Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
Citation: Evgenia Lazarou, Themis P. Exarchos. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices[J]. AIMS Neuroscience, 2024, 11(2): 76-102. doi: 10.3934/Neuroscience.2024006
Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
| [1] | Tazarv A, Labbaf S, Reich SM, et al. (2021) Personalized Stress Monitoring using Wearable Sensors in Everyday Settings. Annu Int Conf IEEE Eng Med Biol Soc 2021: 7332-7335. https://doi.org/10.1109/EMBC46164.2021.9630224 |
| [2] |
Han HJ, Labbaf S, Borelli JL, et al. (2020) Objective stress monitoring based on wearable sensors in everyday settings. J Med Eng Technol 44: 177-189. https://doi.org/10.1080/03091902.2020.1759707 
|
| [3] |
Walter S, Gruss S, Ehleiter H, et al. (2013) The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system. IEEE International Conference on Cybernetics (CYBCO) : pp. 128-131. https://doi.org/10.1109/CYBConf.2013.6617456
|
| [4] | Chan SF, La Greca AM (2013) Perceived Stress Scale (PSS). Encyclopedia of Behavioral Medicine. New York, NY: Springer. https://doi.org/10.1007/978-1-4419-1005-9_773 |
| [5] |
Gomes N, Pato M, Lourenço AR, et al. (2023) A Survey on Wearable Sensors for Mental Health Monitoring. Sensors 23: 1330. https://doi.org/10.3390/s23031330
|
| [6] |
Perna G, Riva A, Defillo A, et al. (2020) HRvariability: Can it serve as a marker of mental health resilience?. J Affect Disord 263: 754-761. https://doi.org/10.1016/j.jad.2019.10.017
|
| [7] |
Can YS, Arnrich B, Ersoy C (2019) Stress detection in daily life scenarios using smartphones and wearable sensors: A survey. J Biomed Inform 92: 103139. https://doi.org/10.1016/j.jbi.2019.103139
|
| [8] |
Dian FJ, Vahidnia R, Rahmati A (2020) Wearables and the Internet of Things (IoT), applications, opportunities, and challenges: A Survey. IEEE Access 8: 69200-69211. https://doi.org/10.1109/ACCESS.2020.2986329
|
| [9] |
Rozario M, Zainuddin AA, Gamage SA (2021) Artificial Intelligence and Machine learning in the Healthcare Sector: A Review. Malays J Sci Adv Technol 1: 89-96. https://doi.org/10.56532/mjsat.v1i3.18
|
| [10] | Kreibig SD, Gendolla GHE (2014) Autonomic nervous system measuring of emotion in education and achievement settings. International handbook of emotions in education (pp. 625–642).Routledge/Taylor & Francis Group. |
| [11] | LeBouef T, Yaker Z, Whited L (2024) Physiology, Autonomic Nervous System. [Updated 2023 May 1]. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing. Available from: https://www.ncbi.nlm.nih.gov/books/NBK538516/ |
| [12] | Dunlavey CJ (2018) Introduction to the Hypothalamic-Pituitary-Adrenal Axis: Healthy and Dysregulated Stress Responses, Developmental Stress and Neurodegeneration. J Undergrad Neurosci Educ 16: R59-R60. |
| [13] |
Won E, Kim YK (2016) Stress, the Autonomic Nervous System, and the Immune-kynurenine Pathway in the Etiology of Depression. Curr Neuropharmacol 14: 665-73. https://doi.org/10.2174/1570159x14666151208113006
|
| [14] |
Crosswell AD, Lockwood KG (2020) Best practices for stress measurement: How to measure psychological stress in health research. Health Psychol Open 7. https://doi.org/10.1177/2055102920933072
|
| [15] |
Epel E, Crosswell A, Mayer S, et al. (2018) More than a feeling: A unified view of stress measurement for population science. Front Neuroendocrinol 49: 146-169. https://doi.org/10.1016/j.yfrne.2018.03.001
|
| [16] |
Healey JA, Picard RW (2005) Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans Intell Transp Syst 6: 156-166. https://doi.org/10.1109/TITS.2005.848368
|
| [17] | Zephyr chest strap bio-sensor. Available from: https://www.zephyranywhere.com/products/bioharness-3 |
| [18] | Masri G, Al-Shargie F, Tariq U, et al. (2023) Mental Stress Assessment in the Workplace: A Review. IEEE Trans Affect Comput . https://doi.org/10.1109/TAFFC.2023.3312762 |
| [19] |
Jalilisadrabad S, Behzadfar M, Moghani Rahimi K (2023) Identification of Urban Stress Measurement Methods. Stress Relief Urban Planning. Singapore: Springer. https://doi.org/10.1007/978-981-99-4202-2_5
|
| [20] |
Rashkovska A, Depolli M, Tomašić I, et al. (2020) Medical-Grade ECG Sensor for Long-Term Monitoring. Sensors 20: 1695. https://doi.org/10.3390/s20061695
|
| [21] | Regalia G, Resnati D, Tognetti S (2023) Sensors on the Wrist. Encyclopedia of Sensors and Biosensors.Elsevier 1-20. https://doi.org/10.1016/B978-0-12-822548-6.00130-8 |
| [22] |
Zangróniz R, Martínez-Rodrigo A, Pastor JM, et al. (2017) Electrodermal Activity Sensor for Classification of Calm/Distress Condition. Sensors 17: 2324. https://doi.org/10.3390/s17102324
|
| [23] |
Martínez-Mozos OM, Sandulescu V, Andrews S, et al. (2017) Stress detection using wearable physiological and sociometric sensors. Int J Neural Syst 27: 1650041. https://doi.org/10.1142/S0129065716500416
|
| [24] |
Horvers A, Tombeng N, Bosse T, et al. (2021) Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review. Sensors 21: 7869. https://doi.org/10.3390/s21237869
|
| [25] |
Iqbal T, Elahi A, Redon P, et al. (2021) A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare. Diagnostics 11: 556. https://doi.org/10.3390/diagnostics11030556
|
| [26] |
Van Es VAA, Lopata RGP, Scilingo EP, et al. (2023) Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring. Sensors 23: 1505. https://doi.org/10.3390/s23031505
|
| [27] |
Verma P, Sood SK (2019) A comprehensive framework for student stress monitoring in fog-cloud IoT environment: M-health perspective. Med Biol Eng Comput 57: 231-244. https://doi.org/10.1007/s11517-018-1877-1
|
| [28] | Silva H, Sousa J, Gamboa H (2012) Study and Evaluation of Palmar Blood Volume Pulse for HR Monitoring in a Multimodal Framework. Proceedings of the 2nd International Workshop on Computing Paradigms for Mental Health (BIOSTEC 2012) – MindCare : 35-40. https://doi.org/10.5220/0003884900350040 |
| [29] |
Kamavuako EN (2022) On the Applications of EMG Sensors and Signals. Sensors 22: 7966. https://doi.org/10.3390/s22207966
|
| [30] |
Jarque-Bou NJ, Sancho-Bru JL, Vergara M (2021) A Systematic Review of EMG Applications for the Characterisation of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues. Sensors 21: 3035. https://doi.org/10.3390/s21093035
|
| [31] |
Amezquita-Garcia J, Bravo-Zanoguera M, Gonzalez-Navarro FF, et al. (2022) Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim. Sensors 22: 3737. https://doi.org/10.3390/s22103737
|
| [32] | Jang MH, Ahn SJ, Lee J, et al. (2018) Validity and Reliability of the Newly Developed Surface Electromyography Device for Measuring Muscle Activity during Voluntary Isometric Contraction. Comput Math Methods Med 2018: 4068493. https://doi.org/10.1155/2018/4068493 |
| [33] |
Golgouneh A, Tarvirdizadeh B (2019) Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms. Neural Comput Appl 32: 7515-7537. https://doi.org/10.1007/s00521-019-04278-7
|
| [34] |
Han L, Zhang Q, Chen X, et al. (2017) Detecting work-related stress with a wearable device. Comput Ind 90: 42-49. https://doi.org/10.1016/j.compind.2017.05.004
|
| [35] |
Peternel K, Pogačnik M, Tavčar R, et al. (2012) A presence-based context-aware chronic stress recognition system. Sensors 12: 15888-15906. https://doi.org/10.3390/s121115888
|
| [36] |
Kulkarni P, Kirkham R, McNaney R (2022) Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 22: 3893. https://doi.org/10.3390/s22103893
|
| [37] |
Ding C, Zhang Y, Ding T (2023) A systematic hybrid machine learning approach for stress prediction. Peer J Comput Sci 9: e1154. https://doi.org/10.7717/peerj-cs.1154
|
| [38] |
Nath RK, Thapliyal H (2021) Machine Learning-Based Anxiety Detection in Older Adults Using Wristband Sensors and Context Feature. SN Comput Sci 2: 359. https://doi.org/10.1007/s42979-021-00744-z
|
| [39] |
Kang M, Chai K (2022) Wearable sensing systems for monitoring mental health. Sensors 22: 994. https://doi.org/10.3390/s22030994
|
| [40] |
Chavda VP, Patel K, Patel S, et al. (2023) Artificial Intelligence and Machine Learning in Healthcare Sector. Bioinformatics Tools for Pharmaceutical Drug Product Development (eds V. Chavda, K. Anand and V. Apostolopoulos) . https://doi.org/10.1002/9781119865728.ch13
|
| [41] |
Gjoreski M, Luštrek M, Gams M, et al. (2017) Monitoring stress with a wrist device using context. J Biomed Inform 73: 159-170. https://doi.org/10.1016/j.jbi.2017.08.006
|
| [42] |
Van den Broek EL, van der Sluis F, Dijkstra T (2013) Cross-validation of bimodal health-related stress assessment. Pers. Ubiquitous Comput 17: 215-227. https://doi.org/10.1007/s00779-011-0468-z
|
| [43] |
Hellhammer DH, Stone AA, Hellhammer J, et al. (2010) Measuring stress. Encycl Behav Neurosci 2: 186-191. https://doi.org/10.1016/B978-0-08-045396-5.00188-3
|
| [44] |
Khullar V, Tiwari RG, Agarwal AK, et al. (2022) Physiological signals based anxiety detection using ensemble machine learning. Cyber Intelligence and Information Retrieval. Berlin: Springer 597-608. https://doi.org/10.1007/978-981-16-4284-5_53
|
| [45] | Issa G (2021) A new two-step ensemble learning model for improving stress prediction of automobile drivers. Int Arab J Inf Techn 18: 819-829. https://doi.org/10.34028/iajit/18/6/9 |
| [46] | Di Martino F, Delmastro F (2020) High-resolution physiological stress prediction models based on ensemble learning and recurrent neural networks. 2020 IEEE Symposium on Computers and Communications (ISCC). Piscataway: IEEE 1-6. https://doi.org/10.1109/ISCC50000.2020.9219716 |
| [47] | Lee E, Rustam F, Washington PB, et al. (2022) Racism detection by analyzing differential opinions through sentiment analysis of tweets using stacked ensemble GCR-NN model. IEEE 10: 9717-9728. https://doi.org/10.1109/ACCESS.2022.3144266 |
| [48] |
Anand RV, Md AQ, Urooj S, et al. (2023) Enhancing Diagnostic Decision-Making: Ensemble Learning Techniques for Reliable Stress Level Classification. Diagnostics 13: 3455. https://doi.org/10.3390/diagnostics13223455
|
| [49] |
Mandler G, Sarason SB (1952) A study of anxiety and learning. J Abnorm Soc Psychol 47: 166-173. https://doi.org/10.1037/h0062855
|
| [50] |
Liebert RM, Morris LW (1967) Cognitive and emotional components of test anxiety: A distinction and some initial data. Psychol Rep 20: 975-978. https://doi.org/10.2466/pr0.1967.20.3.975
|
| [51] |
Scherer KR (2009) The dynamic architecture of emotion: Evidence for the component process model. Cogn Emot 23: 1307-1351. https://doi.org/10.1080/02699930902928969
|
| [52] | Zeidner M (2014) Anxiety in Education. International Handbook of Emotions in Education. Boca Raton, FL: Taylor & Francis 265-288. |
| [53] |
Mou X, Xin Y, Song Y, et al. (2023) An empirical study on learners' learning emotion and learning effect in offline learning environment. PLoS ONE 18: e0294407. https://doi.org/10.1371/journal.pone.0294407
|
| [54] | Noushad S, Ahmed S, Ansari B, et al. (2021) Physiological biomarkers of chronic stress: A systematic review. Int J Health Sci (Qassim) 15: 46-59. |
| [55] |
Russell E, Koren G, Rieder M, et al. (2012) Hair cortisol as a biological marker of chronic stress: Current status, future directions and unanswered questions. Psychoneuroendocrinology 37: 589-601. https://doi.org/10.1016/j.psyneuen.2011.09.009
|
| [56] |
Choi M, Koo G, Seo M, et al. (2017) Wearable device-based system to monitor a driver's stress, fatigue, and drowsiness. IEEE Trans Instrum Meas 67: 634-645. https://doi.org/10.1109/TIM.2017.2779329
|
| [57] |
Weber S, Bühler J, Vanini G, et al. (2023) Identification of biopsychological trait markers in functional neurological disorders. Brain 146: 2627-2641. https://doi.org/10.1093/brain/awac442
|
| [58] |
Hellhammer DH, Wüst S, Kudielka BM (2009) Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34: 163-171. https://doi.org/10.1016/j.psyneuen.2008.10.026
|
| [59] |
Ronca V, Martinez-Levy AC, Vozzi A, et al. (2023) Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3+. Sensors 23: 5847. https://doi.org/10.3390/s23135847
|
| [60] |
Katsis CD, Katertsidis NS, Fotiadis DI (2011) An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomed Signal Process Control 6: 261-268. https://doi.org/10.1016/j.bspc.2010.12.001
|
| [61] |
Allen AP, Kennedy PJ, Cryan JF, et al. (2014) Biological and psychological markers of stress in humans: Focus on the Trier Social Stress Test. Neurosci Biobehav Rev 38: 94-124. https://doi.org/10.1016/j.neubiorev.2013.11.005
|
| [62] |
Alberdi A, Aztiria A, Basarab A (2016) Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. J Biomed Inform 59: 49-75. https://doi.org/10.1016/j.jbi.2015.11.007
|
| [63] | Wijsman J, Grundlehner B, Liu H, et al. (2011) Towards mental stress detection using wearable physiological sensors. In Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, 30 August–3 September 2011 : pp. 1798-1801. https://doi.org/10.1109/IEMBS.2011.6090512 |
| [64] |
Hemmingsson E (2018) Early childhood obesity risk factors: Socioeconomic adversity, family dysfunction, offspring distress, and junk food self-medication. Curr Obes Rep 7: 204-209. https://doi.org/10.1007/s13679-018-0310-2
|
| [65] |
Lesage FX, Berjot S, Deschamps F (2012) Clinical stress assessment using a visual analogue scale. Occup Med (Lond) 62: 600-5. https://doi.org/10.1093/occmed/kqs140
|
| [66] | Aguiló J, Ferrer-Salvans P, Garćia-Rozo A, et al. (2015) Project ES3: Attempting to quantify and measure the level of stress. Rev Neurol 61: 405-415. https://doi.org/10.33588/rn.6109.2015136 |
| [67] | Shi Y, Nguyen MH, Blitz P, et al. (2010) Personalized stress detection from physiological measurements. In Proceedings of the International Symposium on Quality of Life Technology, Las Vegas, NV, USA : 28-29. |
| [68] |
Uesato M, Nabeya Y, Akai T, et al. (2010) Salivary amylase activity is useful for assessing perioperative stress in response to pain in patients undergoing endoscopic submucosal dissection of gastric tumors under deep sedation. Gastric Cancer 13: 84-89. https://doi.org/10.1007/s10120-009-0541-8
|
| [69] |
Ullmann E, Barthel A, Petrowski K, et al. (2016) Pilot study of adrenal steroid hormones in hair as an indicator of chronic mental and physical stress. Sci Rep 6: 1-7. https://doi.org/10.1038/srep25842
|
| [70] | Sunthad K, Niitsu Y, Inoue M, et al. (2019) Brain's Stress Observation System Using 2-Channels NIRS Based on Classroom Activity. In Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA : pp. 1-4. https://doi.org/10.1109/ICCE.2019.8662117 |
| [71] |
Järvelä S, Järvenoja H, Malmberg J (2019) Capturing the dynamic and cyclical nature of regulation: Methodological progress in understanding socially shared regulation in learning. Int J Comput-Supported Collaborative Learn 14: 425-441. https://doi.org/10.1007/s11412-019-09313-2
|
| [72] |
Mollahosseini A, Hasani B, Mahoor MH (2019) AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild. IEEE Trans Affect Comput 10: 18-31. https://doi.org/10.1109/TAFFC.2017.2740923
|
| [73] | Garbarino M, Lai M, Bender D, et al. (2014) Empatica E3 – a wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In: 4th International Conference on Wireless Mobile Communication and Healthcare : 3-6. https://doi.org/10.4108/icst.mobihealth.2014.257418 |
| [74] |
Trobec R, Rashkovska A, Avbelj V (2012) Two proximal skin electrodes — a respiration rate body sensor. Sensors 12: 13813-13828. https://doi.org/10.3390/s121013813
|
| [75] |
Mishra V, Pope G, Lord S, et al. (2020) Continuous Detection of Physiological Stress with Commodity Hardware. ACM Trans Comput Healthc 1: 8. https://doi.org/10.1145/3361562
|
| [76] |
Zhang M, Zhang J, Chung AK (2016) An intelligent mobile stress management system based on wearable sensors. IEEE Trans Ind Inform 12: 4-13. https://doi.org/10.1109/TII.2016.2607150
|
| [77] | IEEE DataPort. IEEE DataPort . https://ieee-dataport.org/ |
| [78] | Open platform to support your research and enable collaboration. OSF . https://osf.io/ |
| [79] | ZenodoOpen Access and Open Data Platform. https://zenodo.org/ |
| [80] | Figshare repository. Figshare . https://figshare.com/ |
| [81] | Open data repository. Data.gov . https://data.gov/ |
| [82] | NIH-Supported Data Sharing Resources. National Library of Medicine . https://www.nlm.nih.gov/NIHbmic/domain_specific_repositories.html |
| [83] |
Burtscher J, Moraud EM, Malatesta D, et al. (2024) Exercise and gait/movement analyses in treatment and diagnosis of Parkinson's Disease. Ageing Res Rev 93. https://doi.org/10.1016/j.arr.2023.102147
|
| [84] |
Kasoju N, Remya NS, Sasi R, et al. (2023) Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology. CSIT 11: 11-30. https://doi.org/10.1007/s40012-023-00380-3
|
| [85] |
Uddin R, Koo I (2024) Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity. Appl Sci 14: 1876. https://doi.org/10.3390/app14051876
|
| [86] |
Guk K, Han G, Lim J, et al. (2019) Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare. Nanomaterials (Basel) 9: 813. https://doi.org/10.3390/nano9060813
|
| [87] |
Junaid SB, Imam AA, Balogun AO, et al. (2022) Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 10: 1940. https://doi.org/10.3390/healthcare10101940
|
| [88] |
Prieto-Avalos G, Cruz-Ramos NA, Alor-Hernández G, et al. (2022) Wearable Devices for Physical Monitoring of Heart: A Review. Biosensors (Basel) 12(5): 292. https://doi.org/10.3390/bios12050292
|
| [89] |
Vitazkova D, Foltan E, Kosnacova H, et al. (2024) Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies. Biosensors 14: 90. https://doi.org/10.3390/bios14020090
|
| [90] |
Tomasic I, Tomasic N, Trobec R, et al. (2018) Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies. Med Biol Eng Comput 56: 547-569. https://doi.org/10.1007/s11517-018-1798-z
|
| [91] | Almurashi AM, Rodriguez E, Garg SK (2023) Emerging Diabetes Technologies: Continuous Glucose Monitors/Artificial Pancreases. J Indian Inst Sci 28: 1-26. https://doi.org/10.1007/s41745-022-00348-3 |
| [92] |
Koinis L, Mobbs RJ, Fonseka RD, et al. (2022) A commentary on the potential of smartphones and other wearable devices to be used in the identification and monitoring of mental illness. Ann Transl Med 10: 1420. https://doi.org/10.21037/atm-21-6016
|
| [93] | Porciuncula F, Roto AV, Kumar D, et al. (2018) Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances. PM R 10: S220-S232. https://doi.org/10.1016/j.pmrj.2018.06.013 |
| [94] | Sun F-T, Kuo C, Cheng H-T, et al. (2010) Activity-aware mental stress detection using physiological sensors. Mobile Computing, Applications, and Services. MobiCASE 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 76. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-29336-8_16 |
| [95] |
Russell E, Koren G, Rieder M, et al. (2014) The detection of cortisol in human sweat: Implications for measurement of cortisol in hair. Ther Drug Monit 36: 30-34. https://doi.org/10.1097/FTD.0b013e31829daa0a
|
| [96] |
Karthikeyan P, Murugappan M, Yaacob S (2013) Detection of human stress using short-term ECG and HRV signals. J Mech Med Biol 13: 1350038. https://doi.org/10.1142/S0219519413500383
|
| [97] | Muaremi A, Bexheti A, Gravenhorst F, et al. (2014) Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain : pp. 185-188. https://doi.org/10.1109/BHI.2014.6864335 |
| [98] | Sandulescu V, Andrews S, Ellis D, et al. (2015) Stress detection using wearable physiological sensors. In Proceedings of the International Work-Conference on the Interplay between Natural and Artificial Computation, Elche, Spain : pp. 526-532. https://doi.org/10.1007/978-3-319-18914-7_55 |
| [99] |
Mohino-Herranz I, Gil-Pita R, Ferreira J, et al. (2015) Assessment of mental, emotional and physical stress through analysis of physiological signals using smartphones. Sensors 15: 25607-25627. https://doi.org/10.3390/s151025607
|
| [100] |
Chen L, Zhao Y, Ye P, et al. (2017) Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Syst Appl 85: 279-291. https://doi.org/10.1016/j.eswa.2017.01.040
|
| [101] | Lee DS, Chong TW, Lee BG (2016) Stress events detection of driver by wearable glove system. IEEE Sens J 17: 194-204. https://doi.org/10.1109/JSEN.2016.2625323 |
| [102] |
Wu W, Pirbhulal S, Zhang H, et al. (2018) Quantitative Assessment for Self-Tracking of Acute Stress based on Triangulation Principle in a Wearable Sensor System. IEEE J Biomed Health Inform 23: 703-713. https://doi.org/10.1109/JBHI.2018.2832069
|
| [103] | Li T, Chen Y, Chen W (2018) Daily stress monitoring using HRvariability of bathtub ecg signals. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA : pp. 2699-2702. |
| [104] |
Liu Y, Du S (2018) Psychological stress level detection based on electrodermal activity. Behav Brain Res 341: 50-53. https://doi.org/10.1016/j.bbr.2017.12.021
|
| [105] |
Arza A, Garzón-Rey JM, Lázaro J, et al. (2019) Measuring acute stress response through physiological signals: Towards a quantitative assessment of stress. Med Biol Eng Comput 57: 271-287. https://doi.org/10.1007/s11517-018-1879-z
|
| [106] |
Bönke L, Aust S, Fan Y, et al. (2019) Examining the effect of Early Life Stress on autonomic and endocrine indicators of individual stress reactivity. Neurobiol Stress 10: 100142. https://doi.org/10.1016/j.ynstr.2018.100142
|
| [107] |
Rotter M, Brandmaier S, Covic M, et al. (2018) Night shift work affects urine metabolite profiles of nurses with early chronotype. Metabolites 8: 45. https://doi.org/10.3390/metabo8030045
|
| [108] |
Can YS, Chalabianloo N, Ekiz D, et al. (2019) Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19: 1849. https://doi.org/10.3390/s19081849
|
| [109] |
Bitkina OV, Kim J, Park J, et al. (2019) Identifying traffic context using driving stress: A longitudinal preliminary case study. Sensors 19: 2152. https://doi.org/10.3390/s19092152
|
| [110] |
Kim J, Park J, Park J (2020) Development of a statistical model to classify driving stress levels using galvanic skin responses. Hum. Factors Ergon Manuf Serv Ind 30: 321-328. https://doi.org/10.1002/hfm.20843
|
| [111] |
Hoferichter F, Raufelder D (2023) Biophysiological stress markers relate differently to grit and school engagement among lower- and higher-track secondary school students. Brit J Educ Psychol 93: 174-194. https://doi.org/10.1111/bjep.12514
|
| [112] |
Larsson SC, Lee WH, Burgess S, et al. (2021) Plasma cortisol and risk of atrial fibrillation: a Mendelian randomization study. J Clin Endocrinol Metab 106: e2521-e2526. https://doi.org/10.1210/clinem/dgab219
|
| [113] | Avci II, Sahin I, Gungor B, et al. (2021) Higher copeptin levels are associated with the risk of atrial fibrillation in patients with rheumatic mitral stenosis. Acta Cardiol Sin 37: 412-419. |
| [114] |
Iqbal T, Redon-Lurbe P, Simpkin AJ, et al. (2021) A Sensitivity Analysis of Biophysiological Responses of Stress for Wearable Sensors in Connected Health. IEEE Access 9: 93567-93579. https://doi.org/10.1109/ACCESS.2021.3082423
|
| [115] | Schmidt P, Reiss A, Duerichen R, et al. (2018) Introducing wesad, a multimodal dataset for wearable stress and affect detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA : pp. 400-408. https://doi.org/10.1145/3242969.3242985 |
| [116] |
Correa JAM, Abadi MK, Sebe N, et al. (2021) Amigos: A dataset for affect, personality and mood research on individuals and groups. IEEE Trans Affect Comput 12: 479-493. https://doi.org/10.1109/TAFFC.2018.2884461
|
| [117] |
Soleymani M, Lichtenauer J, Pun T, et al. (2012) A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Trans Affect Comput 3: 42-55. https://doi.org/10.1109/T-AFFC.2011.25
|
| [118] |
Katsigiannis S, Ramzan N (2018) DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices. IEEE J Biomed Health Inform 22: 98-107. https://doi.org/10.1109/JBHI.2017.2688239
|
| [119] | Koldijk S, Sappelli M, Verberne S, et al. (2014) The swell knowledge work dataset for stress and user modeling research. Proceedings of the 16th International Conference on Multimodal Interaction; Istanbul, Turkey : pp. 291-298. https://doi.org/10.1145/2663204.2663257 |
| [120] |
Zheng W-L, Lu B-L (2015) Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks. IEEE Trans Auton Ment Dev 7: 162-175. https://doi.org/10.1109/TAMD.2015.2431497
|
| [121] |
Zheng W-L, Liu W, Lu Y, et al. (2019) EmotionMeter: A Multimodal Framework for Recognizing Human Emotions. IEEE Trans Cybern 49: 1110-1122. https://doi.org/10.1109/TCYB.2018.2797176
|
| [122] |
Zheng W-L, Lu B-L (2017) A multimodal approach to estimating vigilance using EEG and forehead EOG. J Neural Eng 14: 026017. https://doi.org/10.1088/1741-2552/aa5a98
|
| [123] |
Liu W, Qiu J-L, Zheng W-L, et al. (2021) Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition. IEEE Trans Cogn Dev Syst 14: 715-729. https://doi.org/10.1109/TCDS.2021.3071170
|
| [124] |
Liu W, Zheng W-L, Li Z, et al. (2022) Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People. J Neural Eng 19: 026012. https://doi.org/10.1088/1741-2552/ac5c8d
|
| [125] | Mannocchi I, Lamichhane K, Carli M, et al. (2022) HEROES: A Video-Based Human Emotion Recognition Database. 2022 10th European Workshop on Visual Information Processing (EUVIP), Lisbon, Portugal : pp. 1-6. https://doi.org/10.1109/EUVIP53989.2022.9922723 |
| [126] |
Koelstra S, Muhl C, Soleymani M, et al. (2012) DEAP: A Database for Emotion Analysis Using Physiological Signals. IEEE Trans Affect Comput 3: 18-31. https://doi.org/10.1109/T-AFFC.2011.15
|
| [127] |
Muñoz JE, Gouveia ER, Cameirão MS, et al. (2018) Physiolab - A multivariate physiological computing toolbox for ECG, EMG and EDA signals: A case of study of cardiorespiratory fitness assessment in the elderly population. Multimed Tools Appl 77: 11521-11546. https://doi.org/10.1007/s11042-017-5069-z
|
| [128] |
Mahmood N, Ghorbani N, Troje NF, et al. (2019) AMASS: Archive of Motion Capture As Surface Shapes. 2019 IEEE/CVF International Conference on Computer Vision (ICCV) : 5441-5450. https://doi.org/10.1109/ICCV.2019.00554
|
| [129] |
Cao H, Cooper DG, Keutmann MK, et al. (2014) CREMA-D: Crowd-sourced Emotional Multimodal Actors Dataset. IEEE Trans Affect Comput 5: 377-390. https://doi.org/10.1109/TAFFC.2014.2336244
|
| [130] | Benchekroun M, Istrate D, Zalc V, et al. (2022) Mmsd: A Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals. In Proceedings of the HEALTHINF, Online : 9-11. https://doi.org/10.5220/0010985400003123 |
| [131] |
Elmovitsky PE, Alencar P, Leatherdale ST, et al. (2021) Towards Real-Time Public Health: A Novel Mobile Health Monitoring System. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data); Orlando, FL, USA : 6049-6051. https://doi.org/10.1109/BigData52589.2021.9672059
|
| [132] | Baltrušaitis T, Robinson P, Morency L-P (2016) 1-10. https://doi.org/10.1109/WACV.2016.7477553 |
| [133] |
Keshet A, Reicher L, Bar N, et al. (2023) Wearable and digital devices to monitor and treat metabolic diseases. Nat Metab 5: 563-571. https://doi.org/10.1038/s42255-023-00778-y
|
Evgenia Lazarou, Themis P. Exarchos. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices[J]. AIMS Neuroscience, 2024, 11(2): 76-102. doi: 10.3934/Neuroscience.2024006
DownLoad: