## 2 Main Content of the Special Issue
[30] developed a dyskinesia impairment mobility scale (DIMS) for measuring the presence and severity of dystonia and choreoatheotsis during powered mobility (electric wheelchair) in dyskinetic cerebral palsy. Such a scale could be a promising tool in clinical practice for assisting in accelerating the learning process of using a powered mobility wheelchair and for tailoring individualized mobility training programs. [PERSON] et al. [31] studied the possible adverse effects of indiscriminate mobile phone use in Peruvian students, more specifically focusing on mental complaints when having no access to a mobile phone, also known as 'nomophobia' (no mobile phone phobia). Using a self-designed questionnaire, they identified three symptomatic factors of nomophobia. Their work was additionally relevant for this SI in demonstrating also the possible adverse health effects of wearable technology on users in daily life. The second series of articles focuses on research related to modelling of biological signals in the framework of human health. [PERSON] et al. [32] used a mechanistic model to describe forced expiration in patients suffering from chronic obstructive pulmonary disease (COPD). In their work, they demonstrated that data of forced expiration maneuvers in combination with a physical knowledge-based model allowed to estimate airway resistance in the lungs. Such a model-based approach has especially clinical relevance for screening patients for COPD using classical spirometry in primary care. In their work, [PERSON] et al. [33] developed a mechanistic finite element model to simulate forces and stresses inside knee joints during the landing phase in freestyle skiing aerial. These simulations helped quantifying which types of landings are most challenging in terms of ligament damage. Such results have high relevance for training design and nicely demonstrate how models can help preventing injuries in sports. In their study, [PERSON] et al. [34] improved an existing mechanistic cardiac model based on four modified Van [PERSON] oscillators, each representing one of the main natural pacemakers. Their model allowed to reproduce healthy dynamic heart dynamics, as well as pathological rhythms in case of right bundle branch block (RBBB) and left bundle branch block (LBBB). The clinical relevance of such a model is that it allows simulation and evaluation of heart activity and dynamics under different types of pacemaker couplings. The study of [PERSON] et al. [35] contributed to the diagnosis and management of attention-deficit/hyperactivity disorder (ADHD) by developing a tablet-based application (app) software using support vector machine (SVM) approaches from machine learning. Their approach demonstrated that (serious) games in combination with modelling approaches from the field of artificial intelligence can offer a useful tool for first line screening of children for ADHD by their parents and/or teachers. [PERSON] et al. [36] applied a dynamic Box-Jenkins transfer function modelling approach for quantifying the VO2 kinetics in patients with COPD performing a constant working rate test (CWRT). The added value of this work is that it contributes in optimizing clinical tests for objectively quantifying the physical capacity of patients. As such, measuring and modelling the kinetics of metabolic variables in COPD patients can contribute in managing their disease and in preventing hospital admissions due to exacerbations. [PERSON] et al. [37] applied a combination of a data-based transfer function modelling approach with a mechanistic model to develop a so called data-based mechanistic (or grey box) model to describe the thermoregulatory mechanisms of vasoconstriction and vasodilation to control body temperature during localized cooling. In their research, they demonstrated that dynamic data-based models are not only useful for monitoring or controlling complex biological systems, but can also be used to generate new insights (i.e., reverse engineering) in the working of biological control systems.