Osteoarthritis, Knee Clinical Trial
Official title:
Biometric Recognition and Rehabilitation Assessment of Lower Extremity Sports Injury Based on Gait Touch Information
The current common clinical methods cannot truly reflect the biomechanical status of the knee joint. Based on the foot-knee coupling mechanism, the simple and practical dynamic gait touch information provided by the 3D force platform are closely related to the knee biomechanics. The purpose of this study is to investigate the disease feature recognition, computer-aided diagnosis and rehabilitation assessment based on the gait touch information related to lower limb injuries.
Background:
The current common clinical methods cannot truly reflect the biomechanical status of the knee
joint. The three-dimensional gait analysis is the gold standard, but it is difficult to apply
clinically. There is an urgent need for a clinically practical method to quantitatively
evaluate the biomechanics of the knee joint under dynamic weight bearing.
Methods:
50 healthy volunteers, 450 sports injuries patients (including hip, knee, and ankle joint
diseases) and 50 patients with degenerative osteoarthritis were recruited.
55 passive reflective markers were placed bilaterally on the body. Lower extremity kinematics
and dynamic plantar pressure during walking, jogging were collected.
Outcome evaluation indicators and statistical methods: The following indicators use repeated
measurement two-factor analysis of variance: the left and right sides, different
rehabilitation times are used as repeated measurement variables, to analyze the biomechanical
changes of the lower limb joint biomechanics and gait touch information. A variety of machine
learning methods (such as PCA, SVM, CNN, etc.) are used to analyze, and select the
appropriate algorithm and parameters according to the learning effect. Finally, this study
will establish a machine learning models for computer-aided diagnosis, treatment, and
rehabilitation assessment.
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