With the fast development of mobile devices and great concern on health surveillance, it is becoming increasingly popular to collect, analyze, and interpret people's health-related data by using intelligent mobile devices in their daily life. Oxygen saturation is an important physiological parameter referring to the concentration of oxygen in the blood, and prolonged low oxygen levels may lead to respiratory or cardiac arrest. Previous oxygen saturation detection methods require infrared light, however most of the off-the-shelf mobile devices lack such infrared light transmitter and receiver modules. This paper presents a novel oxygen saturation estimation method emplying a RGB camera and visible light in most of mobile devices. By applying of traditional optical oxygen saturation estimation model to mobile devices, and analyzing its problem for those devices, this study proposes a new oxygen saturation estimation model on intelligent mobile devices and offers an approach to solve the baseline drift problem in mobile cameras. Experiments demonstrate the effectiveness of the proposed method and its potentials in many oxygen saturation based researches such as daily-activity based healthcare with mobile devices.