UBFC-Phys: A Multimodal Database For Psychophysiological Studies of Social Stress

Sabour, Rita Meziati; Benezeth, Yannick; Oliveira, Pierre De; Chappé, Julien; Yang, Fan

doi:10.1109/taffc.2021.3056960

Cited by 79 publications

(39 citation statements)

References 43 publications

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“…In this study, video files from the UBFC data set [9] were used to extract physiological information from individuals. Each one of the videos was processed using our proposed method described in section II C, which returns the rPPG signal.…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the efficacy of the proposed rPPG methodology, the method was benchmarked against the UBFC-Phys data set [9]. This dataset is a public multimodal dataset and whilst it is dedicated to psychophysiological studies, it contains information that can be used to benchmark general rPPG methods.…”

Section: Methodsmentioning

confidence: 99%

“…In this article, we aim to evaluate the efficacy of our rPPG method using the UBFC-Phys public database [9]. The advantage of this dataset over other datasets that are suitable for rPPG evaluation is that UBFC-Phys contains additional data which can be used for stress and emotion analysis.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Visual Photoplethysmography Method

Deus

Sehgal

Talukdar

2021

Preprint

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Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related issues cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individuals physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining growing interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two HRV features, NNi 20 and SAMPEN were capable of differentiating between Stress and Non-Stress states. In addition, three features, IBI, NNi 20, and SAMPEN were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a stressed or unstressed state, the models were able to achieve an accuracy score of 83.11%.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Evaluating Visual Photoplethysmography Method

Deus

Sehgal

Talukdar

2021

Preprint

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“…Since rPPG technology can be integrated with consumer-level cameras, it has great potential for affective computing and human–computer interaction applications. Researchers have demonstrated the feasibility of rPPG-based methods for interpreting human affects, such as cognitive stress estimation [ 72 , 73 ], emotion recognition [ 29 , 74 ], engagement detection [ 75 ], and pain recognition [ 76 , 77 , 78 ]. These studies illustrate the capability of using rPPG technology beyond the medical domain.…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Cheng

Wong

Chin

et al. 2021

Sensors

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Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

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“…including ECG, EEG, EMG, SC, SpO2, and EDA. Some of the datasets were designed for affective applications and provide special affective labels, e.g., MAHNOB [14] and uulmMAC [18] for emotion recognition, BioVid [15] for pain level estimation, and UBFC-Phys [19] for stress recognition. Performance evaluation: Most existing methods compare performance on the average HR of each input video in beats per minute (bpm).…”

Section: Benchmark Datasets and Evaluationsmentioning

confidence: 99%

Facial-Video-Based Physiological Signal Measurement: Recent advances and affective applications

Zhao

2021

IEEE Signal Process. Mag.

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Monitoring physiological changes (e.g., heart rate, respiration, heart rate variability) are important for measuring human's emotions. Physiological responses are more reliable and harder to alter compared to explicit behaviors (e.g., facial expressions, speech), but require special contact sensors to achieve. Research in the latest decade has shown that photoplethysmograph (PPG) signals can be remotely measured (i.e., rPPG) from facial videos under ambient light, from which physiological changes can be extracted. This promising finding has attracted big interests from researchers and the field of rPPG measurement has been growing fast. In this article, we review current progress on intelligent signal processing approaches for rPPG measurement including earlier works of unsupervised approaches and recently proposed supervised models, benchmark datasets, and performance evaluation. Furthermore, we also review studies on rPPG-based affective applications, and compare them with other affective computing modalities. We conclude this article by emphasizing the current main challenges and highlighting future directions.

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UBFC-Phys: A Multimodal Database For Psychophysiological Studies of Social Stress

Cited by 79 publications

References 43 publications

Evaluating Visual Photoplethysmography Method

Evaluating Visual Photoplethysmography Method

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Facial-Video-Based Physiological Signal Measurement: Recent advances and affective applications

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