Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

The exposure to colored light with different wavelengths has proved to affect the physiological functions of the human body. To test such influence, this work was focused on heart rate variability (HRV) analysis associated with different colored light exposure of healthy volunteers. A Photoplethysmography (PPG) wireless sensor network node was employed to acquire information about the cardiac activity, as it proves to be a more convenient and less intrusive method as electrocardiography (ECG). A small group of healthy volunteers were exposed to 10 minutes of blue and red light, preceded and followed by 5 minutes of dark lighting conditions. HRV was acquired during the last 5 minutes of each colored illumination period. The HRV analysis was based on time-domain variables, frequency-domain variables and fractal (nonlinear) analysis. The experimental results from this study show that PPG can be considered as a reliable method to study HRV. The exposure to different colored light conducted to HRV variations that characterize the autonomous nervous system balance. Experimental results obtained for volunteers exposed to different illumination conditions are also included in the paper.

Remote sensing and Virtual Reality (VR) are technologies that create new development opportunities in the field of serious games with application in physiotherapy. Thus, during a physiotherapy training session expressed by a game round the remote sensing of user body motion provides measurements that can be used for objective evaluation of physical therapy outcomes. In this work is presented a serious game for physiotherapy characterized by Kinect natural user interface and a set of VR games developed in the Unity3D. To provide patient electronic health record, game remote configuration as well as for data presentation for physiotherapist a mobile application was developed. Additionally, several training results expressed by upper limb, neck and spine angles are included in the paper.

Music sound has shown to affect brain function and cardiac activity. Based on this premise, this study examines the effects of sound stimulation, such as music and stress noise, on the balance of the autonomous nervous system (ANS). To monitor such effects, physiological data was collected from a group of volunteers to analyze pulse rate variability (PRV), as well as electrodermal activity (EDA). The final goals of this study are supported by the possibility of automatically integrating musical stimuli into a smart environment to potentially lower individual’s stress levels, as well as to improve their well-being. To accomplish this objective, this research involved three different phases. The first was based on the development of a biomedical wearable sensor for measuring PRV and its validation using a reference sensing system. Good correlation results were obtained between both devices. The second consisted of a preliminary study of the effects of ambient, classic and metal music on PRV. Significant variations of PRV were measured between the three music genres. Considering the obtained results in the previous phase and overcoming the limitations presented in it, the third phase was based on a more in-depth study of the effects of musical stimuli and noise stress, through PRV analysis and the measurement of galvanic skin response (GSR). The obtained results suggested that noise stress contributes to the increasing of sympathetic activity. In contrast, ambient music proved to be very effective in increasing parasympathetic activity and stabilizing comfort levels.

The achievement of a healthy indoor environment - an indispensable requirement for human well-being - relies on the establishment of good air quality conditions and thermal comfort levels. This study addresses the development of a healthcare-focused Internet of Things (IoT) system for cardiac assessment using photoplethysmography (PPG) and ballistocardiography (BCG) that can be applied in any indoor environment. Results from its experimental application include the analysis of heart rate variability (HRV) and respiratory status from a group of young adults under different thermal conditions and relative humidity levels. The final goal was to investigate the effects of a short-term exposure of different air temperature and humidity conditions on human thermal comfort, based on the analysis of the autonomic nervous system (ANS) activity. The experiment took place in a regular and non-isothermal office environment. Simulations based on computational fluid dynamics (CFD) were conducted to estimate heat distribution and select the optimal locations for the environmental sensor nodes’s placement. Results indicated that an activation of the parasympathetic system was more notable in short-term exposures to environments with different air temperatures (24°C – 30°C) than with different levels of relative humidity (50% - 70%). Changes in ambient air temperature led to the activation of thermal regulatory reflexes and thermal discomfort, perceived by an increase of the ratio of low frequency to high frequency (LF/HF) at higher air temperature exposures. Additionally, HRV parameters were used to train machine learning models on predicting if the user is at a thermally comfortable environment or discomfortable environment (hot or humid), having achieved an 87% average accuracy.

In this paper, an indoor air quality monitoring system based on a wireless sensor network (WSN) and compatible with the internet of things (IoT) is presented. By providing relevant information about indoor air conditions, the system may help to prevent the triggering of respiratory distresses, such as an asthma crisis, by highlighting critical air quality conditions. The sensor network contains several sensing nodes with temperature, humidity, gas and particulate matter sensors, and a set of actuators that will help improve indoor's thermal comfort and lighting quality. A Web-based application was developed to provide data analysis, system control and alert mechanisms for asthma attacks prevention. A smart lighting system was implemented to generate visual alert mechanisms and improve indoor's lighting comfort.