Unobtrusive Vital Sign Monitoring in Automotive Environments-A Review

Heart Rate Variability for Driver Sleepiness Classification in Real Road Driving Conditions

Approximately 20-30% of all road fatalities are related to driver sleepiness. A long-lasting goal in driver state research has therefore been to develop a robust sleepiness detection system. Since the alertness level is reflected in autonomous nervous system activity, it has been suggested that various heart rate variability (HRV) metrics can be used as features for driver sleepiness classification. Since the heart rate is modulated by many different factors, and not just by sleepiness, it is relevant to question the high driver sleepiness classification accuracies that have occasionally been presented in the literature. The main objective of this paper is thus to test how well a sleepiness classification system based on HRV features really is. A unique data set with 86 drivers, obtained while driving on real roads in real traffic, both in alert and sleep deprived conditions, was used to train and test a support vector machine (SVM) classifier. Subjective ratings based on the Karolinska sleepiness scale (KSS) was used as ground truth to divide the data into three classes (alert, somewhat sleepy and severely sleepy). Even though nearly all the 24 investigated HRV metrics showed significant differences between sleepiness levels, the SVM results only reached a mean accuracy of 61 %, with the worst results originating from the severely sleepy cases. In summary, the high classification performance that may arise in studies with high experimental control could not be replicated under realistic driving conditions. Future works should focus on how various confounding factors should be accounted for when using HRV based metrics as input to a driver sleepiness detection system.

Pulse oximetry based on photoplethysmography imaging with red and green light : Calibratability and challenges

Remotely measuring the arterial blood oxygen saturation (SpO₂) in visible light (Vis) involves different probing depths, which may compromise calibratibility. This paper assesses the feasibility of calibrating camera-based SpO₂ (SpO_2,cam) using red and green light. Camera-based photoplethysmographic (PPG) signals were measured at 46 healthy adults at center wavelengths of 580 nm (green), 675 nm (red), and 840 nm (near-infrared; NIR). Subjects had their faces recorded during normoxia and hypoxia and under gradual cooling. SpO_2,cam estimates in Vis were based on the normalized ratio of camera-based PPG amplitudes in red over green light (RoG). SpO_2,cam in Vis was validated against contact SpO₂ (reference) and compared with SpO_2,cam estimated using red-NIR wavelengths. An RoG-based calibration curve for SpO₂ was determined based on data with a SpO₂ range of 85-100%. We found an [Formula: see text] error of 2.9% (higher than the [Formula: see text] for SpO_2,cam in red-NIR). Additional measurements on normoxic subjects under temperature cooling (from [Formula: see text] to [Formula: see text]) evidenced a significant bias of - 1.7, CI [- 2.7, - 0.7]%. It was also noted that SpO[Formula: see text] estimated at the cheeks was significantly biased (- 3.6, CI [- 5.7, - 1.5]%) with respect to forehead estimations. Under controlled conditions, SpO[Formula: see text] can be calibrated with red and green light but the accuracy is less than that of SpO[Formula: see text] estimated in the usual red-NIR window.

Discriminative Signatures for Remote-PPG

Near-infrared (NIR) remote photoplethysmography (PPG) promises attractive applications in darkness, as it involves unobtrusive, invisible light. However, since the PPG strength (AC/DC) is much lower in the NIR spectrum than in the RGB spectrum, robust vital signs monitoring is more challenging. In this paper, we propose a new PPG-extraction method, DIScriminative signature based extraction (DIS), to significantly improve the pulse-rate measurement in NIR. Our core idea is to use both the color signals containing blood absorption variations and additional disturbance signals as input for PPG extraction. By defining a discriminative signature, we use one-step least-squares regression (joint optimization) to retrieve the pulsatile component from color signals and suppress disturbance signals simultaneously. A large-scale lab experiment, recorded in NIR with heavy body motions, shows the significant improvement of DIS over the state-of-the-art method, whereas its principle is simple and generally applicable.

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

OBJECTIVES

Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting "outliers".

METHODS

We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed.

RESULTS

Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning.

CONCLUSION

Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

Single Element Remote-PPG

Camera-based remote photoplethysmography technology (remote-PPG) has shown great potential for contactless pulse-rate monitoring. However, remote-PPG systems typically analyze face images, which may restrict applications in view of privacy-preserving regulations such as the recently announced General Data Protection Regulation in the European Union. In this paper, we investigate the case of using single-element sensing as an input for remote-PPG extraction, which prohibits facial analysis and thus evades privacy issues. It also improves the efficiency of data storage and transmission. In contrast to known remote-PPG solutions using skin-selection techniques, the input signals in a single-element setup will contain a non-negligible degree of signal components associated with non-skin areas. Current remote-PPG extraction methods based on physiological and optical properties of skin reflections are therefore no longer valid. A new remote-PPG method, named Soft Signature based extraction (SoftSig), is proposed to deal with this situation by softening the dependence of pulse extraction on prior knowledge. A large scale experiment validates the concept of single-element remote-PPG monitoring and shows the improvement of SoftSig over general purpose solutions.