**Contents**



## **About the Editor**

**Jean-Marie Aerts** holds a Master of Science and a PhD degree in Bioengineering from the KU Leuven (former Catholic University of Leuven) in Belgium. Currently, he is leading the Measure, Model & Manage Bioresponses (M3-BIORES) Group of the Division Animal and Human Health Engineering (A2H) in the Department of Biosystems (BIOSYST) at KU Leuven. He is full professor and chairman of Leuven Health Technology Centre (L-HTC) and of the Department of Biosystems. His research focuses on data-based mechanistic modelling of biological systems as a basis for the development of human health engineering technology. Jean-Marie Aerts is an IEEE member and has been a visiting researcher at the Engineering Department of Lancaster University and at the Institute of Biomedical Engineering of the University of Oxford.

#### *Editorial* **Special Issue on "Human Health Engineering Volume II"**

**Jean-Marie Aerts**

> KU Leuven, Department of Biosystems, Division Animal and Human Health Engineering, Measure, Model & Manage Bioresponses Group, Kasteelpark Arenberg 30, 3000 Leuven, Belgium; jean-marie.aerts@kuleuven.be

#### **1. Referees for the Special Issue**

A total of 23 manuscripts were received for our Special Issue (SI), of which 3 manuscripts were directly rejected without peer review. The remaining 20 articles were all strictly reviewed by no less than two reviewers in related fields. Finally, 12 of the manuscripts were recommended for acceptance and published in *Applied Sciences-Basel*. Referees from 16 different countries provided valuable suggestions for the manuscripts in our SI, the top five being the USA, Italy, UK, DE, and Spain. The names of these distinguished reviewers are listed in Table A1. We would like to thank all of these reviewers for their time and effort in reviewing the papers in our SI.

#### **2. Main Content of the Special Issue**

As explained by Aerts [1], we are standing on the shoulders of giants when looking to the development of the research field of Human Health Engineering, i.e., technology for monitoring the physical and/or mental health status of individuals in a variety of applications, as it evolved from seminal work of, among others, Walter Cannon [2], Norbert Wiener [3], and Ludwig von Bertalanffy [4]. For more detailed information on the background and history of the field of Human Health Engineering, the reader is referred to the editorial of Aerts [1] written for the first volume of the special issue on Human Health Engineering.

The 12 presented papers in this second volume are grouped according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing of physiological variables [5–8], papers describing health monitoring applications [9–12], and finally, examples of control applications for human health [13–16]. As indicated by Aerts [1], it is envisioned that the field of human health engineering is complementary to the field of biomedical engineering as it not only contributes to developing technology for curing patients or supporting chronically ill people, but also covers applications on healthy humans (e.g., sports, sleep, and stress) and thus also focuses on disease prevention and optimizing human well-being more generally.

The first series of articles in this SI describes methods for (improved) measuring and/or sensing of health-related physiological signals. The work of Arciniega-Montiel et al. [5] contributes to developing methods for improving the measuring reliability of fetal monitors, used in the case of high-risk pregnancies, on the basis of probabilistic models. Al-Halhouli et al. [6] demonstrated the feasibility of using a wearable and stretchable inkjet-printed strain gauge sensor for estimating respiratory rate, which is a key vital sign variable in many medical and health applications. In their study, Alsayed et al. [7] developed an automated data acquisition system for measuring pinch and pulling forces in patients recovering from stroke. Nishio et al. [8] investigated a method for quantifying the functional decline in proprioceptors in patients with low back pain using vibrations with sweep frequencies covering the entire range of response frequencies of proprioceptors.

The second series of articles describes applications of human health monitoring. In their paper, Cheng et al. [9] described a method combining an electroencephalography

**Citation:** Aerts, J.-M. Special Issue on "Human Health Engineering Volume II". *Appl. Sci.* **2021**, *11*, 7844. https://doi.org/10.3390/app11177844

Received: 3 August 2021 Accepted: 23 August 2021 Published: 26 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

image-based method with a convolutional neural network to predict drowsiness in drivers, which is a major cause of vehicle accidents. Yang et al. [10] developed a biometrics monitor allowing to verify individuals on the basis of an interval-based linear discriminant analysis algorithm applied to electrocardiogram signals. Visnovcova et al. [11] used linear and non-linear analyses of electrodermal activity signals for quantifying sympathetic dysregulation as a non-invasive potential biomarker for monitoring anorexia nervosa-linked cardiovascular risks. In the paper of Jin et al. [12], a system is described for monitoring gait in (elderly) people using toe-area activity as a predictor for stumbling risk.

Finally, the third series of articles in this SI discusses examples of control applications for human health management. Kaçar et al. [13] developed a prototype of a closed-loop controlled valveless piezoelectric insulin pump for keeping the blood glucose level of Type 1 diabetes mellitus patients within desired ranges. In their paper, Trappey et al. [14] developed a system for virtual reality-based exposure therapy for treating people suffering from driving phobia disorder using bioresponse measurements to optimise fear-based driving scenarios. Guttiérez et al. [15] reported about a serious game platform with haptic feedback and EMG monitoring developed for upper limb rehabilitation of spinal cord injury patients. Aprile et al. [16] demonstrated the feasibility of optimising the efficiency of a robot-assisted rehabilitation area for restoring upper limb function, allowing one physiotherapist to supervise four patients.

## **3. Conclusions**

This SI presents recent contributions in the growing field of human health engineering. The contributions highlight research focusing on different aspects of the monitoring and control engineering scheme (sensors, sensing systems, monitoring approaches, and control algorithms) as applied to human health. Applications cover both healthy and (chronically) ill people and this is in relation to physical as well as mental processes.

The combination of ever growing possibilities in sensors and (wearable) sensing technology and powerful artificial intelligence tools is expected to further boost the field of human health engineering, which will become increasingly ubiquitous in our society and will increasingly assist people of all ages in living healthy, high quality, and productive lives.

**Funding:** This special issue frames within research funded by the European Union's Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie gran<sup>t</sup> number 645770.

**Acknowledgments:** We would like to sincerely thank our Section Managing Editor, Marin Ma (marin.ma@mdpi.com), for all the efforts she has made for this special issue in the past year.

**Conflicts of Interest:** The author declares no conflict of interest.

## **Appendix A**

**Table A1.** Special Issue (SI) reviewer list.

