The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

Our results confirm a local vasodilatory effect of applied CO2 therapy. This finding indicates its potential clinical use.

Objectives: Remote photoplethysmography (rPPG) is a promising non-contact measurement technique for assessing numerous physiological parameters: pulse rate, pulse rate variability (PRV), respiratory rate, pulse wave velocity, blood saturation, blood pressure, etc. To justify its use in ultra-short-term (UST) PRV analysis, which is of great benefit for several healthcare applications, the agreement between rPPGand PPG-derived UST-PRV metrics was studied. Approach: Three time-domain metrics-standard deviation of normal-to-normal (NN) intervals (SDNN), root mean square of successive NN interval differences (RMSSD), and the percentage of adjacent NN intervals that differ from each other by more than 50 ms (pNN50)-were extracted from 56 video recordings in a publicly available data set. The selected metrics were calculated on the basis of three groups of 10 s recordings and their average, two groups of 30 s recordings and their average, and a group of 60 s recordings taken from the full-length recordings and then compared with metrics derived from the corresponding reference (PPG) pulse waveform signals by using correlation and effect size parameters, and Bland-Altman plots.Main results: The results show there is stronger agreement as the recording length increases for SDNN and RMSSD, yet there is no significant change for pNN50. The agreement parameters reach r = 0.841 (p < 0.001), r = 0.529 (p < 0.001), and r = 0.657 (p < 0.001), estimated median bias −1.52, −2.28 ms and −1.95% and a small effect size for SDNN, RMSSD, and pNN50 derived from the 60 s recordings, respectively. Significance: Remote photoplethysmography-derived UST-PRV metrics manage to capture UST-PRV metrics derived from reference (PPG) recordings well. This feature is highly desirable in numerous applications for the assessment of one's health and well-being. In future research, the validity of rPPG-derived UST-PRV metrics compared to the gold standard electrocardiography recordings is to be assessed.

Chronic wounds in diabetics are difficult to treat, therefore, adjuvant therapies have been investigated. Bathing in CO2‐rich water (spa therapy) has been known in Europe for decades for its positive effect on peripheral vascular disorders. Recently, much effort has been invested in developing optimal application methods of CO2. Uses include subcutaneous injections of CO2, bathing in CO2‐enriched water, and transcutaneous application of CO2. To verify the effect of transcutaneous application of gaseous CO2 on the healing of chronic diabetic wounds, a randomized double‐blind clinical research was designed. The research included 30 and 27 wounds in the study and control groups, respectively. In addition to standard treatment, patients in the study group received 20 therapies with medical‐grade CO2 gas and the control group received the same treatment with air. Results showed significantly faster healing in the study group: 20 of the 30 wounds in the study group were healed compared with none in the control group. Mean wound surface and volume in the study group was reduced significantly (surface: 96%, P = .001, volume: 99%, P = .003) compared with a small reduction in the control group (surface: 25%, P = .383, volume: 27%, P = .178). Considering our results, transcutaneous application of gaseous CO2 is an effective adjuvant therapy in diabetic chronic wound treatment.

In this paper we present a study of vision-based, human-recognition solutions in human-oriented, mobile-robot applications. Human recognition is composed of detection, tracking and identification. Here, we provide an analysis of each step. The applied vision systems can be conventional 2D, stereo or omnidirectional. The camera sensor can be designed to detect light in the visible or infrared parts of the electromagnetic spectrum. Regardless of the method or the type of sensor chosen, the best results in human recognition can be obtained by using a multimodal solution. In this case, the vision system is enhanced with other forms of sensory information. The most common sensors are laser range finders, microphones and sonars. As medicine is expected to be one of the main fields of application for mobile robots, we give it special emphasis. An overview of current applications and proposal of potential future applications are given. Without doubt, properly controlled mobile robots will play an ever-increasing role in the future of medicine.