## 2 Main Content of the Special Issue  
In the 1930s [PERSON] introduced the term 'homeostasis', reflecting the idea that complex organisms, such as humans, are able to maintain their internal environment quasi-constantly in the face of (external) perturbations [1]. Although homeostasis was originally not discussed in terms of regulatory mechanisms, this changed in the 1950s and 1960s with the work of pioneers such as [PERSON] and [PERSON] who envisioned complex organisms as complex systems that can be studied from an engineering system and/or control perspective [2; 3; 4]. This has been the start of the fast growing field of health engineering that uses concepts from control theory and fault-detection and diagnosis to study, monitor, and/or control health processes [2; 5; 6]. According to modern model-based monitoring and control theory there at least two conditions necessary to study and develop monitoring and control systems, namely continuous information of the relevant process variables (inputs and outputs) and a (mathematical) model describing the relationships between the process inputs and outputs [6; 7]. The first condition stresses the need for sensors and sensing systems that allow capturing the necessary information on the considered biosystem, and the latter assumes access to advanced mathematical modelling approaches. The field of sensor development and sensing systems for biological signals has evolved tremendously the last couple of decades and has resulted in advanced sensing elements that can, among others, harvest their own energy from the human body [8], measure real-time physiological information in biofluids, such as sweat, tears, saliva, and interstitial fluid [9; 10], or continuously measure physiological variables (e.g., activity, heart rate, respiratory rate, and body temperature) using wearable technology [11]. In addition to this, the Internet of Things has also made it possible to interconnect many of these sensing systems with the internet, allowing the capture and exchange of information between connected devices, data storage systems, and relevant stakeholders [12]. The field of signal analysis and modelling has also evolved during recent decades. Although the more classic approaches of mechanistic modelling and data-based (or empirical) modelling are still very relevant and offer yet unexploited added value for health engineering (e.g., [13; 14; 15]), it can be expected that recent trends in artificial intelligence (e.g., big data approaches and deep learning algorithms [16; 17]) will change the health engineering landscape (and life in general) in a radical way. The fusion of sensing systems with powerful (real-time) modelling algorithms creates opportunities to, among others, monitor chronic patients at home [18], connect patients within a smart city environment with relevant caregivers [19], or even actively control physiological variables using smart actuators, such as wheelchairs [20] or electric bikes [21], to enhance physical condition of thermal comfort [22]. Last, but not least, also human-robot, interactions can be considered as means for actively controlling human health conditions, e.g., by supporting human gait using exoskeletons or exosuits [23]. In this special issue on \"Human Health Engineering\", we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions can focus on sensors, wearable hardware, algorithms, or integrated monitoring systems.