Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection

Wei, Chen; Zheng, Shengping; Sun, Xiao

doi:10.1007/978-981-16-0705-9_5

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Second, the observations were assigned to one of three classes (neutral, stress, and relax) based on the conditions under which the data were collected. In accordance with the methods employed in similar studies [41,46,52], it may have been more ecologically valid to supplement the dataset with the subjective scores reported by participants, as these reflected their current stress or relaxation levels. Lastly, the relatively high SD of the outer CV performance indicates stability issues in the LOGO CV methods.…”

Section: Limitationsmentioning

confidence: 99%

“…A critical issue arises when researchers seek to artificially increase dataset size by dividing each participant's physiological data into multiple segments [18,41,42]. This practice violates the fundamental statistical assumption that observations must be independent since these resulting segments are interdependent due to being derived from the same participant.…”

Section: Data Segmentationmentioning

confidence: 99%

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Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

Bahameish,

Stockman,

Requena Carrión

2024

Sensors

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Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.

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Section: Limitationsmentioning

confidence: 99%

Section: Data Segmentationmentioning

confidence: 99%

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

Bahameish,

Stockman,

Requena Carrión

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Various machine learning algorithms have been used to differentiate stressed, neutral, and amused emotion. Authors in [ 40 ] have introduced another large scale dataset of stress using physiological signals. Reference [ 41 ] is a multidomain social media dataset for identifying stressed state of an individual.…”

Section: Related Workmentioning

confidence: 99%

Stress Classification Using Brain Signals Based on LSTM Network

Phutela

Relan

Gabrani³

et al. 2022

Computational Intelligence and Neuroscience

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The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

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A multimodal sensor dataset for continuous stress detection of nurses in a hospital

et al. 2022

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Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.

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Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection

Cited by 3 publications

References 32 publications

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features

Stress Classification Using Brain Signals Based on LSTM Network

A multimodal sensor dataset for continuous stress detection of nurses in a hospital

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