Stress Classification and Personalization: Getting the most out of the least

Sah, Ramesh Kumar; Ghasemzadeh, Hassan

doi:10.48550/arxiv.2107.05666

Cited by 2 publications

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“…It is observed from Table 5 and Table 6 that the overall mental classification performance is found to be very promising as compared to previous research works being conducting on both the datasets for the multi-stress classification problem. The work performed in the year 2021 on the WESAD dataset by Sah et al [ 16 ] achieved a promising accuracy of about 92.85% using CNN. Other works using the RNN model for stress classification include that by Melchiades et al [ 14 ] in 2022, which achieved an accuracy of 86% for the WESAD dataset, whereas Bobade et al [ 4 ] describe machine learning techniques for stress detection, achieving an accuracy of 84.32% in the year 2020.…”

Section: Resultsmentioning

confidence: 99%

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Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

et al. 2022

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The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experiment has been conducted on two standard benchmark datasets, namely WESAD (wearable stress and affect detection) and SWELL. During the studies, testing accuracies of 94.8% and 99.39% are achieved for the WESAD and SWELL datasets, respectively. For the WESAD dataset, chest data are taken for the experiment, including the data of sensor modalities such as three-axis acceleration (ACC), electrocardiogram (ECG), body temperature (TEMP), respiration (RESP), etc.

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Section: Resultsmentioning

confidence: 99%

“…Some research work has been conducted on stress detection using deep learning models. Sah et al [ 16 ] introduced the CNN model for stress detection by using the data of only one sensor modality. Ghosh et al [ 17 ] worked on another method for mental stress detection using two physiological signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

et al. 2022

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A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

Li,

Washington

2024

JMIR AI

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Background There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. Objective We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. Methods We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. Results For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. Conclusions Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

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Stress Classification and Personalization: Getting the most out of the least

Cited by 2 publications

References 0 publications

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

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