Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data

Holder, R. Wade; Sah, Ramesh Kumar; Cleveland, Michael J.; Ghasemzadeh, Hassan

doi:10.1109/ccnc49033.2022.9700682

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…No statistically significant differences were noted for wrist acceleration between appraisals of the coexistence of eustress and distress (9.29±14.28) and the eustress (10.66±14.78). Just as previous studies have indicated the value of acceleration data for predicting stress arousal [56], our data indicate this feature may be useful in differentiating ideal stress appraisal states (i.e., those containing eustress) from less ideal states (i.e., boredom and distress).…”

Section: Plos Onesupporting

confidence: 74%

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Awada,

Becerik Gerber,

Lucas

et al. 2024

PLoS ONE

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Previous studies have primarily focused on predicting stress arousal, encompassing physiological, behavioral, and psychological responses to stressors, while neglecting the examination of stress appraisal. Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity. In this study, we investigated several research questions related to the association between states of stress appraisal (i.e., boredom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, as well as the most effective ML algorithms and data signals for predicting stress appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined through the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82.78% when combining physiological and behavioral features and 79.55% using only the physiological dataset. The small accuracy difference of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results identified electrodermal activity, skin temperature, and blood volume pulse as the most useful physiologic features. Implementing these models within work environments can serve as a foundation for designing workplace policies, practices, and stress management strategies that prioritize the promotion of eustress while reducing distress and boredom. Such efforts can foster a supportive work environment to enhance employee well-being and productivity.

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Section: Plos Onesupporting

confidence: 74%

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Awada,

Becerik Gerber,

Lucas

et al. 2024

PLoS ONE

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“…uni-modal baselines, feature fusion, and score fusion. The table shows that when only two modalities are used, our method achieves accuracy and f1 of 92.90 and 91.73, respectively, outperforming[22],[40],[57]. Including RESP with ECG and EDA improves the model performance by giving a boost of approximately 1% in accuracy and 1.5% in f1, outperforming[28],[55].…”

mentioning

confidence: 93%

Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition

Bhatti

Behinaein

Rodenburg

et al. 2021

2021 9th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block, to create intermediate connections between individual streams responsible for processing each modality. Additionally, our method benefits from two properties. First, it can share information uni-directionally (from one modality to the other) or bi-directionally. Second, it can be integrated into multiple stages at the same time to further allow network gradients to be exchanged in several touch-points. We perform extensive experiments on three public multimodal wearable datasets, WE-SAD, SWELL-KW, and CASE, and demonstrate that our method can effectively regulate and share information between different modalities to learn better representations. Our experiments further demonstrate that once integrated into simple CNN-based multimodal solutions (2, 3, or 4 modalities), our method can result in superior or competitive performance to state-of-the-art and outperform a variety of baseline uni-modal and classical multimodal methods.

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Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning

Li,

Zhang,

Feng

et al. 2024

Health Inf Sci Syst

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Propose Stress is a common problem globally. Prediction of stress in advance could help people take effective measures to manage stress before bad consequences occur. Considering the chaotic features of human psychological states, in this study, we integrate deep learning and chaos theory to address the stress prediction problem. Methods Based on chaos theory, we embed one’s seemingly disordered stress sequence into a high dimensional phase space so as to reveal the underlying dynamics and patterns of the stress system, and meanwhile are able to identify the stress predictable time range. We then conduct deep learning with a two-layer (dimension and temporal) attention mechanism to simulate the nonlinear state of the embedded stress sequence for stress prediction. Results We validate the effectiveness of the proposed method on the public available Tesserae dataset. The experimental results show that the proposed method outperforms the pure deep learning method and Chaos method in both 2-label and 3-label stress prediction. Conclusion Integrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper.

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Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data

Cited by 9 publications

References 12 publications

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition

Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning

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