Contactless pulse rate assessment: Results and insights for application in driving simulator
Camera-based monitoring of Pulse Rate (PR) enables continuous and unobtrusive assessment of driver's state, allowing estimation of fatigue or stress that could impact traffic safety. Commonly used wearable Photoplethysmography (PPG) sensors, while effective, suffer from motion artifacts and user discomfort. This study explores the feasibility of non-contact PR assessment using facial video recordings captured by a Red, Green, and Blue (RGB) camera in a driving simulation environment. The proposed approach detects subtle skin color variations due to blood flow and compares extracted PR values against reference measurements from a wearable wristband Empatica E4. We evaluate the impact of Eulerian Video Magnification (EVM) on signal quality and assess statistical differences in PR between age groups. Data obtained from 80 recordings from 64 healthy subjects covering a PR range of 45-160 bpm are analyzed, and signal extraction accuracy is quantified using metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results show that EVM slightly improves PR estimation accuracy, reducing MAE from 6.48 bpm to 5.04 bpm and RMSE from 7.84 bpm to 6.38 bpm. A statistically significant difference is found between older and younger groups with both video-based and ground truth evaluation procedures. Additionally, we discuss Empatica E4 bias and its potential impact on the overall assessment of contact measurements. Altogether the findings demonstrate the feasibility of camera-based PR monitoring in dynamic environments and its potential integration into driving simulators for real-time physiological assessment.
Code (0)
등록된 구현이 없습니다.
Tasks
Photoplethysmography (PPG)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MCLFIQ: Mobile Contactless Fingerprint Image Quality
We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 m…
Image Quality AssessmentBreath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring
Breath analysis has emerged as a critical tool in health monitoring, offering insights into respiratory function, disease detection, and continuous health assessment. While traditional contact-based methods are reliable,…
Federated LearningTransfer LearningRobust Pulse Rate From Chrominance-Based rPPG
Remote photoplethysmography (rPPG) enables contactless monitoring of the blood volume pulse using a regular camera. Recent research focused on improved motion robustness, but the proposed blind source separation techniqu…
blind source separationPhotoplethysmography (PPG) heart rate estimationmCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Arrhythmia
Arrhythmia is a common cardiac condition that can precipitate severe complications without timely intervention. While continuous monitoring is essential for timely diagnosis, conventional approaches such as electrocardio…
The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp
Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while considered the gold standard, is intrusive, labor-intensive, and unsu…
Sleep Stage DetectionSleep Quality