Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment.

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

The article describes the results of the project “open source smart lamp” aimed at designing and developing a smart object able to manage and control the indoor environmental quality (IEQ) of the built environment. A first version of this smart object, built following a do-it-yourself (DIY) approach using a microcontroller, an integrated temperature and relative humidity sensor, and techniques of additive manufacturing, allows the adjustment of the indoor thermal comfort quality (ICQ), by interacting directly with the air conditioner. As is well known, the IEQ is a holistic concept including indoor air quality (IAQ), indoor lighting quality (ILQ) and acoustic comfort, besides thermal comfort. The upgrade of the smart lamp bridges the gap of the first version of the device providing the possibility of interaction with the air exchange unit and lighting system in order to get an overview of the potential of a nearable device in the management of the IEQ. The upgraded version was tested in a real office equipped with mechanical ventilation and an air conditioning system. This office was occupied by four workers. The experiment is compared with a baseline scenario and the results show how the application of the nearable device effectively optimizes both IAQ and ILQ.