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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.08212 |
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| _version_ | 1866913350027313152 |
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| author | Barki, Hika Chung, Wan-Young |
| author_facet | Barki, Hika Chung, Wan-Young |
| contents | Mental stress is a prevalent condition that can have negative impacts on one's health. Early detection and treatment are crucial for preventing related illnesses and maintaining overall wellness. This study presents a new method for identifying mental stress using a wearable biosensor worn in the ear. Data was gathered from 14 participants in a controlled environment using stress-inducing tasks such as memory and math tests. The raw photoplethysmography data was then processed by filtering, segmenting, and transforming it into scalograms using a continuous wavelet transform (CWT) which are based on two different mother wavelets, namely, a generalized Morse wavelet and the analytic Morlet (Gabor) wavelet. The scalograms were then passed through a convolutional neural network classifier, GoogLeNet, to classify the signals as stressed or non-stressed. The method achieved an outstanding result using the generalized Morse wavelet with an accuracy of 91.02% and an F1-score of 90.95%. This method demonstrates promise as a reliable tool for early detection and treatment of mental stress by providing real-time monitoring and allowing for preventive measures to be taken before it becomes a serious issue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08212 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography Barki, Hika Chung, Wan-Young Signal Processing Mental stress is a prevalent condition that can have negative impacts on one's health. Early detection and treatment are crucial for preventing related illnesses and maintaining overall wellness. This study presents a new method for identifying mental stress using a wearable biosensor worn in the ear. Data was gathered from 14 participants in a controlled environment using stress-inducing tasks such as memory and math tests. The raw photoplethysmography data was then processed by filtering, segmenting, and transforming it into scalograms using a continuous wavelet transform (CWT) which are based on two different mother wavelets, namely, a generalized Morse wavelet and the analytic Morlet (Gabor) wavelet. The scalograms were then passed through a convolutional neural network classifier, GoogLeNet, to classify the signals as stressed or non-stressed. The method achieved an outstanding result using the generalized Morse wavelet with an accuracy of 91.02% and an F1-score of 90.95%. This method demonstrates promise as a reliable tool for early detection and treatment of mental stress by providing real-time monitoring and allowing for preventive measures to be taken before it becomes a serious issue. |
| title | Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2404.08212 |