern Digital Inc.), etc. This systems allows sampling frequencies above 50 Hz with high resolution. In this paper, we will present a cheap, multicamera system for the gait analysis developed in Laboratory of Biomechanics and Automatic Control Systems in Split [3]. Also, the comparison of gait data acquired with one-camera (2D) system and data acquired with two-camera (3D) system was done. Accuracy Main problems with this type of systems are technical limitations considering resolution and frame rate. The idea was to minimise error for given resolution and sample rate using the highest possible full-frame rate (25 fps - PAL) and maximal picture resolution. According to these considerations, maximal time difference of the sampled frames between cameras was 0.02 (1/50) sec. This difference was further minimised using software synchronisation of the data obtained with each camera. Materials and methods We used two commercial cameras for the experiment: Sony Digital 8 (DCR-TRV110E) and Panasonic VHS (G101 VHS). Programs were running on Pentium PC with Windows 98. PC was supplied with ATI 3D RAGE card used for the picture digitalisation. Analysis was carried-out in six steps: video tape recording of the human walking in the laboratory, making of the Avi files for each camera, obtaining calibration co-ordinates, obtaining of the co-ordinates from the markers positioned on the body and leg of a observed person, C++ program execution for camera data synchronisation and calculating 3D co-ordinates and MATLAB results analysis. Calibration has been done according to internal orientation [2], so there is no pre-defined positions for the cameras. Only cubic frame with known dimensions was required. After co-ordinates data filtering using Fourier transformation (cut-off frequency at 7th harmonic) and data interpolation, obtained signals from both cameras were synchronised according to knee-marker vertical co-ordinate minimum. On the Figure 1 is given flow chart for the C++ program in step 5. Fig. 1: C++ program flowchart. Results As a result of the analysis, 3D gait angles are obtained 2D angle results and 3D angle results are compared. Also, 3D-gait animation using MATLAB software package is done. Discussion and conclusions The accent was on the accuracy of the observation, so we are focused on improving that factor. At the picture resolution of 640*480 pixels and observation plane 2m high and 2.7 m wide, error due to pixelisation is under 0.5 cm which can be considered as satisfactory. As opposite to this, error due to non-synchronised cameras at 25 fps can be 20 times higher (10 cm). This error is calculated for normal walking velocity considering highest markers speed. With presented camera synchronisation involving data filtering and interpolation this error is cut-down to less than 1 cm. As the result, we have inexpensive system with satisfactory performances that can be easily mounted and maintained. Further investigation will be focused on automatic data collection with proper expert system for the clinical application. The force plate will be also included in the measurements. References [1] D.A. Winter (1991) The Biomechanics and Motor Control of Human Gait: Normal Eldery and Pathological, Second Edition, University of Waterloo Press. [2] M. G. Strintzis, S. Malassiotis (1999) in IEEE Signal Processing Magazine: Object-Based Coding of Stereoscopic and 3D Image Sequences, 16, pp. 14-28. 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