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Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral. 

The most effective strategy for homes to save energy is by decreasing their electricity consumption. Home Energy Management Systems connect appliances that improve households’ energy performance to thermal comfort. These systems need to take into account human behavior regarding saving energy and thermal comfort. This paper proposes a three-step framework that integrates the Smart Residential Load Simulator (SRLS), Adaptive-Network based on Fuzzy Inference System (ANFIS), and a gamification structure to develop an interface designed to reduce energy consumption without losing thermal comfort. Finally, a gamified mock-up for mobile devices is displayed for a household with high energy consumption levels and a temperature setpoint of 23 ℃. This proposal integrates the concept of social products to empower the interaction between devices and end-users. 

Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. In this review, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.