Technologies for Stress and Wellbeing Monitoring

This chapter explores strategies to increase well-being and reduce stress by analyzing the effects of external stimuli on physiological responses. It examines musical sound stimulation and its impact on stress and well-being by analyzing heart rate variability (HRV), a key indicator of autonomic nervous system (ANS) balance. It also presents a healthcare-centered Internet of Things (IoT) system for cardiac assessment using photoplethysmography (PPG) and ballistocardiography (BCG). Experimental results from indoor applications evaluate HRV and respiratory patterns under varying temperature and humidity conditions to assess their short-term impact on thermal comfort. In addition, this chapter also presents studies conducted through the implementation of new PPG signal processing algorithms to extract several physiological markers simultaneously, such as heart rate (HR), HRV, respiratory rate (RR) and blood oxygen saturation (SpO2), through the implementation of multichannel detection systems with distributed computing platforms. New models for the classification and quantification of stress levels are also discussed, using artificial intelligence techniques such as fuzzy logic and more complex algorithms. Furthermore, the chapter addresses the development of mobile applications, serving as a patient support tool and also as a way of acquiring important markers, from which it is possible to draw correlations between stress, blood pressure and blood glucose levels. As a way of trying to counteract the effects of stress on patients' health, studies on alternative techniques for continuous relaxation are also discussed, such as the use of holistic medicine and aromatherapy.