Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Sensors, Journal Year: 2024, Volume and Issue: 24(10), P. 3221 - 3221

Published: May 18, 2024

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents scoping review personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, devices, real-world implementation challenges. The an overview its biological mechanisms, details methodology search, synthesizes findings. It shows that especially EDA PPG, are frequently utilized demonstrate potential reliability in multimodal settings. Evidence trend towards deep learning found, although limited comparison with traditional methods calls further research. Concerns arise regarding representativeness datasets practical challenges deploying technologies, which include issues related to data quality privacy. Future research should aim develop comprehensive explore techniques not only accurate but also computationally efficient user-centric, thereby closing gap between theoretical applications improve effectiveness systems real scenarios.

Language: Английский

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Art in the city reduces the feeling of anxiety, stress, and negative mood: A field study examining the impact of artistic intervention in urban public space on well-being

Wellbeing Space and Society, Journal Year: 2024, Volume and Issue: 7, P. 100215 - 100215

Published: Aug. 13, 2024

Language: Английский

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Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review

Published: April 28, 2024

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents scoping review personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, devices, real-world implementation challenges. The an overview its biological mechanisms, details methodology search, synthesizes findings. It shows that especially EDA PPG, are frequently utilized demonstrate potential reliability in multimodal settings. Evidence trend towards deep learning found, although limited comparison with traditional methods calls further research. Concerns arise regarding representativeness datasets practical challenges deploying technologies, which include issues related to data quality privacy. Future research should aim develop comprehensive explore techniques not only accurate but also computationally efficient user-centric, thereby closing gap between theoretical applications improve effectiveness systems real scenarios.

Language: Английский

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An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements

Sensors, Journal Year: 2024, Volume and Issue: 24(16), P. 5085 - 5085

Published: Aug. 6, 2024

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements deep learning to automatically detect features from raw data, opens several interesting applications related detecting emotional states. Being able accurately stress-related arousal an acute setting can positively impact imminent status humans, i.e., through avoiding dangerous locations urban traffic setting. This work proposes explainable methodology for automatic detection stress physiological recorded a non-invasive device, Empatica E4 wristband. We propose Long-Short Term-Memory (LSTM) network, extended Deep Generative Ensemble conditional GANs (LSTM DGE), deal low data regime sparsely labeled measurements. As explainability is often main concern models, we leverage Integrated Gradients (IG) highlight most essential used by model prediction and compare results state-of-the-art expert-based stress-detection methodologies terms precision, recall, interpretability. The show that our LSTM DGE outperforms algorithm 3 percentage points 7.18 precision. More importantly, use as layer explainability, there strong overlap between model-derived electrodermal activity existing literature, which current systems medical research psychology are based on.

Language: Английский

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Art in the City Enhances Subjective Well-Being: A Field Study Examining the Impact of Artistic Intervention in Urban Public Space on Well-Being.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

Promoting urban well-being is a significant societal issue in the context of rapid urbanization. Past research has highlighted that interaction with green spaces, such as parks and forests, key promoting well-being. However, there limited knowledge regarding potential from non-nature elements. In present study, we explored whether interacting art could enhance street contexts. our field experiment, built two interventions on streets, decorating them either laminated prints or We measured subjective physiological before after interventions. With this paradigm, assessed if, not only green, but also artistic can improve Our results showed that, intervention an environment, participants reported reduced feelings anxiety, stress, negative mood they did intervention. Further, indicate improvements were linked to participants' evaluations testing location (restorativeness), aesthetic quality (e.g., beauty, meaningfulness), their overall experience enjoyment). These findings have implications city planning, highlight novel tool for enhancing

Language: Английский

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ENSS (Expanded Nursing Stress Assessment) version 1,0: An Android-Based Mobile Application Efficient Stress Assessment in Nurses

International Journal of Advanced Health Science and Technology, Journal Year: 2024, Volume and Issue: 4(1), P. 19 - 24

Published: Feb. 13, 2024

The nursing profession is a that prone to experiencing stress. Long work duration in accompanying patients one of the factors causes stress nurses. To anticipate work-related problems, it necessary periodically identify level experienced by use mobile applications needs be developed for easy and efficient assessment Android-based which are expected help nurses they experiencing. aim this study was developing application android-based levels efficiently effectively. This research uses design Research Development (R&D) with product development validation approach through trials on 34 sampling method total sample. Test using instrument System Usability Scale (SUS). an analysis called ENSS version 1.0 login features, questionnaire main menu load interpretation. test tested respondents score 74.78 category Acceptable or Admissible. has been suitable identification so proper management can carried out.

Language: Английский

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An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements

Published: July 9, 2024

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements deep learning to automatically detect features from raw data, opens several interesting applications related detecting emotional states. Being able accurately stress-related arousal an acute setting can positively impact imminent status humans, i.e., through avoiding dangerous locations urban traffic setting. This work proposes explainable methodology for automatic detection stress physiological recorded a non-invasive device, Empatica E4 wristband. We propose Long-Short Term-Memory (LSTM) network, extended Deep Generative Ensemble conditional GANs (LSTM DGE) , deal low data regime sparsely labeled measurements. As explainability is often main concern models, we leverage Integrated Gradients (IG) highlight most essential used by model prediction and compare results state-of-the-art expert-based methodologies terms precision, recall interpretability. Results show that our LSTM DGE outperforms algorithm 3 percentage points recall, 7.18 precision. More importantly, use as layer explainability, there strong overlap between model-derived electrodermal activity, existing literature, which current systems medical research psychology are based on.

Language: Английский

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Identifying urban stress and bicycle infrastructure relationships: a mixed-methods citizen-science approach

Urban Planning and Transport Research, Journal Year: 2023, Volume and Issue: 11(1)

Published: Oct. 9, 2023

Active mobility is considered a key aspect of the revolution and therefore elementary in combating climate crisis. However, lot research needed to improve situation active mobility, especially concerning inhibiting factors choice means transport. For reasons such as high volume speed motorised traffic, heavy noise, pollution levels, urban space often associated with increased stress. The generation provision stress data are particularly importance for planning. citizen-science approach BMDVFootnote1- project ESSEM implements triangulating that uses biological markers standardized questionnaires make statements about individual ‘stress’. In future, this should help identify vulnerable groups better address them development presented study describes use participatory methods based on three ‘stress hotspots’ Osnabrück, taking into account different forms cycling infrastructure.

Language: Английский

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Stress Assessment for Augmented Reality Applications Based on Head Movement Features

IEEE Transactions on Visualization and Computer Graphics, Journal Year: 2024, Volume and Issue: 30(10), P. 6970 - 6983

Published: April 5, 2024

Augmented reality is one of the enabling technologies upcoming future. Its usage in working and learning scenarios may lead to a better quality work training by helping operators during most crucial stages processes. Therefore, automatic detection stress augmented experiences can be valuable support prevent consequences on people's health foster spreading this technology. In work, we present design non-invasive assessment approach. The proposed system based analysis head movements people wearing Head Mounted Display while performing stress-inducing tasks. First, designed subjective experiment consisting two stress-related tests for data acquisition. Then, statistical has been performed determine which features are representative presence stress. Finally, classifier combination Support Vector Machines trained. approach achieved promising performances thus paving way further studies research direction.

Language: Английский

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Eyes on the Street, Mind on the Ride: Decoding Stress in Active Mobility

Abstracts of the ICA, Journal Year: 2024, Volume and Issue: 7, P. 1 - 2

Published: Sept. 2, 2024

Language: Английский

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