Vojta-therapy is a useful technique to treat the disorders in the central nervous and musculoskeletal system. During the therapy, a specific stimulation is given to the patients in order to cause the patient's body to perform certain reflexive pattern movements. The repetition of this stimulation ultimately brings forth the previously blocked connections between the spinal cord and brain, and after a few sessions, patients can perform these movements without any external stimulation. In this paper we proposed an automatic vision-based monitoring system for accurate therapy. We proposed an infant's (i.e., patient) detection and recognition of specific movements in his/her various body parts during the therapy process, using RGB-D data. First, A robust template matching based algorithm is exploited for infant's detection using his/her head location. Second, various features are computed to capture the movements of different body parts during the therapy. In the classification stage, a multi-class support vector machine (mSVM) is used to classify the accurate movements of infant during the therapy process, which ultimately reveals the correctness of the given treatment. The proposed algorithm is evaluated on our challenging dataset, which was collected in a children hospital. The detection and classification results show that the proposed method is highly useful to recognize the correct movement pattern either in hospital or in-home therapy systems.
['Muhammad Hassan Khan', 'Julien Helsper', 'Cong Yang', 'Marcin Grzegorzek']
An automatic vision-based monitoring system for accurate Vojta-therapy