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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.

Positively advocating that low-cost additive 3D-printing technologies and open-source licensed software/hardware platforms represent an optimal solution to realize low-cost equipment, a mechanical and 3D-printable device for bilateral upper-limb rehabilitation is presented. The design and manufacturing process of this wheel-geared mechanism, enabling in-phase and anti-phase movements, will be openly provided online with the aim of making a set of customizable devices for neurorehabilitation exploitable all over the world even by people/countries with limited economical and technological resources. In order to characterize the interaction with the device, preliminary trials with EMG and kinematics recordings were performed on healthy subjects., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.