A deep learning framework-based exercise assessment for rehabilitation of chronic obstructive pulmonary disease

Abstract

Chronic obstructive pulmonary disease (COPD), which has a high prevalence and mortality rate, is an irreversible condition marked by airflow restriction with different degrees of reversible damage. Notably, there is no cure for COPD, whose treatment primarily relies on rehabilitation exercises to improve airflow limitation. In this paper, a vision-based rehabilitation exercise efficacy prediction system is proposed to assess the efficacy of rehabilitation training for COPD patients. A camera was utilized to capture rehabilitation training videos of COPD patients, and we also collected various physical indicators. In addition, we used clustering algorithm to divide patients with different rehabilitation effects for subsequent progression analysis. Our model achieved a classification of rehabilitation progress accuracy of 90.6%, making it possible to effectively obtain favorable rehabilitation training results without physician supervision. It was meaningful for helping COPD patients get effective feedback when training alone.