Abstract

Although polysomnography is a generally adopted approach for diagnosing obstructive sleep apnoea (OSA), there are several critical drawbacks with it, including massive equipment cost, large expense on replacing damaged components and more importantly invasive devices required to be worn while patients are struggling to sleep. Furthermore, there is no proof that polymonography obtains higher accuracy in detecting patients with OSA than more simple investigations [1]. Video monitoring has been adopted to assist diagnosis on obstructive sleep apnoea. From practical researches [3], the best predictors of morbidity in individual patients, as assessed by improvements with CPAP therapy, are nocturnal oxygen saturation [4, 5] and movement during sleep [4]. Hence, we purpose a robotic, objective and reliable video monitoring system with AI intelligence for analysis on human behavior during sleep, automatically generating a statistics report on body activity, including arm movement, limb movement, head movement and body rotation movement and arousal movement.