A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Ronca, Vincenzo; Giorgi, Andrea De; Rossi, Dario; Florio, Antonello; Flumeri, Gianluca Di; Aricò, Pietro; Sciaraffa, Nicolina; Vozzi, Alessia; Tamborra, Luca; Simonetti, Ilaria; Borghini, Gianluca

doi:10.3390/s21051607

Cited by 19 publications

(11 citation statements)

References 64 publications

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“…is indicates that the pilot SA level cannot be effectively characterized by eyetracking metrics in such scenes based on the current research results. To effectively monitor the evolution of the pilot's SA cognitive states through physiological indicators in scene 1, scene 2, and scene 5, multiple indicators are needed for further consideration, such as heart rate variability (HRV) [37], electrodermal activity (EDA) [38], electromyography (EMG) [39], and electroencephalogram (EEG) [40].…”

Section: Correlation Evaluationmentioning

confidence: 99%

Correlation Evaluation of Pilots’ Situation Awareness in Bridge Simulations via Eye‐Tracking Technology

Jiang

Chen

Kang

2021

Computational Intelligence and Neuroscience

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To maintain situation awareness (SA) when exposed to emergencies during pilotage, a pilot needs to selectively allocate attentional resources to perceive critical status information about ships and environments. Although it is important to continuously monitor a pilot’s SA, its relationship with attention is still not fully understood in ship pilotage. This study performs bridge simulation experiments that include vessel departure, navigation in the fairway, encounters, poor visibility, and anchoring scenes with 13 pilots (mean = 11.3 and standard deviation = 1.4 of experience). Individuals were divided into two SA group levels based on the Situation Awareness Rating Technology (SART-2) score (mean = 20.13 and standard deviation = 5.83) after the experiments. The visual patterns using different SA groups were examined using heat maps and scan paths based on pilots’ fixations and saccade data. The preliminary visual analyses of the heat maps and scan paths indicate that the pilots’ attentional distribution is modulated by the SA level. That is, the most concerning areas of interest (AOIs) for pilots in the high and low SA groups are outside the window (AOI-2) and electronic charts (AOI-1), respectively. Subsequently, permutation simulations were utilized to identify statistical differences between the pilots’ eye-tracking metrics and SA. The results of the statistical analyses show that the fixation and saccade metrics are affected by the SA level in different AOIs across the five scenes, which confirms the findings of previous studies. In encounter scenes, the pilots’ SA level is correlated with the fixation and saccade metrics: fixation count ( p = 0.034 < 0.05 in AOI-1 and p = 0.032 < 0.05 in AOI-2), fixation duration ( p = 0.043 < 0.05 in AOI-1 and p = 0.014 < 0.05 in AOI-2), and saccade count ( p = 0.086 < 0.1 in AOI-1 and p = 0.054 < 0.1 in AOI-2). This was determined by the fixation count ( p = 0.024 < 0.05 in AOI-1 and p = 0.034 < 0.05 in AOI-2), fixation duration ( p = 0.036 < 0.05 in AOI-1 and p = 0.047 < 0.05 in AOI-2), and saccade duration ( p = 0.05 ≤ 0.05 in AOI-1 and p = 0.042 < 0.05 in AOI-2) in poor-visibility scenes. In the remaining scenes, the SA could not be measured using eye movements alone. This study lays a foundation for the cognitive mechanism recognition of pilots based on SA via eye-tracking technology, which provides a reference to establish cognitive competency standards in preliminary pilot screenings.

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Section: Correlation Evaluationmentioning

confidence: 99%

Correlation Evaluation of Pilots’ Situation Awareness in Bridge Simulations via Eye‐Tracking Technology

Jiang

Chen

Kang

2021

Computational Intelligence and Neuroscience

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“…The other purpose of filtering is to emphasise the heartbeat peaks (QRS process) of the ECG signals so that the distance between consecutive peaks can be measured (i.e. each R peak corresponds to a heartbeat) [ 51 ]. Heart rate and heart rate variability values can be estimated once these steps are completed.…”

Section: Introductionmentioning

confidence: 99%

“…Reduced HRV, on the other hand, indicates that the body's stress response is not optimal, potentially exacerbating the negative effects of chronic stress and increasing the risk of stress-related medical conditions. Previous studies reported that HRV indices, as provided by telemedicine and remote healthcare applications [ 51 ] were significantly reduced in patients with depression [ 31 , 53 ] and anxiety disorders, including generalised anxiety disorder (GAD), social anxiety disorder, panic disorder and post-traumatic stress disorder [ 16 , 32 ]. Thus, we can conclude from a number of studies on HRV and mental illness that lower HRV in patients with mental illness is associated with poorer cardiovascular health outcomes and a variety of vascular diseases [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Association between mental illness and blood pressure variability: a systematic review

et al. 2022

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Background Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. Methods Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. Results Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. Conclusions Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.

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“…The camera measurement of human physiological signals has gained considerable interest in the research community in recent years. The monitoring of vital signals such as heart rate (HR), heart rate variability (HRV), respiration rate (RR), oxygen saturation (SpO2) or blood volume pulse (BVP) is crucial in the assessment of the physical and mental human state, and its potential applications [1,13,28]. Traditionally, physiological signals are acquired using continuous bio-signal monitoring devices, which typically require physical contact between the sensor and the skin.…”

Section: Introductionmentioning

confidence: 99%

Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss

Comas¹,

Ruiz²,

Sukno³

2022

Preprint

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We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets between the video facial PPG and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module (TDM) constructed by the incremental aggregation of multiple convolutional derivatives, emulating a Taylor series expansion up to the desired order. Robustness to ground truth offsets is handled by the introduction of TALOS (Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based models. We verify the effectiveness of our model by reporting accuracy and efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to existing models, our approach shows competitive heart rate estimation accuracy with a much lower number of parameters and lower computational cost.

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A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Cited by 19 publications

References 64 publications

Correlation Evaluation of Pilots’ Situation Awareness in Bridge Simulations via Eye‐Tracking Technology

Correlation Evaluation of Pilots’ Situation Awareness in Bridge Simulations via Eye‐Tracking Technology

Association between mental illness and blood pressure variability: a systematic review

Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss

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