Data processing and wearable systems for effective human monitoring

The last few decades have seen an unrestrained diffusion of smart-integrated technologies that are extremely pervasive and customized based on humans’ environments and habits. Wearable and mobile technologies such as smartphones, smartwatches, lightweight sensors, textile-based support systems, flexible displays, and micro-cameras are now supplied with a significant amount of computational power, low-energy wireless communication, long-life battery, and large-memory storage that make them a valid platform for monitoring the everyday life of humans [1]. In this context, a large variety of new sensors are being developed to equip such well-established wearable and mobile technologies with the aim of continuous monitoring of physical behavior, emotional state, well-being, and health condition. Interestingly, the recently improved computational resources of mobile systems allow us to acquire, process, and communicate a large set of different information. Nevertheless, this confronts us with the chance and challenge of managing an impressive amount of heterogeneous data, including physiological signals, through new ad-hoc processing, synthesis methods, and big data analysis as well as ad-hoc experimental paradigms, system designs, and models.