Posture recognition in HINE exercises

Abstract

Pattern recognition, image and video processing based automatic or semi-automatic methodologies are widely used in healthcare services. Especially, image and video guided systems have successfully replaced various medical processes including physical examinations of the patients, analyzing physiological and bio-mechanical parameters, etc. Such systems are becoming popular because of their robustness and acceptability amongst the healthcare community. In this paper, we present an efficient way of infant’s posture recognition in a given video sequence of Hammersmith Infant Neurological Examinations (HINE). Our proposed methodology can be considered as a step forward in the process of automating HINE tests through computer assisted tools. We have tested our methodology with a large set of HINE videos recorded at the neuro-development clinic of hospital. It has been found that the proposed methodology can successfully classify the postures of infants with an accuracy of 78.26 %. © Springer Science+Business Media Singapore 2017.