## Editorial for Special Issue: Contactless Vital Signs Monitoring
and [PERSON] et al., respectively. This is possibly driven by the fact that these two parameters are an early indicator of patient/subject and thus more relevant than SpO\({}_{2}\). Moreover, heart and respiration rate may be measured with quite different technologies including capacitive ECG, radar, image-based motion, and rPPG, which makes benchmarking these technologies relevant. Only two may be characterized as at least having an imaging component [4; 5]. Most papers aim at measuring vital signs. This is interesting since the camera by inception, is primarily an imaging instrument. It is becoming overwhelmingly obvious that cameras should also be seen as sensors where the imaging capabilities (choosing region of interest, etc.) are only a supporting functionality. We chose to categorize the papers into three categories: (1) fundamental/new sensors; (2) applications; and (3) algorithmic. In the first category, [PERSON] et al. [4] report on how the very source of the rPPG signal (pulsatile dermal blood vessels) can be dramatically influenced by exposing the skin to temperature changes and envision how this may help patients with increased cardio-metabolic risk. One paper addresses a fundamental aspect of contactless pulse-oximetry. Measurement of SpO\({}_{2}\) is arguably more difficult than heart rate since the relative amplitudes of the rPPG signal need to be measured rather than the mere periodicities of rPPG signals. Nevertheless, it has been shown to be possible, even during some patient motion [6]. In their current paper [7], they demonstrate an elegant data-driven way to perform calibration in a less cumbersome way than earlier calibration efforts [8]. In a pioneering contribution by [PERSON] et al. [9] a non-camera sensor, a thermopile array, was demonstrated to be capable of monitoring respiratory flow. Using smart signal processing, this inexpensive device may be an attractive, privacy friendly, alternative to thermal cameras. In the second category, two papers aimed at the robust measurement of heart and/or respiration rate for automotive applications, with the aim to monitor the well-being of the driver and possibly predict deterioration to prevent accidents. [PERSON] et al. [10] used non-camera technology: radar sensors and capacitively coupled ECG. They presented both architectural and signal processing innovations to separate physiological motion from driving induced motion. Promising experimental results were reported for real driving conditions on public roads. With the same goal, measuring the bio-signals of drivers, [PERSON] et al. [11] used rPPG and proposed image and signal processing techniques to deal with challenges such as ambient light variations, vibrations, and subject motion. In the third category, it is unsurprising that neural networks have been used to enhance the information extraction from video images, rather than more classical image and signal processing techniques. [PERSON] et al. [5] used deep neural networks to artificially enhance thermal image sequences with low resolution (40 \(\times\) 40 pixels) to a higher resolution and showed that accuracy of the extracted respiration rate was improved. Whether this technique works with the very low (8 \(\times\) 8) resolution of the thermopile array proposed by [PERSON] et al. [9], remains to be seen. Nevertheless, it is impressive to see how much information can be extracted with very moderate means. Finally, [PERSON] [12] et al. showed that 3D convolutional neural networks are in principle capable of extracting pulse rate from videos of faces as well as identifying the facial regions from which the signals are extracted. While it is difficult to compare with other methods, which are often designed to work under specific illumination and motion conditions, a smaller root mean square error was reported with their method than with other methods (8.64 bpm and 10 bpm, respectively. The power of the network was relatively small, however, and the authors note that 'there is room for improvement'.