Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911754433331200 |
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| author | Aqajari, Seyed Amir Hossein Labbaf, Sina Tran, Phuc Hoang Nguyen, Brenda Mehrabadi, Milad Asgari Levorato, Marco Dutt, Nikil Rahmani, Amir M. |
| author_facet | Aqajari, Seyed Amir Hossein Labbaf, Sina Tran, Phuc Hoang Nguyen, Brenda Mehrabadi, Milad Asgari Levorato, Marco Dutt, Nikil Rahmani, Amir M. |
| contents | Daily monitoring of stress is a critical component of maintaining optimal physical and mental health. Physiological signals and contextual information have recently emerged as promising indicators for detecting instances of heightened stress. Nonetheless, developing a real-time monitoring system that utilizes both physiological and contextual data to anticipate stress levels in everyday settings while also gathering stress labels from participants represents a significant challenge. We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data in a daily-life environment. Additionally, we have integrated a smart labeling approach to optimize the ecological momentary assessment (EMA) collection, which is required for building machine learning models for stress detection. We propose a three-tier Internet-of-Things-based system architecture to address the challenges. We utilized a cross-validation technique to accurately estimate the performance of our stress models. We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings. Whereas using PPG data alone, the highest F1-score achieved is approximately 56\%, emphasizing the significance of incorporating both PPG and contextual data in stress detection tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_05367 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings Aqajari, Seyed Amir Hossein Labbaf, Sina Tran, Phuc Hoang Nguyen, Brenda Mehrabadi, Milad Asgari Levorato, Marco Dutt, Nikil Rahmani, Amir M. Signal Processing Machine Learning Daily monitoring of stress is a critical component of maintaining optimal physical and mental health. Physiological signals and contextual information have recently emerged as promising indicators for detecting instances of heightened stress. Nonetheless, developing a real-time monitoring system that utilizes both physiological and contextual data to anticipate stress levels in everyday settings while also gathering stress labels from participants represents a significant challenge. We present a monitoring system that objectively tracks daily stress levels by utilizing both physiological and contextual data in a daily-life environment. Additionally, we have integrated a smart labeling approach to optimize the ecological momentary assessment (EMA) collection, which is required for building machine learning models for stress detection. We propose a three-tier Internet-of-Things-based system architecture to address the challenges. We utilized a cross-validation technique to accurately estimate the performance of our stress models. We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings. Whereas using PPG data alone, the highest F1-score achieved is approximately 56\%, emphasizing the significance of incorporating both PPG and contextual data in stress detection tasks. |
| title | Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.05367 |