Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs)

Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 187

This supplement to Aerospace Medicine and Biology lists 247 reports, articles and other documents announced during November 1978 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, psychological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Emphasis is placed on applied research, but reference to fundamental studies and theoretical principles related to experimental development also qualify for inclusion. Each entry in the bibliography consists of a bibliographic citation accompanied in most cases by an abstract

Heart rate-index estimates oxygen uptake, energy expenditure and aerobic fitness in rugby players

The purpose of the study was to verify the suitability of heart rate-index (HRindex) in predicting submaximal oxygen consumption (VO2), energy expenditure (EE) and maximal oxygen consumption (VO2max) during treadmill running in rugby players. Fifteen professional rugby players (99.8 \ub1 12.7 kg, 1.85 \ub1 0.09 m) performed a running incremental test while VO2 (breath-by-breath) and heart rate (HR) were measured. HRindex was calculated (actual HR/resting HR) to predict submaximal and maximal VO2 ([(HRindex x 6)-5.0] x (3.5 body weight)) and EE. Measured and predicted VO2 and EE were compared by two-way RM-ANOVA (method, speed), correlation and Bland-Altman analysis. Measured and predicted VO2max were compared by paired t-test, correlation and Bland-Altman analysis. Submaximal VO2 and EE significantly increased (baseline VO2: 8.1 \ub1 1.6 ml\ub7kg-1\ub7min-1VO2max: 46.8 \ub1 4.3 ml\ub7kg-1\ub7min-1, baseline EE: 0.03 \ub1 0.01 kcal\ub7kg-1\ub7min-1, peak EE: 0.23 \ub1 0.03 kcal\ub7kg-1\ub7min-1) as a function of speed (p < 0.001 and p < 0.001 for VO2 and EE respectively) yet measured and predicted values at equal treadmill speeds were not significantly different (p = 0.17; p = 0.16) and highly correlated (r = 0.95; r = 0.94). The Bland-Altman analysis confirmed a non-significant bias between measured and estimated VO2 (measured: 40.3 \ub1 10.7, estimated: 40.7 \ub1 10.1 ml\ub7kg-1\ub7min-1, bias = 1.35 ml\ub7kg-1\ub7min-1, z = 1.12, precision = 3.39 ml\ub7kg-1\ub7min-1) and EE (measured: 20.0 \ub1 0.05 kcal\ub7kg-1\ub7min-1, estimated: 20.0 \ub1 0.05 kcal\ub7kg-1\ub7min-1, bias = 0.00 kcal\ub7kg-1\ub7min-1, z = 0.04, precision = 0.02 kcal\ub7kg-1\ub7min-1). Estimated and predicted VO2max were not statistically different (p = 0.91), highly correlated (r = 0.96), and showed a non-significant bias (bias = 0.17, z = 0.22, precision = 1.29 ml\ub7kg-1\ub7min-1). HRindex is a valid field method to track VO2, EE and VO2max during running in rugby players

Heart rate-index estimates oxygen uptake, energy expenditure and aerobic fitness in rugby players

The purpose of the study was to verify the suitability of heart rate-index (HRindex) in predicting submaximal oxygen consumption (VO2), energy expenditure (EE) and maximal oxygen consumption (VO2max) during treadmill running in rugby players. Fifteen professional rugby players (99.8 +/- 12.7 kg, 1.85 +/- 0.09 m) performed a miming incremental test while VO2 (breath-bybreath) and heart rate (FIR) were measured. HRindex was calculated (actual HR/resting HR) to predict submaximal and maximal VO2 ({[(HRindex x 6)-5.0] x (3.5 body weight)}) and EE. Measured and predicted VO2 and EE were compared by two-way RM-ANOVA (method, speed), correlation and Bland-Altman analysis. Measured and predicted VO2max were compared by paired t-test, correlation and Bland-Altman analysis. Submaximal VO2 and EE significantly increased (baseline VO2: 8.1 +/- 1.6 ml.kg(-1).min(-1) VO2max: 46.8 +/- 4.3 ml.kg(-1).min(-1), baseline EE: 0.03 +/- 0.01 kcal.kg(-1).min(-1), peak EE: 0.23 +/- 0.03 kcal.kg(-1).min(-1)) as a function of speed (p < 0.001 and p < 0.001 for VO2 and EE respectively) yet measured and predicted values at equal treadmill speeds were not significantly different (p = 0.17; p = 0.16) and highly correlated (r = 0.95; r = 0.94). The Bland-Altman analysis confirmed a non-significant bias between measured and estimated VO2 (measured: 40.3 +/- 10.7, estimated: 40.7 +/- 10.1 ml.kg(-1).min(-1), bias = 1.35 ml.kg(-1).min(-1), z = 1.12, precision = 3.39 ml.kg(-1).min(-1)) and EE (measured: 20.0 +/- 0.05 kcal.kg(-1).min(-1), estimated: 20.0 +/- 0.05 kcal.kg(-1).min(-1), bias = 0.00 kcal.kg(-1).min(-1), z = 0.04, precision = 0.02 kcal.kg(-1).min(-1)). Estimated and predicted VO2max were not statistically different (p = 0.91), highly correlated (r = 0.96), and showed a nonsignificant bias (bias = 0.17, z = 0.22, precision = 1.29 ml.kg(-1).min(-1)). HRindex is a valid field method to track VO2, EE and VO2max during miming in rugby players