Automatic recognition of movement patterns in the vojta-therapy using RGB-D data

Vojta-therapy is a useful technique for the treatment of physical and mental impairments in humans, and is very effective for children of less than 6 months. During the therapy, a specific stimulation is given to the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately makes the previously blocked connections between the spinal cord and brain available, and after a few session, patients can perform these movements without any external stimulation. The treatment must be performed several times a day or week and can last for a few weeks or months. Therefore, the therapists may recommend an at-home continuation of the therapy. An automatic vision-based system is required which can analyze and verify the correct pattern of a patient's body parts movement during the therapy process at home, ultimately revealing the accuracy of given treatment. We captured a dataset of more than 15,000 images from a Microsoft Kinect camera and a novel segmentation technique in RGB-D data is proposed to segment the patient's body region from the scene using a k-means clustering algorithm. The movement patterns of a patient's body parts are analyzed and a support vector machine (SVM) is trained to classify the correct movements. The classification results show that the proposed method is highly useful to recognize the correct movement patterns.