Contactless pulse rate assessment: Results and insights for application in driving simulator
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study
Artificial Intelligence-derived Vascular Age from Photoplethysmography: A Novel Digital Biomarker for Cardiovascular Health
Robust Heart Rate Detection via Multi-Site Photoplethysmography
PaPaGei: Open Foundation Models for Optical Physiological Signals
토론
이 논문과 연결된 포럼 글이 아직 없습니다.