The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. As aspect of daily life that is often overlooked in maintaining a healthy lifestyle is the air quality of the environment. In this paper we investigate the use of machine learning technologies to predict CO(2) levels as an indicator of air quality in smart environments. We introduce techniques for collecting and analyzing sensor information in smart environments and analyze the correlation between resident activities and air quality levels. The effectiveness of our techniques is evaluated using three physical smart environment testbeds.

中文翻译：

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预测智能环境中的空气质量

智能环境中无处不在的传感技术为监控和协助在这些空间中生活和工作的个人提供了前所未有的机会。作为日常生活的一个方面，在保持健康的生活方式时经常被忽视的是环境的空气质量。在本文中，我们研究了使用机器学习技术来预测 CO(2) 水平作为智能环境中空气质量的指标。我们介绍了在智能环境中收集和分析传感器信息的技术，并分析了居民活动与空气质量水平之间的相关性。我们的技术的有效性是使用三个物理智能环境测试平台来评估的。