research-article

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Evaluating the Reproducibility of Physiological Stress Detection Models

Authors:

Jeffrey Rogers,

Ching-Hua Chen,

David KotzAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 4

Article No.: 147, Pages 1 - 29

https://doi.org/10.1145/3432220

Published: 18 December 2020 Publication History

Abstract

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics.

This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper's thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions.

References

[1]

M Al'Absi and D K Arnett. 2000. Adrenocortical responses to psychological stress and risk for hypertension. Biomedicine & pharmacotherapy 54, 5 (2000), 234--244.

[2]

APA. 2019. Stress in America 2019. https://www.apa.org/news/press/releases/stress. [Online; accessed 10-Oct-2019].

[3]

Brinnae Bent, Benjamin A. Goldstein, Warren A. Kibbe, and Jessilyn P. Dunn. 2020. Investigating sources of inaccuracy in wearable optical heart rate sensors. npj Digital Medicine 3, 1 (Dec. 2020), 1--9. https://doi.org/10.1038/s41746-020-0226-6

[4]

J Bergstra, D Yamins, and D D Cox. 2013. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 (ICML'13). JMLR.org, I-115-I-123.

[5]

George E Billman. 2013. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. 26 pages. https://doi.org/10.3389/fphys.2013.00026

[6]

George Boateng, Vivian Genaro Motti, Varun Mishra, John A. Batsis, Josiah Hester, and David Kotz. 2019. Experience: Design, Development and Evaluation of a Wearable Device for mHealth Applications. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom). https://doi.org/10.1145/3300061.3345432

Digital Library

[7]

JJ Braithwaite, DG Watson, Jones Robert, and Rowe Mickey. 2013. A Guide for Analysing Electrodermal Activity (EDA) & Skin Conductance Responses (SCRs) for Psychological Experiments. Psychophysiology 49, 8 (2013), 1--42. https://www.birmingham.ac.uk/documents/college-les/psych/saal/guide-electrodermal-activity.pdf

[8]

Gillian Butler. 1993. Definitions of stress. Occasional paper (Royal College of General Practitioners) 61 (1993), 1.

[9]

Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2, 3 (2011), 1--27.

Digital Library

[10]

Grace Chen, Varun Mishra, and Ching-Hua Chen. 2019. Temporal Factors of Listening to Music on Stress Reduction. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC '19 Adjunct). ACM, New York, NY, USA, 907--914. https://doi.org/10.1145/3341162.3346272

[11]

Jongyoon Choi, B Ahmed, and Ricardo Gutierrez-Osuna. 2012. Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Transactions on Information Technology in Biomedicine 16, 2 (2012), 279--286. https://doi.org/10.1109/TITB.2011.2169804

Digital Library

[12]

George P Chrousos and Philip W Gold. 1992. The concepts of stress and stress system disorders: overview of physical and behavioral homeostasis. JAMA 267, 9 (1992), 1244--1252.

[13]

Luca Citi. 2020. cvxEDA. https://github.com/lciti/cvxEDA. [Online; accessed 04-February-2020; Git commit 7928444].

[14]

Sheldon Cohen, Tom Kamarck, and Robin Mermelstein. 1983. A global measure of perceived stress. Journal of Health and Social Behavior (1983), 385--396.

[15]

Sharon M Crook, Andrew P Davison, and Hans E Plesser. 2013. Learning from the past: approaches for reproducibility in computational neuroscience. In 20 Years of Computational Neuroscience. Springer, 73--102.

[16]

Elena Di Lascio, Shkurta Gashi, and Silvia Santini. 2018. Unobtrusive Assessment of Students' Emotional Engagement during Lectures Using Electrodermal Activity Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 3, Article Article 103 (Sept. 2018), 21 pages. https://doi.org/10.1145/3264913

Digital Library

[17]

Begum Egilmez, Emirhan Poyraz, Wenting Zhou, Gokhan Memik, Peter Dinda and Nabil Alshurafa. 2017. UStress: Understanding college student subjective stress using wrist-based passive sensing. In IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 673--678. https://doi.org/10.1109/PERCOMW.2017.7917644

[18]

Emaptica. 2018. Empatica E4. https://www.empatica.com/en-eu/research/e4/. [Online; accessed 06-August-2020].

[19]

Emre Ertin, Nathan Stohs, Santosh Kumar, Andrew Raij, Mustafa al'Absi, and Siddharth Shah. 2011. AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys). ACM, 274--287. https://doi.org/10.1145/2070942.2070970

Digital Library

[20]

Association for Computing Machinery. 2016. Artifact Review and Badging. https://www.acm.org/publications/policies/artifact-review-badging. [Online; accessed 04-August-2020].

[21]

H. Gao, A. Yüce, and J. Thiran. 2014. Detecting emotional stress from facial expressions for driving safety. In 2014 IEEE International Conference on Image Processing (ICIP). 5961--5965.

[22]

Maurizio Garbarino, Matteo Lai, Simone Tognetti, Rosalind Picard, and Daniel Bender. 2014. Empatica E3 - A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In Proceedings of the 4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies". ICST. https://doi.org/10.4108/icst.mobihealth.2014.257418

[23]

Enrique Garcia-Ceja, Venet Osmani, and Oscar Mayora. 2016. Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step. IEEE Journal of Biomedical and Health Informatics 20, 4 (Jul. 2016), 1053--1060. https://doi.org/10.1109/JBHI.2015.2446195

[24]

Everette S. Gardner. 1985. Exponential smoothing: The state of the art. Journal of Forecasting 4, 1 (Jan. 1985), 1--28. https://doi.org/10.1002/for.3980040103

[25]

Shkurta Gashi, Elena Di Lascio, and Silvia Santini. 2019. Using Unobtrusive Wearable Sensors to Measure the Physiological Synchrony Between Presenters and Audience Members. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 1, Article Article 13 (March 2019), 19 pages. https://doi.org/10.1145/3314400

Digital Library

[26]

Madeline Gaynor, Abbey Sawyer, Sue Jenkins, and Jamie Wood. 2019. Variable agreement between wearable heart rate monitors during exercise in cystic fibrosis. ERJ Open Research 5, 4 (Oct. 2019), 00006--2019. https://doi.org/10.1183/23120541.00006-2019

[27]

Martin Gjoreski, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. 2016. Continuous Stress Detection Using a Wrist Device: In Laboratory and Real Life. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'16 Adjunct). ACM Press, 1185--1193. https://doi.org/10.1145/2968219.2968306

Digital Library

[28]

Martin Gjoreski, Mitja Luštrek, Matjaž Gams, and Hristijan Gjoreski. 2017. Monitoring stress with a wrist device using context. Journal of Biomedical Informatics 73 (Sep. 2017), 159--170. https://doi.org/10.1016/j.jbi.2017.08.006

Digital Library

[29]

Alberto Greco, Gaetano Valenza, Antonio Lanata, Enzo Pasquale Scilingo, and Luca Citi. 2015. cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering 63, 4 (2015), 797--804.

[30]

Kristina Grifantini. 2010. Sensor Detects Emotions through the Skin. https://www.technologyreview.com/s/421316/sensor-detects-emotions-through-the-skin/

[31]

J. A. Hanley and B. J. McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 1 (1982), 29--36. https://doi.org/10.1148/radiology.143.1.7063747

[32]

Tian Hao, Henry Chang, Marion Ball, Kun Lin, and Xinxin Zhu. 2018. cHRV uncovering daily stress dynamics using bio-signal from consumer wearables. In MEDINFO 2017: Precision Healthcare Through Informatics: Proceedings of the 16th World Congress on Medical and Health Informatics, Vol. 245. IOS Press, 98.

[33]

Tian Hao, Jeffrey Rogers, Hung-Yang Chang, Marion Ball, Kimberly Walter, Si Sun, Ching-Hua Chen, and Xinxin Zhu. 2017. Towards Precision Stress Management: Design and Evaluation of a Practical Wearable Sensing System for Monitoring Everyday Stress. iproc 3, 1 (22 Sep 2017), e15. https://doi.org/10.2196/iproc.8441

[34]

Tian Hao, Kimberly N Walter, Marion J Ball, Hung-yang Chang, Si Sun, and Xinxin Zhu. 2017. StressHacker: Towards Practical Stress Monitoring in the Wild with Smartwatches. In AMIA Annual Symposium. Washington D.C.

[35]

J A Healey and R W Picard. 2005. Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Transactions on Intelligent Transportation Systems 6, 2 (Jun. 2005), 156--166. https://doi.org/10.1109/TITS.2005.848368

Digital Library

[36]

Dirk H Hellhammer, Stefan Wüst, and Brigitte M Kudielka. 2009. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34, 2 (2009), 163--171.

[37]

Javier Hernandez, Rob R. Morris, and Rosalind W. Picard. 2011. Call center stress recognition with person-specific models. Vol. 6974. Springer, Berlin, Heidelberg. 125-134 pages. https://doi.org/10.1007/978-3-642-24600-5_16

[38]

Karen Hovsepian, Mustafa Al'Absi, Emre Ertin, Thomas Kamarck, Motohiro Nakajima, and Santosh Kumar. 2015. cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (UbiComp '15). ACM, 493--504. https://doi.org/10.1145/2750858.2807526

Digital Library

[39]

Zachary D. King, Judith Moskowitz, Begum Egilmez, Shibo Zhang, Lida Zhang, Michael Bass, John Rogers, Roozbeh Ghaffari, Laurie Wakschlag, and Nabil Alshurafa. 2019. micro-Stress EMA: A Passive Sensing Framework for Detecting In-the-wild Stress in Pregnant Mothers. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 91 (Sept. 2019), 22 pages. https://doi.org/10.1145/3351249

Digital Library

[40]

Martin Kusserow, Oliver Amft, and Gerhard Troster. 2013. Monitoring Stress Arousal in the Wild. IEEE Pervasive Computing 12, 2 (April 2013), 28--37. https://doi.org/10.1109/MPRV.2012.56

Digital Library

[41]

Sylvain Laborde, Emma Mosley, and Julian F Thayer. 2017. Heart rate variability and cardiac vagal tone in psychophysiological research - Recommendations for experiment planning, data analysis, and data reporting. 213 pages. https://doi.org/10.3389/fpsyg.2017.00213

[42]

Hong Lu, Denise Frauendorfer, Mashfiqui Rabbi, Marianne S Mast, Gokul T Chittaranjan, Andrew T Campbell, Daniel G Perez, and Tanzeem Choudhury. 2012. StressSense: detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp'12). https://doi.org/10.1145/2370216.2370270

Digital Library

[43]

MC10. 2019. BiostampRC. https://www.mc10inc.com/our-products/biostamprc. [Online; accessed 06-May-2020].

[44]

Bruce S McEwen and Eliot Stellar. 1993. Stress and the individual: mechanisms leading to disease. Archives of Internal Medicine 153, 18 (1993), 2093--2101.

[45]

Varun Mishra, Tian Hao, Si Sun, Kimberly N. Walter, Marion J. Ball, Ching-Hua Chen, and Xinxin Zhu. 2018. Investigating the Role of Context in Perceived Stress Detection in the Wild. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - UbiComp '18. ACM Press, New York, New York, USA, 1708--1716. https://doi.org/10.1145/3267305.3267537

Digital Library

[46]

Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. 2018. The Case for a Commodity Hardware Solution for Stress Detection. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - UbiComp '18. ACM Press, 1717--1728. https://doi.org/10.1145/3267305.3267538

Digital Library

[47]

Varun Mishra, Gunnar Pope, Sarah Lord, Stephanie Lewia, Byron Lowens, Kelly Caine, Sougata Sen, Ryan Halter, and David Kotz. 2020. Continuous Detection of Physiological Stress with Commodity Hardware. ACM Transactions on Computing for Healthcare 1, 2 (Apr. 2020), 1--30. https://doi.org/10.1145/3361562

Digital Library

[48]

Amir Muaremi, Agon Bexheti, Franz Gravenhorst, Bert Arnrich, and Gerhard Troster. 2014. Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 185--188. https://doi.org/10.1109/BHI.2014.6864335

[49]

Eduardo Ogasawara, Leonardo C Martinez, Daniel De Oliveira, Geraldo Zimbrão, Gisele L Pappa, and Marta Mattoso. 2010. Adaptive normalization: A novel data normalization approach for non-stationary time series. In The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.

[50]

Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. 2019. Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data. arXiv preprint arXiv:1902.07892 (2019).

[51]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

[52]

Kurt Plarre, Andrew Raij, Syed Monowar Hossain, Amin Ahsan Ali, Motohiro Nakajima, Mustafa Absi, Emre Ertin, Thomas Kamarck, Santosh Kumar, Marcia Scott, Daniel Siewiorek, Asim Smailagic, and Lorentz E Wittmers. 2011. Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. 97--108. http://ieeexplore.ieee.org/abstract/document/5779068/

[53]

John C. Platt. 1999. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS. MIT Press, 61--74.

[54]

Hans E Plesser. 2018. Reproducibility vs. replicability: a brief history of a confused terminology. Frontiers in neuroinformatics 11 (2018), 76.

[55]

Polar. 2017. Polar H7. https://support.polar.com/us-en/support/H7_heart_rate_sensor. [Online; accessed 04-August-2020].

[56]

Gunnar C Pope, Varun Mishra, Stephanie Lewia, Byron Lowens, David Kotz, Sarah Lord, and Ryan Halter. 2018. An Ultra-Low Resource Wearable EDA Sensor Using Wavelet Compression. In Proceedings of the IEEE Conference on Body Sensor Networks (BSN). 193--196. https://doi.org/10.1109/BSN.2018.8329691

[57]

R Rosmond and P Björntorp. 1998. Endocrine and metabolic aberrations in men with abdominal obesity in relation to anxio-depressive infirmity. Metabolism 47, 10 (1998), 1187--1193.

[58]

Ensar Arif Sağbaş, Serdar Korukoglu, and Serkan Balli. 2020. Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques. Journal of medical systems 44, 4 (Feb. 2020), 68. https://doi.org/10.1007/s10916-020-1530-z

[59]

Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin Marlin, Emre Ertin, Mustafa Al'Absi, and Santosh Kumar. 2015. PuffMarker: A multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, New York, New York, USA, 999--1010. https://doi.org/10.1145/2750858.2806897

Digital Library

[60]

Akane Sano and Rosalind W Picard. 2013. Stress Recognition Using Wearable Sensors and Mobile Phones. In Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, 671--676. https://doi.org/10.1109/ACII.2013.117

[61]

Hillol Sarker, Inbal Nahum-Shani, Mustafa Al'Absi, Santosh Kumar, Matthew Tyburski, Md M Rahman, Karen Hovsepian, Moushumi Sharmin, David H Epstein, Kenzie L Preston, C Debra Furr-Holden, and Adam Milam. 2016. Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI). ACM Press, 4489--4501. https://doi.org/10.1145/2858036.2858218

Digital Library

[62]

Philip Schmidt, Attila Reiss, Robert Duerichen, Claus Marberger, and Kristof Van Laerhoven. 2018. Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. In Proceedings of the 20th ACM International Conference on Multimodal Interaction (ICMI '18). ACM, New York, NY, USA, 400--408. https://doi.org/10.1145/3242969.3242985

Digital Library

[63]

Fred Shaffer and J P Ginsberg. 2017. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health 5 (2017), 258. https://doi.org/10.3389/fpubh.2017.00258

[64]

Fred Shaffer, Rollin McCraty, and Christopher L Zerr. 2014. A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Frontiers in psychology 5 (2014), 1040.

[65]

Fernando Silveira, Brian Eriksson, Anmol Sheth, and Adam Sheppard. 2013. Predicting Audience Responses to Movie Content from Electro-Dermal Activity Signals. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '13). Association for Computing Machinery, New York, NY, USA, 707--716. https://doi.org/10.1145/2493432.2493508

Digital Library

[66]

Feng-Tso Sun, Cynthia Kuo, Heng-Tze Cheng, Senaka Buthpitiya, Patricia Collins, and Martin Griss. 2012. Activity-aware Mental Stress Detection Using Physiological Sensors. In Mobile Computing, Applications, and Services, Vol. 76. 1--20. https://doi.org/10.1007/978-3-642-29336-8_16

[67]

Terumi Umematsu, Akane Sano, Sara Taylor, and Rosalind W. Picard. 2019. Improving students' daily life stress forecasting using lstm neural networks. In 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BHI.2019.8834624

[68]

Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students Using Smartphones. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). ACM. https://doi.org/10.1145/2632048.2632054

Digital Library

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Index Terms

Evaluating the Reproducibility of Physiological Stress Detection Models
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 4

December 2020

1356 pages

EISSN:2474-9567

DOI:10.1145/3444864

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Published: 18 December 2020

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Park ELee DHan YDiefendorff JLee U(2024)Hide-and-seek: Detecting Workers' Emotional Workload in Emotional Labor Contexts Using Multimodal SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785938:3(1-28)Online publication date: 9-Sep-2024
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https://doi.org/10.1109/ICASSP48485.2024.10447599
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