{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:44:12Z","timestamp":1774586652516,"version":"3.50.1"},"reference-count":123,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T00:00:00Z","timestamp":1604793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.<\/jats:p>","DOI":"10.3390\/s20216367","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"6367","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5916-8391","authenticated-orcid":false,"given":"Agata","family":"Ko\u0142akowska","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9054-9149","authenticated-orcid":false,"given":"Wioleta","family":"Szwoch","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4683-4727","authenticated-orcid":false,"given":"Mariusz","family":"Szwoch","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gdansk, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"ref_1","first-page":"386","article-title":"Sensors and Mobile Phones: Evolution and State-of-the-Art","volume":"66","author":"Ali","year":"2014","journal-title":"Pak. J. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/SURV.2012.031412.00077","article-title":"Mobile Phone Sensing Systems: A Survey","volume":"15","author":"Khan","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_3","unstructured":"Muaremi, A., Arnrich, B., and Tr\u00f6ster, G. (2012, January 18). A Survey on Measuring Happiness with Smart Phones. Proceedings of the 6th International Workshop on Ubiquitous Health and Wellness (Part of Pervasive 2012 Conference), Newcastle, UK."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/MPRV.2016.36","article-title":"Opportunistic and Context-Aware Affect Sensing on Smartphones","volume":"15","author":"Rana","year":"2016","journal-title":"IEEE Pervasive Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.cosrev.2017.07.002","article-title":"A survey on mobile affective computing","volume":"25","author":"Politou","year":"2017","journal-title":"Comput. Sci. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Szwoch, M. (2016, January 11\u201314). Evaluation of affective intervention process in development of affect-aware educational video games. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland.","DOI":"10.15439\/2016F529"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/978-3-030-03658-4_13","article-title":"Using Different Information Channels for Affect-Aware Video Games\u2014A Case Study","volume":"Volume 892","author":"Szwoch","year":"2019","journal-title":"Image Processing and Communications Challenges 10"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Landowska, A., Szwoch, M., and Szwoch, W. (2016). Methodology of Affective Intervention Design for Intelligent Systems. Interact. Comput., 28.","DOI":"10.1093\/iwc\/iwv047"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A., Landowska, A., Szwoch, M., Szwoch, W., and Wr\u00f3bel, M.R. (2013, January 6\u20138). Emotion recognition and its application in software engineering. Proceedings of the 2013 6th International Conference on Human System Interactions (HSI), Gdansk, Poland.","DOI":"10.1109\/HSI.2013.6577877"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A. (2016, January 11\u201314). Towards detecting programmers\u2019 stress on the basis of keystroke dynamics. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland.","DOI":"10.15439\/2016F263"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1037\/h0030377","article-title":"Constants across cultures in the face and emotion","volume":"17","author":"Ekman","year":"1971","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/TPAMI.2008.52","article-title":"A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions","volume":"31","author":"Zeng","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Hussain, A., Liu, D., and Wang, Z. (2012). Survey of the Facial Expression Recognition Research. Advances in Brain Inspired Cognitive Systems, Springer.","DOI":"10.1007\/978-3-642-31561-9"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/TPAMI.2014.2366127","article-title":"Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition","volume":"37","author":"Sariyanidi","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mehta, D., Siddiqui, M.F.H., and Javaid, A. (2018). Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors, 18.","DOI":"10.3390\/s18020416"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xhafa, F., Patnaik, S., and Zomaya, A.Y. (2018). Facial Expression Recognition Based on Deep Learning: A Survey. Advances in Intelligent Systems and Interactive Applications, Springer International Publishing.","DOI":"10.1007\/978-3-319-69096-4"},{"key":"ref_17","unstructured":"Li, S., and Deng, W. (2020). Deep Facial Expression Recognition: A Survey. IEEE Trans. Affect. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TSMCC.2007.893280","article-title":"Gesture Recognition: A Survey","volume":"37","author":"Mitra","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_19","first-page":"1","article-title":"Automatic Affect Perception Based on Body Gait and Posture: A Survey","volume":"9","author":"Naghdy","year":"2017","journal-title":"Int. J. Soc. Robot."},{"key":"ref_20","unstructured":"Noroozi, F., Kaminska, D., Corneanu, C., Sapinski, T., Escalera, S., and Anbarjafari, G. (2018). Survey on Emotional Body Gesture Recognition. IEEE Trans. Affect. Comput."},{"key":"ref_21","unstructured":"Xu, S., Fang, J., Hu, X., Ngai, E., Guo, Y., Leung, V.C.M., Cheng, J., and Hu, B. (2020). Emotion Recognition From Gait Analyses: Current Research and Future Directions. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Furey, E., and Blue, J. (2019, January 1\u20134). The Emotographic Iceberg: Modelling Deep Emotional Affects Utilizing Intelligent Assistants and the IoT. Proceedings of the 2019 19th International Conference on Computational Science and Its Applications (ICCSA), Saint Petersburg, Russia.","DOI":"10.1109\/ICCSA.2019.00037"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.patcog.2010.09.020","article-title":"Survey on speech emotion recognition: Features, classification schemes, and databases","volume":"44","author":"Ayadi","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_24","first-page":"5760","article-title":"Extraction of Emotions from Speech\u2014A Survey","volume":"12","author":"Aeluri","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s13278-018-0505-2","article-title":"Emotion Detection from Text and Speech\u2014A Survey","volume":"8","author":"Sailunaz","year":"2018","journal-title":"Soc. Netw. Anal. Min. SNAM"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sebe, N., Cohen, I., Gevers, T., and Huang, T. (2004). Multimodal approaches for emotion recognition: A survey. Proc. SPIE Int. Soc. Opt. Eng., 5670.","DOI":"10.1117\/12.600746"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s12193-009-0025-5","article-title":"Multimodal Emotion Recognition in Speech-based Interaction Using Facial Expression, Body Gesture and Acoustic Analysis","volume":"3","author":"Kessous","year":"2009","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sharma, G., and Dhall, A. (2020). A Survey on Automatic Multimodal Emotion Recognition in the Wild, Springer.","DOI":"10.1007\/978-3-030-51870-7_3"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.biopsycho.2010.03.010","article-title":"Autonomic Nervous System Activity in Emotion: A Review","volume":"84","author":"Kreibig","year":"2010","journal-title":"Biol. Psychol."},{"key":"ref_30","unstructured":"Ko\u0142akowska, A., Landowska, A., Szwoch, M., Szwoch, W., and Wr\u00f3bel, M. (2015). Modeling Emotions for Affect-Aware Applications, Faculty of Management University of Gdansk."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Landowska, A. (2018). Towards New Mappings between Emotion Representation Models. Appl. Sci., 8.","DOI":"10.3390\/app8020274"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"272916","DOI":"10.1155\/2013\/272916","article-title":"A Study of Mobile Sensing Using Smartphones","volume":"2013","author":"Liu","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.measurement.2018.12.014","article-title":"A sensor-centric survey on the development of smartphone measurement and sensing systems","volume":"135","author":"Grossi","year":"2019","journal-title":"Measurement"},{"key":"ref_34","unstructured":"(2020, September 30). Sensors Overview. Available online: developer.android.com."},{"key":"ref_35","unstructured":"(2020, September 30). Compare iPhone Models. Available online: www.apple.com."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szwoch, M., and Pieniazek, P. (2015, January 25\u201327). Facial emotion recognition using depth data. Proceedings of the 2015 8th International Conference on Human System Interaction (HSI), Warsaw, Poland.","DOI":"10.1109\/HSI.2015.7170679"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"13376","DOI":"10.1016\/j.eswa.2012.05.065","article-title":"Multimodal Behavioral Analysis for Non-Invasive Stress Detection","volume":"39","author":"Carneiro","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_38","unstructured":"Trojahn, M., Arndt, F., Weinmann, M., and Ortmeier, F. (2013, January 4\u20137). Emotion Recognition through Keystroke Dynamics on Touchscreen Keyboards. Proceedings of the ICEIS, Angers, France."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hossain, R.B., Sadat, M., and Mahmud, H. (2014, January 22\u201323). Recognition of human affection in Smartphone perspective based on accelerometer and user\u2019s sitting position. Proceedings of the 2014 17th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCITechn.2014.7073097"},{"key":"ref_40","first-page":"23","article-title":"Emotion Detection from Natural Walking","volume":"Volume 9567","author":"Cui","year":"2016","journal-title":"Revised Selected Papers of the Second International Conference on Human Centered Computing"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dai, D., Liu, Q., and Meng, H. (2016, January 13\u201315). Can your smartphone detect your emotion?. Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, China.","DOI":"10.1109\/FSKD.2016.7603434"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Exposito, M., Hernandez, J., and Picard, R.W. (2018, January 3\u20136). Affective Keys: Towards Unobtrusive Stress Sensing of Smartphone Users. Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct, Barcelona, Spain.","DOI":"10.1145\/3236112.3236132"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ruensuk, M., Oh, H., Cheon, E., Oakley, I., and Hong, H. (2019, January 5). Detecting Negative Emotions during Social Media Use on Smartphones. Proceedings of the Asian CHI Symposium 2019: Emerging HCI Research Collection, Glasgow, UK.","DOI":"10.1145\/3309700.3338442"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tikadar, S., Kazipeta, S., Ganji, C., and Bhattacharya, S. (2017). A Minimalist Approach for Identifying Affective States for Mobile Interaction Design. Human-Computer Interaction\u2014INTERACT 2017, Springer International Publishing.","DOI":"10.1007\/978-3-319-67744-6_1"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Maramis, C., Stefanopoulos, L., Chouvarda, I., and Maglaveras, N. (2018). Emotion Recognition from Haptic Touch on Android Device Screens. Precision Medicine Powered by pHealth and Connected Health, Springer.","DOI":"10.1007\/978-981-10-7419-6_34"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tikadar, S., and Bhattacharya, S. (2019). A Novel Method to Build and Validate an Affective State Prediction Model from Touch-Typing. Human-Computer Interaction\u2013INTERACT 2019, Springer International Publishing.","DOI":"10.1007\/978-3-030-29390-1_6"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sarsenbayeva, Z., van 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_48","doi-asserted-by":"crossref","first-page":"13511","DOI":"10.1109\/JSEN.2020.3004399","article-title":"Motion Reveal Emotions: Identifying Emotions from Human Walk Using Chest Mounted Smartphone","volume":"20","author":"Hashmi","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wampfler, R., Klingler, S., Solenthaler, B., Schinazi, V.R., and Gross, M. (2020, January 25\u201330). Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3313831.3376504"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bachmann, A., Klebsattel, C., Budde, M., Riedel, T., Beigl, M., Reichert, M., Santangelo, P., and Ebner-Priemer, U. (2015, January 9\u201311). How to Use Smartphones for Less Obtrusive Ambulatory Mood Assessment and Mood Recognition. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing, Osaka, Japan.","DOI":"10.1145\/2800835.2804394"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lee, H., Choi, Y.S., Lee, S., and Park, I.P. (2012, January 14\u201317). Towards unobtrusive emotion recognition for affective social communication. Proceedings of the 2012 IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2012.6181098"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ricci, F., Bontcheva, K., Conlan, O., and Lawless, S. (2015). Smartphone Based Stress Prediction. User Modeling, Adaptation and Personalization, Springer International Publishing.","DOI":"10.1007\/978-3-319-20267-9"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Pielot, M., Dingler, T., Pedro, J.S., and Oliver, N. (2015, January 7\u201311). When Attention is Not Scarce\u2014Detecting Boredom from Mobile Phone Usage. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2804252"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sasaki, W., Nakazawa, J., and Okoshi, T. (2018, January 8\u201312). Comparing ESM Timings for Emotional Estimation Model with Fine Temporal Granularity. Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore.","DOI":"10.1145\/3267305.3267699"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ijhcs.2019.04.005","article-title":"Emotion detection from touch interactions during text entry on smartphones","volume":"130","author":"Ghosh","year":"2019","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Bauer, G., and Lukowicz, P. (2012, January 19\u201323). Can smartphones detect stress-related changes in the behaviour of individuals?. Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, Lugano, Switzerland.","DOI":"10.1109\/PerComW.2012.6197525"},{"key":"ref_57","unstructured":"LiKamWa, R., Liu, Y., Lane, N.D., and Zhong, L. (2011, January 1). Can your smartphone infer your mood. Proceedings of the PhoneSense Workshop, Seattle, WA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ma, Y., Xu, B., Bai, Y., Sun, G., and Zhu, R. (2012, January 9\u201312). Daily Mood Assessment Based on Mobile Phone Sensing. Proceedings of the 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks, London, UK.","DOI":"10.1109\/BSN.2012.3"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Moturu, S., Khayal, I., Aharony, N., Pan, W., and Pentland, A. (2011, January 9\u201311). Using social sensing to understand the links between sleep, mood and sociability. Proceedings of the IEEE International Conference on Social Computing, Boston, MA, USA.","DOI":"10.1109\/PASSAT\/SocialCom.2011.200"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, W., Chen, X., and Lu, S. (2018). MoodExplorer: Towards Compound Emotion Detection via Smartphone Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1.","DOI":"10.1145\/3161414"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"LiKamWa, R., Liu, Y., Lane, N.D., and Zhong, L. (2013, January 25\u201328). MoodScope: Building a Mood Sensor from Smartphone Usage Patterns. Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services, Taipei, Taiwan.","DOI":"10.1145\/2462456.2483967"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., and Campbell, A.T. (2014, January 13\u201317). StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students Using Smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.","DOI":"10.1145\/2632048.2632054"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/JBHI.2015.2446195","article-title":"Automatic Stress Detection in Working Environments From Smartphones\u2019 Accelerometer Data: A First Step","volume":"20","author":"Osmani","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.jbi.2016.08.023","article-title":"Stress Modelling and Prediction in Presence of Scarce Data","volume":"63","author":"Maxhuni","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Roshanaei, M., Han, R., and Mishra, S. (August, January 31). EmotionSensing: Predicting Mobile User Emotion. Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia.","DOI":"10.1145\/3110025.3110127"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jrp.2016.06.004","article-title":"Putting mood in context: Using smartphones to examine how people feel in different locations","volume":"69","author":"Sandstrom","year":"2017","journal-title":"J. Res. Personal."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Servia-Rodr\u00edguez, S., Rachuri, K., Mascolo, C., Rentfrow, P., Lathia, N., and Sandstrom, G. (2017, January 3\u20137). Mobile Sensing at the Service of Mental Well-being: A Large-scale Longitudinal Study. Proceedings of the 26 International World Wide Web Conference, Perth, Australia.","DOI":"10.1145\/3038912.3052618"},{"key":"ref_68","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, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971654"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TAFFC.2016.2592504","article-title":"Individuals\u2019 Stress Assessment Using Human-Smartphone Interaction Analysis","volume":"9","author":"Ciman","year":"2018","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/T-AFFC.2011.23","article-title":"Quantitative Study of Individual Emotional States in Social Networks","volume":"3","author":"Tang","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"17292","DOI":"10.3390\/s131217292","article-title":"Mobile sensing systems","volume":"13","author":"Macias","year":"2013","journal-title":"Sensors"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Sun, B., Ma, Q., Zhang, S., Liu, K., and Liu, Y. (May, January 26). iSelf: Towards cold-start emotion labeling using transfer learning with smartphones. Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM), Hong Kong, China.","DOI":"10.1109\/INFOCOM.2015.7218495"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Olsen, A.F., and Torresen, J. (2016, January 19\u201321). Smartphone accelerometer data used for detecting human emotions. Proceedings of the 2016 3rd International Conference on Systems and Informatics (ICSAI), Shanghai, China.","DOI":"10.1109\/ICSAI.2016.7810990"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compeleceng.2017.05.004","article-title":"Emotion recognition using mobile phones","volume":"60","author":"Zualkernan","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ghandeharioun, A., McDuff, D., Czerwinski, M., and Rowan, K. (2019, January 3\u20136). EMMA: An Emotion-Aware Wellbeing Chatbot. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK.","DOI":"10.1109\/ACII.2019.8925455"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., and Pentland, A.S. (2014, January 24\u201328). Pervasive stress recognition for sustainable living. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communication Workshops, Budapest, Hungary.","DOI":"10.1109\/PerComW.2014.6815230"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Lane, N.D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., and Campbell, A. (2011, January 23\u201326). Bewell: A smartphone application to monitor, model and promote wellbeing. Proceedings of the 5th International Conference on Pervasive Computing Technologies for Healthcare, Dublin, Ireland.","DOI":"10.4108\/icst.pervasivehealth.2011.246161"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Sahu, S., Ganguly, N., Mitra, B., and De, P. (2019, January 7\u201311). EmoKey: An Emotion-aware Smartphone Keyboard for Mental Health Monitoring. Proceedings of the 2019 11th International Conference on Communication Systems Networks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS.2019.8711078"},{"key":"ref_79","unstructured":"Ghosh, S., Ganguly, N., Mitra, B., and De, P. (November, January 29). Effectiveness of Deep Neural Network Model in Typing-Based Emotion Detection on Smartphones. Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"49695","DOI":"10.1109\/ACCESS.2020.2979898","article-title":"Clustering-Based Emotion Recognition Micro-Service Cloud Framework for Mobile Computing","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/j.1751-9004.2009.00170.x","article-title":"Experience Sampling Methods: A Modern Idiographic Approach to Personality Research","volume":"3","author":"Conner","year":"2009","journal-title":"Soc. Personal. Psychol. Compass"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Shi, D., Chen, X., Wei, J., and Yang, R. (2015, January 19\u201321). User Emotion Recognition Based on Multi-class Sensors of Smartphone. Proceedings of the 2015 IEEE International Conference on Smart City\/SocialCom\/SustainCom (SmartCity), Chengdu, China.","DOI":"10.1109\/SmartCity.2015.116"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Lee, H., Cho, A., Jo, Y., and Whang, M. (2018). The Relationships Between Behavioral Patterns and Emotions in Daily Life. Advances in Computer Science and Ubiquitous Computing, Springer.","DOI":"10.1007\/978-981-10-7605-3_212"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Saadatian, E., Salafi, T., Samani, H., Lim, Y.D., and Nakatsu, R. (2014, January 28\u201331). An Affective Telepresence System Using Smartphone High Level Sensing and Intelligent Behavior Generation. Proceedings of the Second International Conference on Human-Agent Interaction, Tsukuba, Japan.","DOI":"10.1145\/2658861.2658878"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Chauhan, V., Ganguly, N., Mitra, B., and De, P. (2015, January 7\u201311). Impact of Experience Sampling Methods on Tap Pattern Based Emotion Recognition. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, Osaka, Japan.","DOI":"10.1145\/2800835.2804396"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Gao, Y., Bianchi-Berthouze, N., and Meng, H. (2012). What Does Touch Tell Us about Emotions in Touchscreen-Based Gameplay?. ACM Trans. Comput. Hum. Interact., 19.","DOI":"10.1145\/2395131.2395138"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2017, January 23\u201326). Evaluating effectiveness of smartphone typing as an indicator of user emotion. Proceedings of the 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, USA.","DOI":"10.1109\/ACII.2017.8273592"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2017, January 4\u20137). TapSense: Combining Self-Report Patterns and Typing Characteristics for Smartphone Based Emotion Detection. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services, Vienna, Austria. MobileHCI \u201917.","DOI":"10.1145\/3098279.3098564"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1007\/s00500-016-2115-0","article-title":"Assessing users\u2019 emotion at interaction time: A multimodal approach with multiple sensors","volume":"21","author":"Giancristofaro","year":"2017","journal-title":"Soft Comput."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2017, January 8\u201311). Towards designing an intelligent experience sampling method for emotion detection. Proceedings of the 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2017.7983143"},{"key":"ref_91","unstructured":"Ghosh, S., Ganguly, N., Mitra, B., and De, P. (2019). Designing An Experience Sampling Method for Smartphone based Emotion Detection. IEEE Trans. Affect. Comput."},{"key":"ref_92","first-page":"1","article-title":"A technique for the measurement of attitudes","volume":"22","author":"Likert","year":"1932","journal-title":"Arch. Psychol."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"100086","DOI":"10.1016\/j.smhl.2019.100086","article-title":"An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients","volume":"15","author":"Cai","year":"2020","journal-title":"Smart Health"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring emotion: The self-assessment manikin and the semantic differential","volume":"25","author":"Bradley","year":"1994","journal-title":"J. Behav. Ther. Exp. Psychiatry"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1037\/0022-3514.54.6.1063","article-title":"Development and validation of brief measures of positive and negative affect: The PANAS scales","volume":"54","author":"Watson","year":"1988","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_96","unstructured":"Pollak, J.P., Adams, P., and Gay, G. (2011, January 7\u201312). PAM: A photographic affect meter for frequent, in situ measurement of affect. Proceedings of the 29th ACM SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A Global Measure of Perceived Stress","volume":"24","author":"Cohen","year":"1983","journal-title":"J. Health Soc. Behav."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0005-7967(94)00075-U","article-title":"The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories","volume":"33","author":"Lovibond","year":"1995","journal-title":"Behav. Res. Ther."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Balducci, F., Impedovo, D., Macchiarulo, N., and Pirlo, G. (2020). Affective states recognition through touch dynamics. Multimed. Tools Appl.","DOI":"10.1007\/s11042-020-09146-4"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Mitra, B., and De, P. (2020, January 25\u201330). Towards Improving Emotion Self-Report Collection Using Self-Reflection. Proceedings of the Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA.","DOI":"10.1145\/3334480.3383019"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TIFS.2012.2225048","article-title":"Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication","volume":"8","author":"Frank","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Serwadda, A., Phoha, V.V., and Wang, Z. (October, January 29). Which verifiers work?: A benchmark evaluation of touch-based authentication algorithms. Proceedings of the 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA.","DOI":"10.1109\/BTAS.2013.6712758"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Teh, P.S., Zhang, N., Tan, S.Y., Shi, Q., Khoh, W.H., and Nawaz, R. (2019). Strengthen user authentication on mobile devices by using user\u2019s touch dynamics pattern. Sens. J. Ambient. Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-019-01654-y"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Epp, C., Lippold, M., and Mandryk, R.L. (2011, January 7\u201312). Identifying Emotional States Using Keystroke Dynamics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada.","DOI":"10.1145\/1978942.1979046"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A. (2013, January 6\u20138). A review of emotion recognition methods based on keystroke dynamics and mouse movements. Proceedings of the 2013 6th International Conference on Human System Interactions (HSI), Gdansk, Poland.","DOI":"10.1109\/HSI.2013.6577879"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A. (2015, January 25\u201327). Recognizing emotions on the basis of keystroke dynamics. Proceedings of the 8th International Conference on Human System Interaction, Warsaw, Poland.","DOI":"10.1109\/HSI.2015.7170682"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ciman, M., Wac, K., and Gaggi, O. (2015, January 20\u201323). iSensestress: Assessing stress through human-smartphone interaction analysis. Proceedings of the 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Istanbul, Turkey.","DOI":"10.4108\/icst.pervasivehealth.2015.259280"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Sneha, H.R., Rafi, M., Manoj Kumar, M.V., Thomas, L., and Annappa, B. (2017, January 22\u201324). Smartphone based emotion recognition and classification. Proceedings of the 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India.","DOI":"10.1109\/ICECCT.2017.8117872"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Goenka, S., Ganguly, N., Mitra, B., and De, P. (2019, January 3\u20136). Representation Learning for Emotion Recognition from Smartphone Keyboard Interactions. Proceedings of the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, UK.","DOI":"10.1109\/ACII.2019.8925518"},{"key":"ref_110","unstructured":"Olsen, A.F. (2016). Detecting Human Emotions Using Smartphone Accelerometer Data. [Master\u2019s Thesis, Department of Informatics, University of Oslo]."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., and Campbell, A.T. (2010, January 3\u20135). The Jigsaw Continuous Sensing Engine for Mobile Phone Applications. Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, Zurich, Switzerland.","DOI":"10.1145\/1869983.1869992"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Li, Q., Song, S., Li, R., Xu, Y., Xi, W., and Gao, H. (2019). Classifier Fusion Method Based Emotion Recognition for Mobile Phone Users. Broadband Communications, Networks, and Systems, Springer International Publishing.","DOI":"10.1007\/978-3-030-36442-7_14"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s11036-013-0484-5","article-title":"BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing","volume":"19","author":"Lane","year":"2014","journal-title":"Mob. Netw. Appl."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Oh, K., Park, H., and Cho, S. (2010, January 26\u201329). A Mobile Context Sharing System Using Activity and Emotion Recognition with Bayesian Networks. Proceedings of the 2010 7th International Conference on Ubiquitous Intelligence Computing and 7th International Conference on Autonomic Trusted Computing, Xi\u2019an, China.","DOI":"10.1109\/UIC-ATC.2010.26"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., and Pianesi, F. (2013, January 8\u201314). Happiness Recognition from Mobile Phone Data. Proceedings of the 2013 International Conference on Social Computing, Alexandria, VA, USA.","DOI":"10.1109\/SocialCom.2013.118"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., and Pentland, A.S. (2014, January 3\u20137). Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654933"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Ko\u0142akowska, A. (2018). Usefulness of Keystroke Dynamics Features in User Authentication and Emotion Recognition. Human-Computer Systems Interaction: Backgrounds and Applications 4, Springer.","DOI":"10.1007\/978-3-319-62120-3_4"},{"key":"ref_118","unstructured":"Kim, M., Kim, H., Lee, S., and Choi, Y.S. (2013, January 8\u201311). A touch based affective user interface for smartphone. Proceedings of the 2013 IEEE International Conference on Consumer Electronics (ICCE), Berlin, Germany."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Lietz, R., Harraghy, M., Calderon, D., Brady, J., Becker, E., and Makedon, F. (2019, January 5\u20137). Survey of Mood Detection through Various Input Modes. Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Rhodes, Greece. PETRA\u201919.","DOI":"10.1145\/3316782.3321543"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"13414","DOI":"10.1038\/s41598-019-50002-9","article-title":"Touchscreen typing pattern analysis for remote detection of the depressive tendency","volume":"9","author":"Mastoras","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Cao, B., Zheng, L., Zhang, C., Yu, P.S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., and Leow, A.D. (2017, January 13\u201317). DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098086"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1017\/S0033291717001659","article-title":"A systematic review of the psychometric properties, usability and clinical impacts of mobile mood-monitoring applications in young people","volume":"48","author":"Dubad","year":"2018","journal-title":"Psychol. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6367\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:30:46Z","timestamp":1760178646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,8]]},"references-count":123,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216367"],"URL":"https:\/\/doi.org\/10.3390\/s20216367","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,8]]}}}