The experimental evaluation carried out on the large captured dataset shows that the proposed system is highly useful in monitoring the patient's body movements during Vojta therapy. In the final step, a multi-class support vector machine is used to classify these movements. In the second step, a multi-dimensional feature vector is computed to define various movements of patient's body during the therapy. In the first step, patient's body is automatically detected and segmented and two novel techniques are proposed for this purpose. The proposed framework works in three steps. In this paper, we propose a computer vision-based system to monitor the correct movements of the patient during the therapy treatment using the RGBD data. After few therapy sessions, the patient can perform these movements without external stimulation. The repetition of stimulation ultimately brings forth the previously blocked connections between the spinal cord and the brain. In Vojta therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements which the patient is unable to perform in a normal manner. Vojta therapy is considered a useful technique to treat the motor disabilities. cerebral palsy, spinal scoliosis, peripheral paralysis of arms/legs, hip joint dysplasia and various myopathies. Consequently, they introduce some diseases in the human e.g. Neurological disorders may affect the motor neurons, which are associated with skeletal muscles and control the body movement. The experimental evaluation performed on a large knee x-ray dataset shows that our method is able to efficiently detect osteoarthritis, achieving more than 97% detection accuracy.Ī neurological illness is t he disorder in human nervous system that can result in various diseases including the motor disabilities. The knee joint space width is calculated, and the radiographs are classified based on the comparison with the standard normal knee joint space width. The knee region is extracted automatically using template matching. Different image processing techniques have been applied on knee radiographs to enhance their quality. In this paper, we present a computer-vision-based system that can assist the radiologists by analyzing the radiological symptoms in knee x-rays for osteoarthritis. ![]() ![]() Such manual inspections require an expert radiologist to analyze the x-ray image moreover, it is a tedious and time-consuming task. ![]() The primary feature in observing extremity and advancement of osteoarthritis is joint space narrowing (cartilage loss) which is manually computed on knee x-rays by a radiologist. Knee issues are very frequent among people of all ages, and osteoarthritis is one of the most common reasons behind them.
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