由于智能移动设备的蓬勃发展和人们对于自身健康状况的高度关注,通过智能移动设备监测身体指标和健康状况正逐渐成为一个研究热点.血氧饱和度是指血液中氧合血红蛋白在所有血红蛋白中的比例,它是呼吸系统和循环系统的重要生理参数之一,可以反映相关人群的病情变化及身体健康情况等.一般传统血氧饱和度的检测方法需要红外光的支持,而目前的智能移动设备没有红外光发射和接收模块,鉴于此,研究了面向仅具有摄像头和可见光源的移动设备血氧饱和度检测方法.通过分析传统光学模型直接应用于移动设备后存在的问题,提出全新的面向移动设备的血氧饱和度检测模型,并研究其中必备的摄像头成像基线漂移问题的修正算法.提出的模型和方法可以支持许多与血氧饱和度检测相关的应用,并启发相关的人机交互研究.

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.

国家自然科学基金(61422212, 61232013, 61170182, 61303162);国家高技术研究发展计划(863)(2015AA020506)