Automatization of a protocol for the postural assessment of patients with Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and partially reversible airway obstruction. Gait is often used to analyze and monitor the progression of diseases that interfere with the posture and locomotion such as COPD, however, due to dyspnea and skeletal muscle weakness presented by some patients with COPD, activities related to gait are avoided. In this case, the evaluation can be made by analyzing the movements related to other daily activities such as stand-to-sit and sit-to-stand. The aim of this study is to automatically identify a group of nine anatomical points used by a postural assessment protocol for lateral view of the stand-to-sit and sit-to-stand activity, allowing the extraction of information necessary for the protocol at any time of the execution of the activity. The proposed algorithm employs Digital Image processing techniques such as morphological filtering and image segmentation to identify anatomical points marked in the COPD patients with reflective markers. Comparing the anatomical points marked on the patient and the points detected by the proposed algorithm in 30 videos demonstrated that only two points were not identified in some frames and in 70% of the videos the algorithm detected all of the marked points. The automatization of the postural assessment protocol performed by the proposed algorithm assists the physiotherapist in obtaining a quantitative method for monitoring the evolution of the patient's posture and enables periodic reviews to be made more quickly, accurately and throughout the physical therapy treatment.