1. Introduction

2. Methods

2.3. Selection of Studies

In designing our search strategy, we meticulously adhered to predefined inclusion criteria to ensure the relevance and reliability of the gathered information. The inclusion criteria listed below served as a robust framework, guiding our pursuit of pertinent literature to meet the specific parameters essential for our research objectives:

• The study was conducted from 2010 onwards 1.
• The study was conducted in laboratory settings or real-life contexts.
• The study was published in English.
• The study used mainly non-invasive 2 wearables or bands.
• The study focused on mental stress.
• The study involved creating models or datasets for personalized stress detection or investigating the challenges in real-world applications.
• The study was not a review article.
• The study did not use synthetic data produced by GANs or other generative systems.
• The study was not exclusively published as an abstract or poster at a conference.

Furthermore, to ensure a robust and reliable screening process, M.B., S.P., M.D. and S.G. independently conducted the screening, resolving discrepancies through collaborative discussions and consensus-building. In the initial phase, studies were selected based on information from titles or abstracts. In cases where this information was insufficient, the full texts were retrieved for a more comprehensive analysis to determine eligibility. Subsequently, the full texts of all selected studies were obtained at a later stage to guarantee the quality and relevance of the information included in the review. A detailed view of the screening steps and the number of studies selected at each stage is provided in the results section.

Table 1. Research questions and related search queries.

Table 1. Research questions and related search queries.

Research question	Queries
What are the primary biosignals provided by wearables that can be utilized for personalized stress detection?	(“personalized” OR “subject level” OR “individual”) AND (“stress”) AND (“detection” OR “prediction” OR “recognition” OR “classification”) AND (“wearable”)
What are the key artificial intelligence techniques used for developing models for personalized stress detection?	(“physiological”) AND (“stress”) AND (“detection” OR “prediction” OR “recognition” OR “classification”) AND (“wearable”)
Are there publicly available datasets for training personalized stress detection models?	("personalized" OR "subject level" OR "individual") AND ("stress") AND ("detection" OR "prediction" OR "recognition" OR "classification") AND ("dataset") (“emotion” OR “stress”) AND (“detection” OR “recognition” OR “research” OR “classification” OR “prediction”) AND (“dataset” OR “database”)
What are the wearable devices available on the market that allow the acquisition of raw data?	Web search: {biosignal} AND “wearable device”
What are the primary challenges encountered in the practical implementation of stress detection models in the real-world?	("wearables" OR "wearable devices") AND ("machine learning" OR "artificial intelligence" OR "monitoring") AND ("challenge" OR "challenges" OR "issues" OR "perspectives" OR "limitations" OR "topics") AND ("ethical" OR "ethics" OR "sensors") AND ("arousal" OR "distress" OR "stress" OR "physiological activity" OR "physiological reactions" OR "physiological response") ("wearables" OR "wearable devices" OR "smartwatch" OR "smartwatches") AND ("machine learning" OR "ml" OR "artificial intelligence" OR "ai" OR "monitoring" OR "prediction" OR "classification" OR "detection") AND ("challenge" OR "challenges" OR "issues" OR "perspectives" OR "limitations" OR "topics") AND ("ethical" OR "ethics" OR "sensors") AND ("arousal" OR "distress" OR "eustress" OR "stress" OR "physiological activity" OR "physiological reactions" OR "physiological response")

3. Results

Figure 1. Number of studies published in each category over the past decade.

Figure 1. Number of studies published in each category over the past decade.

Figure 2. Number of devices capable of capturing raw biosignals released over the past decade.

Figure 2. Number of devices capable of capturing raw biosignals released over the past decade.

Figure 3. PRISMA flowchart of this scoping review.

Figure 3. PRISMA flowchart of this scoping review.

3.4. Real-World Challenges in Stress Detection Solutions.

Our search on the real-world challenges in stress detection solutions identified 11 articles. A qualitative examination of these articles unveiled five prominent themes: data quality and distortions, technical aspects related to wearable devices, user experience and behavior, privacy, and interpretability. On a quantitative level, the most frequently encountered theme is data quality and distortions (n=9), followed by user experience and behavior (n=4) and technical aspects related to wearable devices (n=3). On the other hand, the themes of privacy and interpretability were addressed by just one article each.

Table 10. List of studies on real-world challenges in stress detection solutions, grouped by theme.

Table 10. List of studies on real-world challenges in stress detection solutions, grouped by theme.

Theme	Study(s)
Data quality and signal distortion	[109,110,111,112,113,114,115,116,117]
Technical aspects related to wearable devices	[109,114,118]
User experience and behavior	[112,114,118,119]
Privacy	[118]
Interpretability	[116]

3.4.1. Data Quality and Signal Distortion

Concerning data quality and signal distortion, three main issues have been identified: signal distortion, label assignment, and missing data handling. Regarding signal distortion, studies have emphasized that raw data from wearable devices is often distorted and might not meet minimum standards for their use in real-world contexts. Among the most common causes of these distortions are motion artifacts [111,112,114,116], physical or human activity [109,114,116,117], and optical sensor issues [111]. Motion artifacts and human activity artifacts stem from the subject’s movement during measurement, where the sensor may lose adherence (in the case of ECG or EDA) or move too far from the skin (in the case of PPG), resulting in instances of poor contact, and in some cases, complete loss of contact. While this poses a tangible problem, the literature suggests strategies for managing such distortions, including the incorporation of accelerometer data into models and the implementation of human activity recognition classifiers for signal cleaning, as well as the use of filters discussed in Section 3.1.2. Regarding optical sensor issues, the situation is more complex but limited to PPG and SKT sensors. These sensors rely on skin characteristics for signal construction and are consequently strongly influenced by them. Specifically, tattoos in the wrist area, skin perfusion, and skin tone may distort the optical sensor output due to different reflection patterns [111]. The solution to these issues is unclear and represents a more concrete obstacle than motion artifacts. While signal distortion may adversely impact the integrity of data used for developing and implementing personalized stress detection models, we also confront the issue of label distortion. The quality of labels in a dataset is crucial as it guides the algorithm in learning. However, the quality of labels poses a particularly complex challenge in developing stress detection models, as it is necessary to find a strategy for measuring a vague construct such as stress. The definition of stress as a psychological construct is still an ongoing process, and researchers have not yet reached a consensus. The challenge, therefore, is to reliably measure a construct that is not well-defined. One solution proposed by researchers is to expose individuals to protocols that elicit stress states and use cortisol tests for label assignment [115]. However, besides lacking ecological validity, this approach is impractical for creating personalized stress detection models in the real world, as it would require a time-consuming in-person session to create the training set, limiting the scalability. An alternative approach researchers propose is to use validated scales that measure constructs ideally close to perceived stress for label assignment. Although this solution is more methodologically robust, it should be emphasized that consistency between the scale and model output has to be maintained, and any data manipulation or aggregation (for example, for building a binary classifier from scales continuous values) should be avoided [115]. The quality of labels thus represents an open problem that requires clarification both regarding the construct measured and the instruments for its measurement, which should be compatible with personalized stress detection solutions that are implemented. As previously outlined in the section about signal distortion, real-life data from wearable device sensors may contain missing data [115]. Specifically, missing data in biosignals represent interruptions in the time series. The presence of gaps in a time series poses a practical problem, affecting the feature extraction stage in machine learning-based approaches and the possibility of utilizing such biosignal fragments in deep learning models. Interpolation was traditionally employed to address this issue; however, this approach is outdated, may introduce bias, and can adversely affect model performance. Recent studies have proposed two main methods to manage the gaps in the time series. The first method [113] can be applied in developing personalized stress detection models based on machine learning and involves using complete biosignal fragments to train classifiers capable of predicting the missing features of incomplete signals. This approach introduces fewer distortions than interpolation because it operates not on the time series but on its high-level informative content, leveraging globally learned knowledge. The second method, on the other hand, is more general and models signal fragmentation as a sparsity issue. In particular, according to [110], it is possible to exploit nuclear norm minimization, a method based on the low-rank assumption, to derive unobserved sensor data thanks to mathematical properties (i.e., observed entries are sampled uniformly at random, and exact recovery can be achieved leveraging probability). Although these approaches are effective, most studies developing personalized stress detection models in real-life scenarios tend to discard incomplete signals or rely on interpolation to reduce computational load and model complexity. Considering this, it is essential to examine new perspectives for managing missing data, ensuring a seamless, efficient, and prompt resolution of data gaps.

3.4.2. Technical Aspects Related to Wearable Devices

Focusing on the technical aspects related to wearable devices, the literature analysis has identified three limitations that affect real-world implementations of personalized stress detection models: battery life [109,114], computing and network capabilities [109], and vendor updates [118]. Battery life is the primary challenge when deploying personalized stress detection models that rely on biosignals from wearables. While the production processes of wearable devices have improved over the years, resulting in increasingly efficient processors, the physical limit of batteries remains. Most commercially available wearable devices, especially smartwatches, use batteries with capacities below 500 mAh [120], capable of supporting device activity for only a limited number of hours. The battery constraint impacts the operational choices researchers are forced to make. Specifically, researchers must find a tradeoff between information granularity and battery life [109,114]. Hitting the battery limit can result in incomplete data and study dropouts due to poor experience. In order to reduce battery drain, researchers may limit the number of measurements, data upload frequency, and the complexity of on-device operations. Regarding on-device operations, it is essential to note that wearable devices offer limited network and computing capabilities and are currently unable to execute complex models. These functions are usually outsourced to an edge layer (e.g., phone) and cloud solutions [109]. Lastly, it is noteworthy that most wearable devices lack open-source accessibility, which introduces potential challenges associated with the code governing the sensors. Researchers have raised concerns about the possibility of encountering alterations in raw data stemming from updates or firmware modifications to the device introduced by vendors. Addressing these technical challenges is critical and involves implementing strategies for optimizing processes or data collection, exploring new hardware solutions, and granting researchers greater control over the source code of devices, potentially through the adoption of open-source solutions.

3.4.3. User Experience and Behavior

Regarding user experience and behavior, three main challenges have been identified for implementing real-world solutions for personalized stress detection: the presence of technical support, granting user data access, and the implementation of best practices for Ecological Momentary Assessments (EMAs). Regarding the technical support side, researchers have emphasized the need to establish a contact line with the participants involved in the study [112,119], as it is common for them to encounter difficulties that, if ignored, can lead to missing data or study dropout. In particular, the presence of technical support helps to reduce device malfunctions due to improper usage and to manage any configuration issues [112,119]. However, it should be noted that a user-friendly wearable like a consumer smartwatch reduces the need for frequent intervention. Regarding user data access during the study, it has been observed that although it can positively affect motivation, it can lead to behavior manipulations by the participant, which may change his or her behavior to observe effects on the dashboard provided [112]. It should be emphasized, however, that this behavior was observed in a sample of adolescents, so it may be more relevant to specific types of user groups. Considering best practices for the implementation of Ecological Momentary Assessments (EMAs), researchers largely agree on the need to identify an accuracy/labels tradeoff that allows collecting a sufficient number of labeled data through EMAs for creating a training set that is large enough to train a good classifier for stress [118,119] without posing a significant burden on the subject. While exaggerating with labels could lead to dropout, having a small amount of data points could negatively impact the final performance of the classifier. Identifying a strategy to optimize the number of EMAs is therefore crucial. Findings from [114,119] suggest that user response rates are influenced by the activity performed at the time of survey reception, the time of day, the subject’s mood, and personal patterns. [119] suggests that developing machine learning models for identifying the ideal timing for the user to send the minimum number of communications while still collecting the desired amount of data would be beneficial. Finally, engaging solutions (e.g., apps) for collecting EMAs seem to promote higher response rates than more traditional methods (e.g., SMS). In conclusion, researchers should focus on building tailored solutions to gather the data, considering the need for technical support and implementing strategies for collecting training labels with a less invasive and more engaging approach.

3.4.4. Privacy

Privacy refers to the right of individuals to keep personal information and activities confidential. Given that solutions for personalized stress detection are based on physiological data and contain sensitive information about the psychophysical state of an individual, this dimension becomes significant. Medical data and privacy are critical aspects of healthcare, and protecting patients’ sensitive information is a legal and ethical responsibility. In this regard, researchers [118] proposed a set of best practices for managing data in studies or real-life applications. In particular, according to privacy by design principles, participants should be informed in detail about the data collection process, the use of collected data, and the purpose of the research. Data collection should also be limited to essential data, excluding those not strictly necessary for the stress detection task, such as GPS data, which could indirectly identify an individual. To address the risks of data leaks and promote privacy, it is advisable to integrate solutions that adhere to security standards and allow users to limit or revoke data access at any time. A potential strategy to mitigate privacy-related risks could be developing personalized stress detection models that work on wearable devices [39]. However, it is essential to remember that with current technology, wearables have limited computational power and batteries that limit this possibility. Regarding the legal aspect of implementing models in real life, many countries are developing regulations or guidelines to guide the adoption of privacy-friendly AI. However, a shared set of rules has yet to be identified. Fostering a dialogue between developers, end-users, and regulatory bodies is pivotal to establishing a shared understanding of the ethical considerations involved in personalized stress detection model development. As we continue to advance in this field, a commitment to responsible innovation and a holistic approach to privacy will be crucial in shaping the future of research.

3.4.5. Interpretability

As mentioned earlier, the stress detection task falls within medical applications and, as such, requires models capable of providing information about the rules followed by the model in making decisions. A white box model is preferable in medical AI due to its transparency, providing clear insights into the decision-making process, which is crucial for ensuring accountability and trust in healthcare applications. This transparency allows medical practitioners to understand and interpret the model’s decisions, enhancing usability and facilitating collaboration between clinicians and AI systems. In this regard, researchers [116] have proposed using SHAP (SHapley Additive exPlanations) values to make the models more interpretable. SHAP values fairly distribute the contribution of each feature to the prediction of a model among its individual features. By assigning a numerical value to each feature’s impact on the prediction, SHAP values enhance interpretability by revealing the importance of different factors in influencing the model outcomes. This helps to understand the model’s decision-making process better but also aids in explaining individual predictions, making the model more transparent and trustworthy for users and stakeholders. However, the limitation of this solution lies in the fact that it can be mainly applied to models that adopt a machine-learning approach. The explainability of deep learning models is a more complex topic that requires targeted investigation by researchers. So far, interpretable models for personalized stress detection based on deep learning have yet to be available, and more research on interpretable deep learning models is needed.

4. Discussion

5. Conclusion

Abbreviations

References

1. WHO. Stress. https://www.who.int/news-room/questions-and-answers/item/stress. Accessed: 2023-10-24.
2. APA. Stress. https://dictionary.apa.org/stress. Accessed: 2023-10-24.
3. APA. Stress effects on the body. https://www.apa.org/topics/stress/body. Accessed: 2023-10-24.
4. Sinha, R. Chronic stress, drug use, and vulnerability to addiction. Ann. N. Y. Acad. Sci. 2008, 1141, 105–130. [Google Scholar] [CrossRef] [PubMed]
5. Marin, M.F.; Lord, C.; Andrews, J.; Juster, R.P.; Sindi, S.; Arsenault-Lapierre, G.; Fiocco, A.J.; Lupien, S.J. Chronic stress, cognitive functioning and mental health. Neurobiol. Learn. Mem. 2011, 96, 583–595. [Google Scholar] [CrossRef] [PubMed]
6. Cannon, W.B. The wisdom of the body. Plan. Perspect. 1932, 312. [Google Scholar] [CrossRef]
7. Chu, B.; Marwaha, K.; Sanvictores, T.; Ayers, D. Physiology, Stress Reaction; StatPearls Publishing, 2022.
8. Godoy, L.D.; Rossignoli, M.T.; Delfino-Pereira, P.; Garcia-Cairasco, N.; de Lima Umeoka, E.H. A Comprehensive Overview on Stress Neurobiology: Basic Concepts and Clinical Implications. Front. Behav. Neurosci. 2018, 12, 127. [Google Scholar] [CrossRef]
9. Cohen, S.; Kamarck, T.; Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 1983, 24, 385–396. [Google Scholar] [CrossRef] [PubMed]
10. Kirschbaum, C.; Pirke, K.M.; Hellhammer, D.H. The ’Trier Social Stress Test’–a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 1993, 28, 76–81. [Google Scholar] [CrossRef]
11. Crosswell, A.D.; Lockwood, K.G. Best practices for stress measurement: How to measure psychological stress in health research. Health Psychol Open 2020, 7, 2055102920933072. [Google Scholar] [CrossRef]
12. Epel, E.S.; Crosswell, A.D.; Mayer, S.E.; Prather, A.A.; Slavich, G.M.; Puterman, E.; Mendes, W.B. More than a feeling: A unified view of stress measurement for population science. Front. Neuroendocrinol. 2018, 49, 146–169. [Google Scholar] [CrossRef]
13. Bryk, A.S.; Raudenbush, S.W. Application of hierarchical linear models to assessing change. Psychol. Bull.
14. Giannakakis, G.; Grigoriadis, D.; Giannakaki, K.; Simantiraki, O.; Roniotis, A.; Tsiknakis, M. Review on Psychological Stress Detection Using Biosignals. IEEE Transactions on Affective Computing 2022, 13, 440–460. [Google Scholar] [CrossRef]
15. Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef]
16. Arksey, H.; O’Malley, L. Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef]
17. Shi, Y.; Hoai Nguyen, M.; Blitz, P.; French, B.; Fisk, S.; De la Torre, F.; Smailagic, A.; Siewiorek, D.; al’ Absi, M.; Ertin, E.; et al. , Personalized Stress Detection from Physiological Measurements; 2010.
18. Sandulescu, V.; Andrews, S.; Ellis, D.; Bellotto, N.; Mozos, O.M. Stress Detection Using Wearable Physiological Sensors. In Artificial Computation in Biology and Medicine; Ferrández Vicente, J.M.; Álvarez-Sánchez, J.R.; De La Paz López, F.; Toledo-Moreo, F.J.; Adeli, H., Eds.; Springer International Publishing, 2015; Vol. 9107, pp. 526–532.
19. Wu, M.; Cao, H.; Nguyen, H.L.; Surmacz, K.; Hargrove, C. Modeling perceived stress via HRV and accelerometer sensor streams. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2015, 2015, 1625–1628. [Google Scholar]
20. Xu, Q.; Nwe, T.L.; Guan, C. Cluster-Based Analysis for Personalized Stress Evaluation Using Physiological Signals. IEEE Journal of Biomedical and Health Informatics 2015, 19, 275–281. [Google Scholar] [CrossRef]
21. Kikhia, B.; Stavropoulos, T.; Andreadis, S.; Karvonen, N.; Kompatsiaris, I.; Sävenstedt, S.; Pijl, M.; Melander, C. Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia. Sensors 2016, 16, 1989. [Google Scholar] [CrossRef] [PubMed]
22. Mozos, O.M.; Sandulescu, V.; Andrews, S.; Ellis, D.; Bellotto, N.; Dobrescu, R.; Ferrandez, J.M. Stress Detection Using Wearable Physiological and Sociometric Sensors. Int. J. Neural Syst. 2017, 27, 1650041. [Google Scholar] [CrossRef]
23. Akmandor, A.O.; Jha, N.K. Keep the Stress Away with SoDA: Stress Detection and Alleviation System. IEEE Transactions on Multi-Scale Computing Systems 2017, 3, 269–282. [Google Scholar] [CrossRef]
24. Saeed, A.; Ozcelebi, T.; Lukkien, J.; Van Erp, J.B.F.; Trajanovski, S. Model Adaptation and Personalization for Physiological Stress Detection. In Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE; 2018; pp. 209–216. [Google Scholar]
25. Taamneh, S.; Tsiamyrtzis, P.; Dcosta, M.; Buddharaju, P.; Khatri, A.; Manser, M.; Ferris, T.; Wunderlich, R.; Pavlidis, I. A multimodal dataset for various forms of distracted driving. Sci Data 2017, 4, 170110. [Google Scholar] [CrossRef]
26. Can, Y.S.; Chalabianloo, N.; Ekiz, D.; Ersoy, C. Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study. Sensors 2019, 19, 1849. [Google Scholar] [CrossRef] [PubMed]
27. Can, Y.S.; Chalabianloo, N.; Ekiz, D.; Fernandez-Alvarez, J.; Riva, G.; Ersoy, C. Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches. IEEE Access 2020, 8, 38146–38163. [Google Scholar] [CrossRef]
28. Indikawati, F.I.; Winiarti, S. Stress Detection from Multimodal Wearable Sensor Data. IOP Conference Series: Materials Science and Engineering 2020, 771, 012028. [Google Scholar] [CrossRef]
29. Schmidt, P.; Reiss, A.; Duerichen, R.; Marberger, C.; Van Laerhoven, K. Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. In Proceedings of the Proceedings of the 20th ACM International Conference on Multimodal Interaction, New York, NY, USA, 2018; ICMI ’18, pp. 400–408.
30. Tervonen, J.; Puttonen, S.; Sillanpää, M.J.; Hopsu, L.; Homorodi, Z.; Keränen, J.; Pajukanta, J.; Tolonen, A.; Lämsä, A.; Mäntyjärvi, J. Personalized mental stress detection with self-organizing map: From laboratory to the field. Comput. Biol. Med. 2020, 124, 103935. [Google Scholar] [CrossRef]
31. Li, B.; Sano, A. Early versus Late Modality Fusion of Deep Wearable Sensor Features for Personalized Prediction of Tomorrow’s Mood, Health, and Stress. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2020, 2020, 5896–5899. [Google Scholar]
32. Sano, A.; Taylor, S.; McHill, A.W.; Phillips, A.J.; Barger, L.K.; Klerman, E.; Picard, R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. J. Med. Internet Res. 2018, 20, e210. [Google Scholar] [CrossRef]
33. Li, B.; Sano, A. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2020, 4, 1–26. [Google Scholar] [CrossRef]
34. Cipresso, P.; Serino, S.; Borghesi, F.; Tartarisco, G.; Riva, G.; Pioggia, G.; Gaggioli, A. Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach. ACTA IMEKO 2021, 10, 239. [Google Scholar] [CrossRef]
35. Pourmohammadi, S.; Maleki, A. Continuous mental stress level assessment using electrocardiogram and electromyogram signals. Biomed. Signal Process. Control 2021, 68, 102694. [Google Scholar] [CrossRef]
36. Tazarv, A.; Labbaf, S.; Reich, S.M.; Dutt, N.; Rahmani, A.M.; Levorato, M. Personalized Stress Monitoring using Wearable Sensors in Everyday Settings. In Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021, pp. 7332–7335.
37. Yu, H.; Vaessen, T.; Myin-Germeys, I.; Sano, A. Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild. In Proceedings of the 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Nara, Japan; 2021; pp. 1–8. [Google Scholar]
38. Lisowska, A.; Wilk, S.; Peleg, M. Catching patient’s attention at the right time to help them undergo behavioural change: Stress classification experiment from blood volume pulse. In Artificial Intelligence in Medicine; Lecture notes in computer science, Springer International Publishing: Cham, 2021; pp. 72–82. [Google Scholar]
39. Fauzi, M.A.; Yang, B.; Blobel, B. Comparative Analysis between Individual, Centralized, and Federated Learning for Smartwatch Based Stress Detection. Journal of Personalized Medicine 2022, 12, 1584. [Google Scholar] [CrossRef]
40. Han, Y.; Lee, H.; Toshnazarov, K.E.; Noh, Y.; Lee, U. StressBal: Personalized Just-in-time Stress Intervention with Wearable and Phone Sensing. In Proceedings of the Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Cambridge United Kingdom, 2022; pp. 41–43.
41. Iqbal, T.; Simpkin, A.J.; Roshan, D.; Glynn, N.; Killilea, J.; Walsh, J.; Molloy, G.; Ganly, S.; Ryman, H.; Coen, E.; et al. Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. Sensors 2022, 22, 8135. [Google Scholar] [CrossRef]
42. Fazeli, S.; Levine, L.; Beikzadeh, M.; Mirzasoleiman, B.; Zadeh, B.; Peris, T.; Sarrafzadeh, M. Passive Monitoring of Physiological Precursors of Stress Leveraging Smartwatch Data. In Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2022; pp. 2893–2899. [Google Scholar]
43. Sah, R.K.; Cleveland, M.J.; Habibi, A.; Ghasemzadeh, H. Stressalyzer: Convolutional Neural Network Framework for Personalized Stress Classification. In Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022; pp. 4658–4663.
44. Hasanpoor, Y.; Tarvirdizadeh, B.; Alipour, K.; Ghamari, M. Stress Assessment with Convolutional Neural Network Using PPG Signals. In Proceedings of the 2022 10th RSI International Conference on Robotics and Mechatronics (ICRoM). IEEE; 2022; pp. 472–477. [Google Scholar]
45. Eom, S.; Eom, S.; Washington, P. SIM-CNN: Self-Supervised Individualized Multimodal Learning for Stress Prediction on Nurses Using Biosignals. Technical report, Health Informatics, 2023.
46. Hosseini, S.; Gottumukkala, R.; Katragadda, S.; Bhupatiraju, R.T.; Ashkar, Z.; Borst, C.W.; Cochran, K. A multimodal sensor dataset for continuous stress detection of nurses in a hospital. Sci Data 2022, 9, 255. [Google Scholar] [CrossRef]
47. Finseth, T.T.; Dorneich, M.C.; Vardeman, S.; Keren, N.; Franke, W.D. Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations. IEEE Access 2023, 11, 25431–25454. [Google Scholar] [CrossRef]
48. Islam, T.; Washington, P. Personalization of Stress Mobile Sensing using Self-Supervised Learning 2023. arXiv:cs.LG/2308.02731].
49. Li, J.; Washington, P. A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study 2023. arXiv:cs.LG/2308.14245].
50. Moser, M.K.; Resch, B.; Ehrhart, M. An Individual-Oriented Algorithm for Stress Detection in Wearable Sensor Measurements. IEEE Sens. J. 2023, 23, 22845–22856. [Google Scholar] [CrossRef]
51. Kyriakou, K.; Resch, B.; Sagl, G.; Petutschnig, A.; Werner, C.; Niederseer, D.; Liedlgruber, M.; Wilhelm, F.; Osborne, T.; Pykett, J. Detecting Moments of Stress from Measurements of Wearable Physiological Sensors. Sensors 2019, 19. [Google Scholar] [CrossRef]
52. Tazarv, A.; Labbaf, S.; Rahmani, A.; Dutt, N.; Levorato, M. Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings. 2023 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2023. [Google Scholar]
53. Tutunji, R.; Kogias, N.; Kapteijns, B.; Krentz, M.; Krause, F.; Vassena, E.; Hermans, E.J. Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study. J. Med. Internet Res. 2023, 25, e39995. [Google Scholar] [CrossRef]
54. Enderle, J.; Bronzino, J. Introduction to Biomedical Engineering; Academic Press, 2012.
55. Swapna, M.; Viswanadhula, U.M.; Aluvalu, R.; Vardharajan, V.; Kotecha, K. Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence. Journal of Sensor and Actuator Networks 2022, 11, 17. [Google Scholar] [CrossRef]
56. Semmlow, J. Circuits, Signals, and Systems for Bioengineers: A MATLAB-Based Introduction; Academic Press, 2017.
57. Weber, J.; Angerer, P.; Apolinário-Hagen, J. Physiological reactions to acute stressors and subjective stress during daily life: A systematic review on ecological momentary assessment (EMA) studies. PLoS One 2022, 17, e0271996. [Google Scholar] [CrossRef]
58. Noteboom, J.T.; Barnholt, K.R.; Enoka, R.M. Activation of the arousal response and impairment of performance increase with anxiety and stressor intensity. J. Appl. Physiol. 2001, 91, 2093–2101. [Google Scholar] [CrossRef] [PubMed]
59. Sevil, M.; Rashid, M.; Askari, M.R.; Maloney, Z.; Hajizadeh, I.; Cinar, A. Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data. Signals Commun. Technol. 2020, 1, 188–208. [Google Scholar] [CrossRef]
60. Sevil, M.; Rashid, M.; Hajizadeh, I.; Askari, M.R.; Hobbs, N.; Brandt, R.; Park, M.; Quinn, L.; Cinar, A. Discrimination of simultaneous psychological and physical stressors using wristband biosignals. Comput. Methods Programs Biomed. 2021, 199, 105898. [Google Scholar] [CrossRef]
61. Brain Mapping: An Encyclopedic Reference; Academic Press, 2015.
62. Park, J.; Seok, H.S.; Kim, S.S.; Shin, H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front. Physiol. 2021, 12, 808451. [Google Scholar] [CrossRef]
63. Guyton, A.C.; Hall, J.E. Human Physiology and Mechanisms of Disease; Saunders, 1997.
64. Herborn, K.A.; Graves, J.L.; Jerem, P.; Evans, N.P.; Nager, R.; McCafferty, D.J.; McKeegan, D.E.F. Skin temperature reveals the intensity of acute stress. Physiol. Behav. 2015, 152, 225–230. [Google Scholar] [CrossRef]
65. Atterhög, J.H.; Eliasson, K.; Hjemdahl, P. Sympathoadrenal and cardiovascular responses to mental stress, isometric handgrip, and cold pressor test in asymptomatic young men with primary T wave abnormalities in the electrocardiogram. Br. Heart J. 1981, 46, 311–319. [Google Scholar] [CrossRef] [PubMed]
66. Bhide, A.; Durgaprasad, R.; Kasala, L.; Velam, V.; Hulikal, N. Electrocardiographic changes during acute mental stress. Int. J. Med. Sci. Public Health 2016, 5, 835. [Google Scholar] [CrossRef]
67. Romero, M.L.; Butler, L.K. Endocrinology of Stress. Int. J. Comp. Psychol. 2007, 20. [Google Scholar] [CrossRef]
68. Abelson, J.L.; Khan, S.; Giardino, N. HPA axis, respiration and the airways in stress–a review in search of intersections. Biol. Psychol. 2010, 84, 57–65. [Google Scholar] [CrossRef] [PubMed]
69. Huang, C.J.; Webb, H.E.; Zourdos, M.C.; Acevedo, E.O. Cardiovascular reactivity, stress, and physical activity. Front. Physiol. 2013, 4, 314. [Google Scholar] [CrossRef]
70. Britton, J.W.; Frey, L.C.; Hopp, J.L.; Korb, P.; Koubeissi, M.Z.; Lievens, W.E.; Pestana-Knight, E.M.; St. Louis, E.K. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants; American Epilepsy Society: Chicago.
71. Arthur, A.Z. Stress as a state of anticipatory vigilance. Percept. Mot. Skills 1987, 64, 75–85. [Google Scholar] [CrossRef]
72. McEwen, B.S.; Gianaros, P.J. Central role of the brain in stress and adaptation: links to socioeconomic status, health, and disease. Ann. N. Y. Acad. Sci. 2010, 1186, 190–222. [Google Scholar] [CrossRef]
73. Kim, S.H.; Geem, Z.W.; Han, G.T. Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System. Sensors 2020, 20. [Google Scholar] [CrossRef]
74. Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef]
75. Polat.; Kemal.; Öztürk.; Şaban. Diagnostic biomedical signal and image processing applications with deep learning methods; Intelligent Data-Centric Systems, Academic Press: San Diego, CA, 2023.
76. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30.
77. Zeng, Z.; Kaur, R.; Siddagangappa, S.; Rahimi, S.; Balch, T.; Veloso, M. Financial Time Series Forecasting using CNN and Transformer 2023. arXiv:cs.LG/2304.04912].
78. Benchekroun, M.; Istrate, D.; Zalc, V.; Lenne, D. Mmsd: A multi-modal dataset for real-time, continuous stress detection from physiological signals. In Proceedings of the Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies. SCITEPRESS - Science and Technology Publications, 2022.
79. Coşkun, B.; Ay, S.; Erol Barkana, D.; Bostanci, H.; Uzun, İ.; Oktay, A.B.; Tuncel, B.; Tarakci, D. A physiological signal database of children with different special needs for stress recognition. Sci Data 2023, 10, 382. [Google Scholar] [CrossRef]
80. Koldijk, S.; Sappelli, M.; Verberne, S.; Neerincx, M.A.; Kraaij, W. The SWELL Knowledge Work Dataset for Stress and User Modeling Research. In Proceedings of the Proceedings of the 16th International Conference on Multimodal Interaction, New York, NY, USA, 2014; ICMI ’14, pp. 291–298.
81. Haouij, N.E.; Poggi, J.M.; Sevestre-Ghalila, S.; Ghozi, R.; Jaïdane, M. AffectiveROAD system and database to assess driver’s attention. In Proceedings of the Proceedings of the 33rd Annual ACM Symposium on Applied Computing, New York, NY, USA, 2018; SAC ’18, pp. 800–803.
82. Markova, V.; Ganchev, T.; Kalinkov, K. CLAS: A Database for Cognitive Load, Affect and Stress Recognition. In Proceedings of the 2019 International Conference on Biomedical Innovations and Applications (BIA). IEEE; 2019; pp. 1–4. [Google Scholar]
83. Parent, M.; Albuquerque, I.; Tiwari, A.; Cassani, R.; Gagnon, J.F.; Lafond, D.; Tremblay, S.; Falk, T.H. PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research. Front. Neurosci. 2020, 14, 542934. [Google Scholar] [CrossRef]
84. Meziati, R.; Benezeth, Y.; De Oliveira, P.; Chappé, J.; Yang, F. UBFC-Phys, 2021.
85. Chen, W.; Zheng, S.; Sun, X. Introducing MDPSD, a Multimodal Dataset for Psychological Stress Detection. In Big Data; Communications in computer and information science, Springer Singapore: Singapore, 2021; pp. 59–82. [Google Scholar]
86. SMILE (momentary stress labels with electrocardiogram, skin conductance, and acceleration data). https://compwell.rice.edu/workshops/embc2022/challenge, 2022. Accessed: 2023-10-24.
87. Hosseini, M.; Sohrab, F.; Gottumukkala, R.; Bhupatiraju, R.T.; Katragadda, S.; Raitoharju, J.; Iosifidis, A.; Gabbouj, M. EmpathicSchool: A multimodal dataset for real-time facial expressions and physiological data analysis under different stress conditions 2022. arXiv:cs.MM/2209.13542].
88. Hart, S.G.; Staveland, L.E. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Advances in Psychology; Hancock, P.A.; Meshkati, N., Eds.; North-Holland, 1988; Vol. 52, pp. 139–183.
89. Spielberger, C.D.; Gorsuch, R.L.; Lushene, R.E. STAI Manual for the State-trait Anxiety Inventory (“Self-evaluation Questionnaire”); Consulting Psychologists Press, 1970.
90. Bradley, M.M.; Lang, P.J. Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. J. Behav. Ther. Exp. Psychiatry 1994, 25, 49–59. [Google Scholar] [CrossRef] [PubMed]
91. Moskowitz, D.S.; Young, S.N. Ecological momentary assessment: what it is and why it is a method of the future in clinical psychopharmacology. J. Psychiatry Neurosci. 2006, 31, 13–20. [Google Scholar] [PubMed]
92. EmbracePlus. https://www.empatica.com/en-eu/embraceplus/. Accessed: 2023-11-7.
93. Samsung Galaxy Watch 4. https://www.samsung.com/it/watches/galaxy-watch/galaxy-watch4-black-bluetooth-sm-r860nzkaitv/buy/, 2021. Accessed: 2023-11-7.
94. Samsung Galaxy Watch 5. https://www.samsung.com/it/watches/galaxy-watch/galaxy-watch5-40mm-graphite-bluetooth-sm-r900nzaaitv/, 2022. Accessed: 2023-11-7.
95. Samsung Galaxy Watch 6. https://www.samsung.com/it/watches/galaxy-watch/galaxy-watch6-bluetooth-40mm-gold-bluetooth-sm-r930nzeaitv/, 2023. Accessed: 2023-11-7.
96. Polar OH1+. https://www.polar.com/it/sensors/oh1-optical-heart-rate-sensor. Accessed: 2023-11-7.
97. Polar H10. https://www.polar.com/it/sensors/h10-heart-rate-sensor. Accessed: 2023-11-7.
98. Polar Verity Sense. https://www.polar.com/it/products/accessories/polar-verity-sense. Accessed: 2023-11-7.
99. Williams, G. Bangle.Js - hackable smart watch. https://banglejs.com/. Accessed: 2023-11-7.
100. Shimmer3 ECG unit. https://shimmersensing.com/product/shimmer3-ecg-unit-2/. Accessed: 2023-11-7.
101. Shimmer3 GSR+ unit. https://shimmersensing.com/product/shimmer3-gsr-unit/. Accessed: 2023-11-7.
102. CardioBAN Kit. https://www.pluxbiosignals.com/products/cardioban. Accessed: 2023-11-7.
103. MuscleBAN Kit. https://www.pluxbiosignals.com/products/muscleban-kit. Accessed: 2023-11-7.
104. Montgomery, S.M.; Nair, N.; Chen, P.; Dikker, S. Introducing EmotiBit, an open-source multi-modal sensor for measuring research-grade physiological signals. Science Talks 2023, 6. [Google Scholar] [CrossRef]
105. BrainBit Callibri. https://store.brainbit.com/collections/hardware/products/callibri-sdk. Accessed: 2023-12-5.
106. BrainBit Headband. https://store.brainbit.com/collections/hardware/products/brainbit-sdk. Accessed: 2023-12-5.
107. Muse S (gen 2). https://choosemuse.com/products/muse-s-gen-2. Accessed: 2023-12-5.
108. Smyth, J.M.; Zawadzki, M.J.; Marcusson-Clavertz, D.; Scott, S.B.; Johnson, J.A.; Kim, J.; Toledo, M.J.; Stawski, R.S.; Sliwinski, M.J.; Almeida, D.M. Computing Components of Everyday Stress Responses: Exploring Conceptual Challenges and New Opportunities. Perspect. Psychol. Sci. 2023, 18, 110–124. [Google Scholar] [CrossRef]
109. Siirtola, P.; Peltonen, E.; Koskimäki, H.; Mönttinen, H.; Röning, J.; Pirttikangas, S. Wrist-worn Wearable Sensors to Understand Insides of the Human Body: Data Quality and Quantity. In Proceedings of the The 5th ACM Workshop on Wearable Systems and Applications, New York, NY, USA, 2019; WearSys ’19; pp. 17–21. [Google Scholar]
110. Jiang, J.Y.; Chao, Z.; Bertozzi, A.L.; Wang, W.; Young, S.D.; Needell, D. Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices. In Proceedings of the Proceedings of the 28th ACM International Conference on Information and Knowledge Management, New York, NY, USA, 2019; pp. 192773–2781.
111. Bent, B.; Goldstein, B.A.; Kibbe, W.A.; Dunn, J.P. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med 2020, 3, 18. [Google Scholar] [CrossRef]
112. Thammasan, N.; Stuldreher, I.V.; Schreuders, E.; Giletta, M.; Brouwer, A.M. A Usability Study of Physiological Measurement in School Using Wearable Sensors. Sensors 2020, 20. [Google Scholar] [CrossRef]
113. Iranfar, A.; Arza, A.; Atienza, D. ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2021, 2021, 535–541. [Google Scholar]
114. Tazarv, A.; Labbaf, S.; Rahmani, A.M.; Dutt, N.; Levorato, M. Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement. In Proceedings of the Proceedings of the Conference on Information Technology for Social Good, New York, NY, USA, 2021; GoodIT ’21, pp. 186–191.
115. Booth, B.M.; Vrzakova, H.; Mattingly, S.M.; Martinez, G.J.; Faust, L.; D’Mello, S.K. Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers. IEEE Transactions on Affective Computing 2022, 13, 2201–2217. [Google Scholar] [CrossRef]
116. Chalabianloo, N.; Can, Y.S.; Umair, M.; Sas, C.; Ersoy, C. Application level performance evaluation of wearable devices for stress classification with explainable AI. Pervasive Mob. Comput. 2022, 87, 101703. [Google Scholar] [CrossRef]
117. Can, Y.S. Stressed or just running? Differentiation of mental stress and physical activityby using machine learning. Turkish Journal of Electrical Engineering and Computer Sciences 2022, 30, 312–327. [Google Scholar] [CrossRef]
118. Mohr, D.C.; Zhang, M.; Schueller, S.M. Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. Annu. Rev. Clin. Psychol. 2017, 13, 23–47. [Google Scholar] [CrossRef]
119. Cummings, P.; Petitclerc, A.; Moskowitz, J.; Tandon, D.; Zhang, Y.; MacNeill, L.A.; Alshurafa, N.; Krogh-Jespersen, S.; Hamil, J.L.; Nili, A.; et al. Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake. Affect Sci 2022, 3, 849–861. [Google Scholar] [CrossRef] [PubMed]
120. González-Cañete, F.J.; Casilari, E. A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems. Sensors 2021, 21. [Google Scholar] [CrossRef] [PubMed]
121. Shiffman, S.; Stone, A.A.; Hufford, M.R. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 2008, 4, 1–32. [Google Scholar] [CrossRef] [PubMed]
122. Fradkov, A.L. Early History of Machine Learning. IFAC-PapersOnLine 2020, 53, 1385–1390. [Google Scholar] [CrossRef]