Walking Cycle and Walking Phases Extraction from Videos using Transfer Learning

Young patients with neurological pathologies develop walking inadequacies. These inadequacies are due to a strong contraction of the muscle at three levels: pelvis, knee, and foot. Patients can have surgery only once adulated. Before that, physicians, after observing the patient walking and detecting the zone of disorder, inject toxin in one of the three levels in order to relax the muscle. The detection of trouble areas is done by extracting walking cycles and walking phases, then calculating angles formed between the different joints at key moments in the walking phases. With the naked eye, it is difficult for the physician to detect each phase in the walking cycle of patients and to extract angles of inclination. As a solution, the physician takes frontal and sagittal videos of patients and analyzes them offline. The objective of this work is to extract and classify automatically the walking phases of a walking cycle to help physicians in their task. This process is made automatic thanks to transfer learning of VGG16 network. Extensive experiments compare the different Network settings in order to study the integration of the model in clinical routines.