Probabilistic Signal Quality Metric for Reduced Complexity Unsupervised Remote Photoplethysmography

Benezeth, Yannick; Bobbia, Serge; Nakamura, Keisuke; Gómez, Randy; Dubois, Julien

doi:10.1109/ismict.2019.8744004

Cited by 8 publications

(8 citation statements)

References 19 publications

(27 reference statements)

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“…Typically, as HRV measures are inherently nonlinear, ML algorithms and other statistical data-driven methods such as Modified Varying Index Coefficient Autoregression Model (MVICAR) [ 43 ] can be applied in stress detection systems. ML algorithms have enabled accurate and efficient HRV-based stress detection and classification systems [ 29 , 44 , 45 , 46 , 47 ]. EDA, which measures the electrical activity of sweat glands, is another method that can be monitored with wearable devices, providing continuous and real-time monitoring of stress levels.…”

Section: Related Workmentioning

confidence: 99%

“…There have been a growing number of research papers. For example, Benezeth et al [ 46 ] proposed an rPPG-based algorithm that estimates HRV using a simple camera, showing a strong correlation between the HRV features and different emotional states. Similarly, Sabour et al [ 29 ] proposed an rPPG-based stress estimation system with an accuracy of 85.48%.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Fontes,

Machado,

Vinkemeier

et al. 2024

Sensors

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Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Fontes,

Machado,

Vinkemeier

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Typically as HRV measures are inherently nonlinear, ML algorithms and other statistical data-driven methods such as Modified Varying Index Coefficient Autoregression Model (MVICAR) [43] can be applied in stress detection systems. ML algorithms have enabled accurate and efficient HRV-based stress detection and classification systems [29,[44][45][46][47]. EDA, which measures the electrical activity of sweat glands, is another method that can be monitored with wearable devices, providing continuous and real-time monitoring of stress levels.…”

Section: Related Workmentioning

confidence: 99%

“…With this, HRV measures, pulse rate and breathing rate can be measured using an everyday camera for facial video analysis to remotely detect and monitor stress [28,[30][31][32]. There have been a growing number of research paper, for example Benezeth et al [46] proposed an rPPG-based algorithm that estimates HRV using a simple camera, showing a strong correlation between the HRV features and different emotional states. Similarly, Sabour et al [29] proposed an rPPG-based stress estimation system with an accuracy of 85.48%.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Fontes,

Machado,

Vinkemeier

et al. 2024

Preprint

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Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

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“…In this scenario, the algorithm can be integrated into the processing algorithm. For example, in [32,61], a remote iPPG signal is extracted from an image divided into several subregions. Each subregion provides an iPPG signal and these signals are evaluated by an SQI algorithm (signal to noise ratio (SNR) in this case).…”

Section: Supporting the Signal Processing Chainmentioning

confidence: 99%

A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods

et al. 2022

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Photoplethysmography is a method to visualize the variation in blood volume within tissues with light. The signal obtained has been used for the monitoring of patients, interpretation for diagnosis or for extracting other physiological variables (e.g., pulse rate and blood oxygen saturation). However, the photoplethysmography signal can be perturbed by external and physiological factors. Implementing methods to evaluate the quality of the signal allows one to avoid misinterpretation while maintaining the performance of its applications. This paper provides an overview on signal quality index algorithms applied to photoplethysmography. We try to provide a clear view on the role of a quality index and its design. Then, we discuss the challenges arising in the quality assessment of imaging photoplethysmography.

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Probabilistic Signal Quality Metric for Reduced Complexity Unsupervised Remote Photoplethysmography

Cited by 8 publications

References 19 publications

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

Enhancing Stress Detection: A Comprehensive Approach through rPPG Analysis and Deep Learning Techniques

A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods

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