Stress Detection with Deep Learning Approaches Using Physiological Signals

Albertetti, Fabrizio; Simalastar, Alena; Rizzotti-Kaddouri, Aïcha

doi:10.1007/978-3-030-69963-5_7

Cited by 15 publications

(6 citation statements)

References 21 publications

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“…In a separate study, Li et al [30] combined computer usage with HR and HRV data, achieving a prediction accuracy of 71% for eustress, whereas our model reported an accuracy of 88%. Additionally, a study focused on detecting distress events using an ML model based on EDA and BVP attained an F 1 -score of 0.71 [42], compared to our model's F 1 -score of 0.85.…”

Section: Predicting Mood Stress Eustress and Distressmentioning

confidence: 86%

Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Awada,

Becerik-Gerber,

Lucas

et al. 2023

Sensors

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This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model’s R2 of 0.48 and MAE of 16.62. The extended model’s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.

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Section: Predicting Mood Stress Eustress and Distressmentioning

confidence: 86%

Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Awada,

Becerik-Gerber,

Lucas

et al. 2023

Sensors

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“…The original released data contains anxiety labels that were reported in a 5-point scale by subjects. Following the setting in [10], we calculated the z-score of labels for each participant separately, and marked labels below the personalized average as negative (0) and labels above the mean as positive (1). We used 2 hours of the data (5 minutes x 24 steps) to infer upcoming anxiety labels.…”

Section: A Datasetmentioning

confidence: 99%

“…Rising statistics like this has led to an increase in research on mental health, including mental health and well-being prediction using physiological signals over the past couple of years. Several authors research on predicting stress levels, and various mental health conditions using data collected both in clinical settings and in the wild [1], [7], [30], [31], [36], [39].…”

Section: Introductionmentioning

confidence: 99%

Bias Reducing Multitask Learning on Mental Health Prediction

Zanna¹,

Sridhar²,

Han³

et al. 2022

Preprint

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There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, and identify illnesses at an earlier stage when interventions may be more effective. However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities. This lack of standards persists due to factors such as technical difficulties, complexities of high dimensional clinical health data, etc., which are especially true for physiological signals. This along with prior evidence of relations between some physiological signals with certain demographic identities restates the importance of exploring bias in mental health prediction models that utilize physiological signals. In this work, we aim to perform a fairness analysis and implement a multi-task learning based bias mitigation method on anxiety prediction models using ECG data. Our method is based on the idea of epistemic uncertainty and its relationship with model weights and feature space representation. Our analysis showed that our anxiety prediction base model introduced some bias with regards to age, income, ethnicity, and whether a participant is born in the U.S. or not, and our bias mitigation method performed better at reducing the bias in the model, when compared to the reweighting mitigation technique. Our analysis on feature importance also helped identify relationships between heart rate variability and multiple demographic groupings.

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“…For example, a study used feature selection to reduce the dimensionality of physiological and behavioural data for stress detection and found that the reduced dataset yielded similar performance to the full dataset [19]. Another study used a combination of feature selection and deep learning techniques for stress detection and found that the selected features improved the accuracy and efficiency of the algorithm [20].…”

Section: Introductionmentioning

confidence: 99%

Early Mental Stress Detection Using Q-Learning Embedded Starling Murmuration Optimiser-Based Deep Learning Model

Moosavi,

Zafar,

Sanfilippo

et al. 2023

IEEE Access

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Stress affects individual of all ages as a regular part of life, but excessive and chronic stress can lead to physical and mental health problems, decreased productivity, and reduced quality of life. By identifying stress at an early stage, individuals can take steps to manage it effectively and improve their overall wellbeing. Feature selection is a critical aspect of early stress detection because it helps identify the most relevant and informative features that can differentiate between stressed and non-stressed individuals. This paper firstly proposes a variance based feature selection technique that uses q-learning embedded Starling Murmuration Optimiser (QLESMO) to choose relevant features from a publicly available dataset in which stresses experienced by nurses working during the Covid'19 Pandemic is recorded using bio-signals and user surveys. Furthermore, a comparative study with other metaheuristic based feature selection techniques have been demonstrated. Next, to evaluate the efficacy of the proposed algorithm, 10 benchmark test functions have been used. The reduced feature subset is then classified through a 1D convolutional neural network (CNN) model (QLESMO-CNN) and is seen to perform well in terms of the evaluation metrics in comparison to other competitive algorithms. Finally, the proposed technique is compared with the State-of-the-Art methodologies present in literature. The experiments provide a strong basis to determine features that are most relevant for early mental stress classification using a hybrid model combining CNN, Reinforcement Learning and metaheuristic algorithms.

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Stress Detection with Deep Learning Approaches Using Physiological Signals

Cited by 15 publications

References 21 publications

Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

Bias Reducing Multitask Learning on Mental Health Prediction

Early Mental Stress Detection Using Q-Learning Embedded Starling Murmuration Optimiser-Based Deep Learning Model

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