## Editorial for Special Issue: Contactless Vital Signs Monitoring  
[PERSON] and [PERSON],*()  
1 Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; [EMAIL_ADDRESS]
2 Remote Sensing, Philips Research Laboratories, 5656 AE Eindhoven, The Netherlands
* Correspondence: [EMAIL_ADDRESS]  
Received: 16 December 2019; Accepted: 20 December 2019; Published: 24 December 2019  
Keywords:remote vital sign monitoring; camera-based photo-plethysmography; video health monitoring, artificial intelligence  
Cameras have changed our way of life in many ways. Billions of images are being sent across the world every day for leisure or news purposes. Cameras are also well on their way to revolutionize health care, particularly when combined with artificial intelligence (AI). For example, [PERSON] et al. [1] demonstrated that images from consumer level devices combined with AI contained impressive, if not superior, diagnostic value for skin cancer classification. [PERSON] [2] also acknowledged image interpretation as one of the three levels where AI is having a major impact on medicine. He also notes that bias, privacy, and security are important limitations, however. We feel that it is important to also emphasize the importance of these three factors in our limited sphere of influence as mere technology developers. When video streams rather than static images are considered, not only is diagnosis but also patient monitoring in scope: a large number of vital signs can be measured with consumer level cameras. With the use of cameras other than conventional color cameras (e.g., thermal or multi-spectral), the complete pallet of vital signs appears to be within reach. While the sensitivity and accuracy may not necessarily be superior (yet) when compared to conventional contact probes, the potential advantages are numerous and diverse. [PERSON] et al. [3] list these advantages in an excellent review paper in this Special Issue: \"... robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups.\" Arguably, it is an understatement to say that cameras will improve healthcare, make it more affordable, and available to more people. Rather than listing all of the health care settings where cameras will have an impact, we refer to the review paper by [PERSON] et al. for a comprehensive overview. Cameras are likely to change health care beyond replacing conventional medical diagnostics and the monitoring of vital signs. Patient status beyond traditional vital signs (e.g., delirium detection, pain) and hospital workflow will also benefit from camera monitors on the work floor. It is conceivable that even an overall health status will consist less of an array of 'numbers' from probes that measure traditional vital signs, and increasingly of metrics derived from AI. In this Special Issue, however, the focus is on the traditional vital signs (i.e., respiration, heart rate, oxygen saturation, blood pressure, core temperature), but we also welcomed contributions on adjacent health parameters measured unobtrusively, ranging from tissue perfusion and hydration to actigraphy and sleep-staging. Additionally, the sensor does not need to be a camera, as long as the measurement is fundamentally contactless. We were fortunate to receive many high quality papers, of which 8 madethe selection for this Special Issue and we are grateful to the authors for submitting their work to _Applied Sciences_. Despite the focus on traditional vital signs, the papers cover a wide range of topics. Measurement principles include capacitive electro-cardiography (ECG), Doppler radar, remote photo-plethysmography (PPG), and image-based motion. The number of vital signs addressed in the papers is largely limited to heart rate and respiration rate, although SpO\({}_{2}\) and PPG amplitude as a perfusion parameter are addressed in two papers by [PERSON] et al.