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Tracking of markers for 2D and 3D gait analysis using home video cameras
a Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This work presents the development of an affordable optical motion-capture system which uses home video cameras for 2D and 3D gait analysis. The 2D gait analyzer system consists of one camcorder and one PC while the 3D gait analyzer system uses two camcorders, a flash and two PCs. Both systems make use of 25 fps camcorder, LED markers and technical computing software to track motions of markers attached to human body during walking. In the experiment for 3D gait analyzer system, the two cameras are synchronized by using flash. The recorded videos for both systems are extracted into frames and then converted into binary images, and bridge morphological operation is applied for unconnected pixel to facilitate marker detection process. Least distance method is then employed to track the markers motions, and 3D Direct Linear Transformation is used to reconstruct 3D markers positions. The correlation between length in pixel and in the real world resulted from calibration process is used to reconstruct 2D markers positions. To evaluate the reliability of the 2D and 3D optical motion-capture system developed in the present work, spatio-temporal and kinematics parameters calculated from the obtained markers positions are qualitatively compared with the ones from literature, and the results show good compatibility. Copyright © 2013, IGI Global.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]2D Gait Analysis,3D Direct Linear Transformation,3D Gait Analysis,Camera Synchronization,Home Video Camera,Image Processing,Least Distance Method[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors gratefully acknowledge the support from several research grants, which had made this study possible. i.e. ITB Research Grant 2009, AUN/SEED-Net Research Program for Alumni Members, and ITB Research and Innovation Program 2011.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.4018/jehmc.2013070103[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]