{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:37:55Z","timestamp":1760240275267,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T00:00:00Z","timestamp":1555545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at University of Petra","award":["4\/3\/2017."],"award-info":[{"award-number":["4\/3\/2017."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Affective understanding is an area of affective computing which is concerned with advancing the ability of a computer to understand the affective state of its user. This area continues to receive attention in order to improve the human-computer interactions of automated systems and services. Systems within this area typically deal with big data from different sources, which require the attention of data engineers to collect, process, integrate and store. Although many studies are reported in this area, few look at the issues that should be considered when designing the data pipeline for a new system or study. By reviewing the literature of affective understanding systems one can deduct important issues to consider during this design process. This paper presents a design model that works as a guideline to assist data engineers when designing data pipelines for affective understanding systems, in order to avoid implementation faults that may increase cost and time. We illustrate the feasibility of this model by presenting its utilization to develop a stress detection application for drivers as a case study. This case study shows that failure to consider issues in the model causes major errors during implementation leading to highly expensive solutions and the wasting of resources. Some of these issues are emergent such as performance, thus implementing prototypes is recommended before finalizing the data pipeline design.<\/jats:p>","DOI":"10.3390\/data4020052","type":"journal-article","created":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T11:58:21Z","timestamp":1555588701000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data Engineering for Affective Understanding Systems"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5684-6772","authenticated-orcid":false,"given":"Nuha","family":"El-Khalili","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]},{"given":"May","family":"Alnashashibi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7575-9287","authenticated-orcid":false,"given":"Wael","family":"Hadi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]},{"given":"Abed Alkarim","family":"Banna","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]},{"given":"Ghassan","family":"Issa","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Petra, Amman 11196, Jordan"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,18]]},"reference":[{"key":"ref_1","unstructured":"Alexandra, J.M., Andres, L., Ocumpaugh, J., Baker, R.S., Slater, S., Paquette, L., Jiang, Y., Karumbaiah, S., Bosch, N., and Munshi, A. (2019, January 4\u20138). Affect Sequences and Learning in Betty\u2019s Brain. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, Tempe, AZ, USA."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vea, A., Mesina, M.R., Toriaga, R.P., and Padlan, N. (2017, January 25\u201327). Development of an Intelligent Agent that Detects Student\u2019s Negative Affect while Making a Computer Program. Proceedings of the International Conference on Advances in Image Processing, Bangkok, Thailand.","DOI":"10.1145\/3133264.3133293"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, C., and Tong, L. (2018). Developing Automatic Form and Design System Using Integrated Grey Relational Analysis and Affective Engineering. Appl. Sci., 8.","DOI":"10.3390\/app8010091"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sarsenbayeva, Z., Berkel, N., Hettiachchi, D., Jiang, W., Dingler, T., Velloso, E., Kostakos, V., and Goncalves, J. (2019). Measuring the Effects of Stress on Mobile Interaction. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3.","DOI":"10.1145\/3314411"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1080\/10447318.2015.1064638","article-title":"Toward a Taxonomy of Affective Computing","volume":"31","author":"Schwark","year":"2015","journal-title":"Int. J. Hum. Comput. Interact."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TAFFC.2016.2553038","article-title":"BAUM-1: A Spontaneous Audio-Visual Face Database of Affective and Mental States","volume":"8","author":"Zhalehpour","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting Stress During Real-World Driving Tasks Using Physiological Sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","unstructured":"Schie\u00dfl, C. (2007, January 27\u201330). Stress and Strain while driving. Proceedings of the Young Researchers Seminar 2007, European Conference of Transport Research Institutes (ECTRI), Brno, Czech Republic."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vander Sloten, J., Verdonck, P., Nyssen, M., and Haueisen, J. (2008, January 23\u201327). Influence of Mental Stress on Heart Rate and Heart Rate Variability. Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings, Antwerp, Belgium.","DOI":"10.1007\/978-3-540-89208-3"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bundele, M., and Banerjee, R. (2009, January 14\u201316). Detection of fatigue of vehicular driver using skin conductance and oximetry pulse: A neural network approach. Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services, Kuala Lumpur, Malaysia.","DOI":"10.1145\/1806338.1806478"},{"key":"ref_11","first-page":"155","article-title":"Trapezius Muscle EMG as Predictor of Mental Stress","volume":"12","author":"Wijsman","year":"2010","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"ref_12","first-page":"617210","article-title":"Towards Driver\u2019s State Recognition on Real Driving Conditions","volume":"2011","author":"Rigas","year":"2011","journal-title":"Int. J. Veh. Technol."},{"key":"ref_13","unstructured":"Bakker, J., Pechenizkiy, M., and Sidorova, N. (2011, January 11). What\u2019s your current stress level?. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ertin, E., Stohs, N., Kumar, S., Raij, A., Al\u2019Absi, M., Shah, S., and Jeong, J.W. (2011, January 1\u20134). AutoSense: Unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (SenSys 2011), Seattle, WA, USA.","DOI":"10.1145\/2070942.2070970"},{"key":"ref_15","unstructured":"D\u2019Mello, S., and Calvo, R. (2011, January 9\u201312). Call Center Stress Recognition with Person-Specific Models. Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII 2011), Memphis, TN, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Paschero, M., Vescovo, G.D., Benucci, L., Rizzi, A., Santello, M., Fabbri, G., and Mascioloi, F. (2012, January 28\u201331). A real time classifier for emotion and stress recognition in a vehicle driver. Proceedings of the International Symposium on Industrial Electronics (ISIE), Hangzhou, China.","DOI":"10.1109\/ISIE.2012.6237345"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schneegass, S., Pfleging, B., Broy, N., Schmidt, A., and Heinrich, F. (2013, January 28\u201330). A Data Set of Real World Driving to Assess Driver Workload. Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI \u201913), Eindhoven, The Netherlands.","DOI":"10.1145\/2516540.2516561"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Marcos-Ramiro, A., Pizarro-Perez, D., Marron-Romera, M., and Gatica-Perez, D. (2014, January 12\u201316). Automatic Blinking Detection towards Stress Discovery. Proceedings of the 16th International Conference on Multimodal Interaction, Istanbul, Turkey.","DOI":"10.1145\/2663204.2663239"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luijcks, R., Hermens, H., Bodar, L., Vossen, C., and Lousberg, R. (2014). Experimentally Induced Stress Validated by EMG Activity. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0095215"},{"key":"ref_20","unstructured":"Liu, D., and Ulrich, M. (2018, June 04). Listen to Your Heart: Stress Prediction Using Consumer Heart Rate Sensors. Available online: http:\/\/cs229.stanford.edu\/proj2013\/LiuUlrich-ListenToYourHeart-StressPredictionUsingConsumerHeartRateSensors.pdf."},{"key":"ref_21","unstructured":"Sun, D., Paredes, P., and Canny, J. (May, January 26). MouStress: Detecting Stress from Mouse Motion. Proceedings of the SIGCHI Conference on Human Factors in Computing Systemss, Toronto, ON, Canada."},{"key":"ref_22","first-page":"1009","article-title":"The level of driver personality and stress experienced as factors influencing behavior on the road","volume":"Volume 168","author":"Filipczak","year":"2015","journal-title":"Sustainable Development"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hovsepian, K., Al\u2019Absi, M., Ertin, E., Kamarck, T., Nakajima, M., and Kumar, S. (2015, January 7\u201311). cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment. Proceedings of the ACM International Conference on Ubiquitous Computing (UbiComp 2015), Osaka, Japan.","DOI":"10.1145\/2750858.2807526"},{"key":"ref_24","unstructured":"EL Haouij, N., Ghozi, R., Poggi, J., Ghalila, S., and Jaidane, M. (2015, January 1\u20135). Feature extraction and selection of electrodermal reaction towards stress level recognition: Two real-world driving experiences. Proceedings of the 47e Journ\u00e9es de Statistique de la Soci\u00e9t\u00e9 Fran\u00e7aise de Statistique, Lille, France."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MWC.2015.7054715","article-title":"AIWAC: Affective interaction through wearable computing and cloud technology","volume":"22","author":"Chen","year":"2015","journal-title":"IEEE Wirel. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1109\/TITS.2015.2445314","article-title":"A mobile sensing approach to stress detection and memory activation for public bus drivers","volume":"16","author":"Rodrigues","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Boateng, G., and Kotz, D. (2016, January 4\u20136). StressAware: An App for Real-Time Stress Monitoring on the Amulet Wearable Platform. Proceedings of the IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA.","DOI":"10.1109\/URTC.2016.8284068"},{"key":"ref_28","unstructured":"Aigrain, J., Spodenkiewicz, M., Dubuisson, S., Detyniecki, M., Cohen, D., and Chetouani, M. (2016). Multimodal stress detection from multiple assessments. IEEE Trans. Affect. Comput., 99."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mottelson, A., and Hornb\u00e6k, K. (2016, January 12\u201316). An Affect Detection Technique using Mobile Commodity Sensors in the Wild. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing UbiComp\u201916, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971654"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/17538947.2016.1239771","article-title":"Big Data and cloud computing: innovation opportunities and challenges","volume":"10","author":"Yanga","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/3314410","article-title":"W!NCE: Unobtrusive Sensing of Upper Facial Action Units with EOG-based Eyewear","volume":"3","author":"Rostaminia","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Clay, A., Couture, N., and Nigay, L. (2009, January 10\u201312). Engineering affective computing: A unifying software architecture. Proceedings of the Affective Computing and Intelligent Interaction and Workshops, Amsterdam, The Netherlands.","DOI":"10.1109\/ACII.2009.5349541"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kandel, S., Paepcke, A., Hellerstein, J., and Heer, J. (2011, January 7\u201312). Wrangler: Interactive Visual Specification of Data Transformation Scripts. Proceedings of the ACM CHI Conference on Human Factors, Vancouver, BC, Canada.","DOI":"10.1145\/1978942.1979444"},{"key":"ref_34","first-page":"13","article-title":"Data Engineering: Using Data Analysis Techniques in Producing Data Driven Products","volume":"161","author":"Nnebedum","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25607","DOI":"10.3390\/s151025607","article-title":"Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones","volume":"15","author":"Ferreira","year":"2015","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1145\/3314420","article-title":"Towards a Diffraction-based Sensing Approach on Human Activity Recognition","volume":"3","author":"Zhang","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rahman, T., Zhang, M., Voida, S., and Choudhury, T. (2014, January 20\u201323). Towards Accurate Non-Intrusive Recollection of Stress Levels Using Mobile Sensing and Contextual Recall. Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, Oldenburg, Germany.","DOI":"10.4108\/icst.pervasivehealth.2014.254957"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Witten","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_39","unstructured":"Barua, S., Begun, S., and Ahmed, M.U. (2015, January 2\u20134). Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals. Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, V\u00e4ster\u00e5s, Sweden. Studies in Health Technology and Informatics."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"30653","DOI":"10.3390\/s151229822","article-title":"A review of intelligent driving style analysis systems and related artificial intelligence algorithms","volume":"15","author":"Meiring","year":"2015","journal-title":"Sensors"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/52\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:46:25Z","timestamp":1760186785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,18]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["data4020052"],"URL":"https:\/\/doi.org\/10.3390\/data4020052","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2019,4,18]]}}}