Normal human gaits based on image edge detection

doi:10.3969/j.issn.2095-4344.2013.13.013

Abstract

Abstract:

BACKGROUND: The parameters of kinematics and dynamics are usually used to describe the law of gaits. Up to now, in the fields of sports medicine, rehabilitation engineering and bionics, gait analysis can provide important basis for ascertaining the scenario of diagnosis disease, the scenario of treatment and recovery.
OBJECTIVE: To acquire the lower extremity motion gaits video on the treadmill by the image measuring device system based on the human motion in order to analyze the regular pattern of human gaits.
METHODS: The identification points were set on the right and left hip joints, knee joints and foot plate by the image-gathering system in order to acquire the lower extremity motion gait while the subject walked on a treadmill. After processing and analyzing the data based on the theory of image edge detection, angle between the right and left thighs and vertical direction, angle between leg and vertical direction, angle between foot plate and vertical direction and the changes of the angles of identification points of knee joints and ankle joints were obtained.
RESULTS AND CONCLUSION: The human lower extremity motion gait obtained according to the theory of image edge detection has relatively low cost and small error, and the accuracy was similar to the imported equipment. Finally, a database of human gait was built based on the measurement results, and it provided a basis for the further establishment of assessing standard for human gait, better analysis of abnormal gait and rehabilitation treatments．

Key words: bone and joint implants, bone and joint biomechanics, video image, edge detection, gaits, lower limbs, joint angle, law of motion, image capture system, database, rehabilitation evaluation, assessment standards, other grants-supported paper

CLC Number:

R318

Meng Qing-yun, Tan Shi-li, Yu Hong-liu, Shen Li-xing. Normal human gaits based on image edge detection[J]. Chinese Journal of Tissue Engineering Research, 2013, 17(13): 2368-2374.

Figures/Tables 9

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