The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants’ feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references.

Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.

Low-cost light-scattering particulate matter (PM) sensors are often advocated for the dense monitoring networks. Recent literature has focused on evaluating their performance. Nonetheless, low-cost sensors are also considered unreliable and imprecise. Consequently, exploring techniques for anomaly detection, resilient calibration, and data quality improvement should be discussed more. In this study, we analyse a year-long acquisition campaign by positioning 56 low-cost light-scattering sensors near the inlet of an official PM monitoring station. We use the collected measurements to design and test a data processing pipeline composed of different stages, including fault detection, filtering, outlier removal, and calibration. These can be used in large-scale deployment scenarios where the quantity of sensors’ data can be too high to be analysed manually. Our framework also exploits sensor redundancy to improve reliability and accuracy. Our results show that the proposed data processing framework produces more reliable measurements, reduces errors, and increases the correlation with the official reference.

Light-scattering particulate matter sensors can be very inexpensive, but also unreliable. They are often presented as a solution for the creation of dense monitoring networks, offering an alternative approach to mathematical modeling and interpolation techniques applied to data from institutional monitoring stations. The purpose of this article is to study the benefits provided by light-scattering sensors in measuring daily concentrations of particulate matter, when they are used to complement the existing institutional network of high-quality instrumentation. A one-year experiment campaign was performed, placing 56 light-scattering sensors close to an official monitoring station. The effectiveness of the sensors is evaluated by comparing the correlation of the sensors with the official data of the nearby station, with respect to the correlation between the same station and other stations in the official monitoring network. Finally, the different data sources are calibrated on a single reference station, in order to act as predictors of the PM concentration in the point of interest. Estimates are analyzed using root mean square error (RMSE) comparison.