## 2 Main Content of the Special Issue
In their review article, [PERSON] et al. [38] describe methodological aspects related to the construction of a federated facility to optimize the analyses of multiple datasets, the impact of missing data and methods for handling missing data in cohort studies. The described database management systems permits the increase of the statistical power of medical multi-center studies, allowing for more advanced statistical analyses and answering research questions that might not be addressed by a single study. The third series of articles describes applications of human health monitoring. [PERSON] et al. [39] showed in their work the possibilities and added value of integrating wearable sensors (heart rate, air temperature, and air humidity) in bicyclists' helmets. They developed a prototype of a smart helmet for monitoring thermal comfort based on adaptive personalized models. Such technology could be used in a next step to actively control thermal comfort of cyclists' head. Another thermal comfort application was studied in the work of [PERSON] et al. [40]. Here, the authors focused on developing a system for online personalized monitoring of thermal sensation. They used measurements of heart rate, metabolic rate, skin temperature, heat flux between skin and ambient air, and aural temperature in combination with least-squares support vector machine algorithms to estimate an individual's thermal sensation. The combination of these algorithms with wearable sensors allow, in a next step, to develop personalized indoor climate control systems by integrating online information from the occupants. [PERSON] and [PERSON] [41] used physiological variables linked to thermoregulation to develop a drowsiness monitor for drivers. Since the process of falling asleep is accompanied by a shift/decrease in body temperature, online estimations of heat loss and heat production of drivers' can be used to monitor drowsiness. In their work, they measured heart rate, as indirect estimate of heat production, and nose tip and wrist temperature, as an indirect estimate of heat loss, to monitor changes in driver's thermoregulation. This proof-of-principle shows that wearable technology in combination with algorithms from machine learning can contribute to traffic safety. In a another health monitoring study, [PERSON] et al. [42] demonstrated that a set of five vital signs (heart rate, respiration rate, oxygen saturation, arterial blood pressure, and body temperature) measured bi-hourly on patients in an intensive care unit (ICU) could be used to predict mortality. They combined a linear hard margin support vector machine (SVM) with a feature engineering approach to classify survivors and non-survivors during their stay at the ICU, demonstrating the added value of combining vital signs monitoring with machine learning for monitoring critically ill patients. Finally, the fourth series of articles in this SI discusses examples of control applications for human health management. The study of [PERSON] et al. [43] describes a model predictive controller (MPC) approach for managing human bodyweight using energy intake as the control input. Based on the data of the Minnesota starvation experiment, they show that the combination of weekly bodyweight and energy intake measurements with model predictive control theory allows to calculate future energy intake for following a predefined bodyweight trajectory. This nicely demonstrates how control theory could have a significant contribution in solving one of the major health challenges of today, namely obesity. However, the same technology could also be used to help elderly people suffering from underweight conditions. Another application of MPC is described by [PERSON] et al. [44]. In their work, they demonstrate that MPC can also be successfully used to design smart four-wheeled rollators for elderly and/or disabled people. Their work focused on developing a lateral stability control for four-wheeled rollators allowing users to move in more smooth trajectories.