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Indoor air quality pollution [1][2] represents one of the factors associated with the etiology of chronic obstructive pulmonary disease and also plays an important role in respiratory distress, the second most common symptom of adults that request emergency transportation to the hospital and is associated with a relatively high overall mortality before hospital discharge [3][4][5][6]. The prevention of acute respiratory distress or asthma attacks can be possible by monitoring the air quality conditions using distributed smart sensing systems characterized by accuracy, short time response, and robustness as well as by data processing, data logging and data communication capabilities. Considering the importance of indoor air quality monitoring, different distributed measuring system architectures and associated calibration methods and systems are presented in the literature [7][8][9]. The main elements of these kind of systems are not only temperature and relative humidity sensors, but also gas detectors and gas concentration sensors whose metrological characteristics, such as accuracy and linearity are very limited, which implies the design and implementation of signal processing algorithms namely for numerical linearization and common factors correction [10][11][12]. Taking into account the indoor spatial distribution of the temperature and relative humidity values as well as the concentration values of pollutants (e.g CO, CO2 resulting of combustion), the development of distributed measuring systems [13][14] that can include personal computers (PCs) or mobile devices (e.g. PDAs [15] or smart phones [16]) based human-sensing system interface represents an important requirement for optimal indoor air quality monitoring. This chapter presents a practical approach concerning distributed smart sensing solutions for air quality monitoring, highlighting the original contributions of the authors in this area. The first part of the chapter deals with the relation between the subject’s health status, respiratory distress condition and air quality conditions. The second part contains a brief presentation of solid state sensors [17] that materialize the sensing component of air quality monitoring systems and the third part presents a distributed architecture based on an

Distributed Smart Sensing Systems for Indoor Monitoring of Respiratory Distress Triggering Factors