{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:51:13Z","timestamp":1771667473059,"version":"3.50.1"},"reference-count":105,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2022-NR068758, RS-2025-00559234"],"award-info":[{"award-number":["RS-2022-NR068758, RS-2025-00559234"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2025-02305705"],"award-info":[{"award-number":["RS-2025-02305705"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>With advancements in mobile sensing technologies, there is a growing need for scalable and interpretable stress monitoring solutions that remain robust over time. Existing smartphone passive sensing approaches rely on statistical app usage features or multi-modal sensor data, making them susceptible to distribution shift and feature evolution (e.g., schema drift), and adding model complexity. To address these challenges, we propose the Smartphone Human Interaction-based Routine Behavior Task Mining, Modeling, and Feature Extraction (SHIRBT-MMF) framework, which models stress-related behaviors by mining finegrained interaction routine tasks rather than aggregated app usage patterns. SHIRBT-MMF leverages multi-level sequential pattern mining and large language model-based automated task modeling to extract interpretable and stable features from within-app UI state transitions. Unlike traditional methods that require hundreds of apps, SHIRBT focuses on a small, consistent set of routine-based tasks, mitigating covariate shift and feature evolution while improving model robustness. We validated SHIRBT-MMF through one- and four-month in-the-wild datasets with 26 participants, demonstrating that the SHIRBT-based personalized model achieves an average accuracy of 75%, outperforming baseline models by 5% while using only 3-6% of app types. Additionally, SHIRBT features remain stable over time, reducing covariate shift and ensuring reliable performance. With its expandability to other mental health, interpretability, and privacy-conscious design, the SHIRBT-MMF framework lays the foundation for personalized digital mental health monitoring.<\/jats:p>","DOI":"10.1145\/3770644","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:42:32Z","timestamp":1764704552000},"page":"1-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Smartphone Human Interaction Routine Behavior Task Mining and Modeling for Daily Stress Monitoring"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0268-3434","authenticated-orcid":false,"given":"Hansoo","family":"Lee","sequence":"first","affiliation":[{"name":"Intelligence and Interaction Research Center, KIST, Seoul, Republic of Korea and School of Computing, KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1142-6232","authenticated-orcid":false,"given":"Taehyeon","family":"Park","sequence":"additional","affiliation":[{"name":"School of Computing, KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8405-4919","authenticated-orcid":false,"given":"Youngji","family":"Koh","sequence":"additional","affiliation":[{"name":"School of Computing, KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8711-7732","authenticated-orcid":false,"given":"Jae-Gil","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computing, KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1888-1569","authenticated-orcid":false,"given":"Uichin","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computing, KAIST, Daejeon, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3158645"},{"key":"e_1_2_1_2_1","volume-title":"2021 IEEE\/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 1\u20138.","author":"Ackerman Samuel","year":"2021","unstructured":"Samuel Ackerman, Parijat Dube, Eitan Farchi, Orna Raz, and Marcel Zalmanovici. 2021. Machine learning model drift detection via weak data slices. In 2021 IEEE\/ACM Third International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 1\u20138."},{"key":"e_1_2_1_3_1","volume-title":"Detection of data drift and outliers affecting machine learning model performance over time. arXiv preprint arXiv:2012.09258","author":"Ackerman Samuel","year":"2020","unstructured":"Samuel Ackerman, Eitan Farchi, Orna Raz, Marcel Zalmanovici, and Parijat Dube. 2020. Detection of data drift and outliers affecting machine learning model performance over time. arXiv preprint arXiv:2012.09258 (2020)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.1995.380415"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_2_1_6_1","first-page":"e5505","article-title":"Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study","volume":"18","author":"Asselbergs Joost","year":"2016","unstructured":"Joost Asselbergs, Jeroen Ruwaard, Michal Ejdys, Niels Schrader, Marit Sijbrandij, Heleen Riper, et al. 2016. Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study. Journal of medical Internet research 18, 3 (2016), e5505.","journal-title":"Journal of medical Internet research"},{"key":"e_1_2_1_7_1","volume-title":"Stress in America, United States","author":"Psychological American","year":"2019","unstructured":"American Psychological Association et al. 2019. Stress in America, United States, 2007\u20132018. (2019)."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775109"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1080\/13676261.2014.992317","article-title":"Your whole life depends on it\u2019: Academic stress and high-stakes testing in Ireland","volume":"18","author":"Banks Joanne","year":"2015","unstructured":"Joanne Banks and Emer Smyth. 2015. \u2018Your whole life depends on it\u2019: Academic stress and high-stakes testing in Ireland. Journal of youth studies 18, 5 (2015), 598\u2013616.","journal-title":"Journal of youth studies"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/PerComW.2012.6197525"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2986459.2986743"},{"key":"e_1_2_1_12_1","volume-title":"Pablo Moreno-Mu\u00f1oz, Rodrigo Carmona Camacho, Enrique Baca-Garc\u00eda, Antonio Art\u00e9s-Rodr\u00edguez, et al.","author":"Berrouiguet Sofian","year":"2018","unstructured":"Sofian Berrouiguet, David Ram\u00edrez, Mar\u00eda Luisa Barrig\u00f3n, Pablo Moreno-Mu\u00f1oz, Rodrigo Carmona Camacho, Enrique Baca-Garc\u00eda, Antonio Art\u00e9s-Rodr\u00edguez, et al. 2018. Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the evidence-based behavior (eB2) study. JMIR mHealth and uHealth 6, 12 (2018), e9472."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654933"},{"key":"e_1_2_1_14_1","volume-title":"Random forests. Machine learning 45","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5\u201332."},{"key":"e_1_2_1_15_1","volume-title":"Investing in treatment for depression and anxiety leads to fourfold return. Retrieved","author":"Brunier Alison","year":"2025","unstructured":"Alison Brunier. 2016. Investing in treatment for depression and anxiety leads to fourfold return. Retrieved August 1, 2025 from https:\/\/www.who.int\/news\/item\/13-04-2016-investing-in-treatment-for-depression-and-anxiety-leads-to-fourfold-return"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330690"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422821"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3195728.3195741"},{"key":"e_1_2_1_20_1","unstructured":"Sheldon Cohen. 1988. Perceived stress in a probability sample of the United States. (1988)."},{"key":"e_1_2_1_21_1","unstructured":"Android Developer. 2025. AccessibilityEvent. Retrieved August 1 2025 from https:\/\/developer.android.com\/reference\/android\/view\/accessibility\/AccessibilityEvent"},{"key":"e_1_2_1_22_1","unstructured":"Android Developer. 2025. AccessibilityService. Retrieved August 1 2025 from https:\/\/developer.android.com\/reference\/android\/accessibilityservice\/AccessibilityService"},{"key":"e_1_2_1_23_1","unstructured":"Android Developer. 2025. NotificationListenerService. Retrieved August 1 2025 from https:\/\/developer.android.com\/reference\/android\/location\/LocationListener"},{"key":"e_1_2_1_24_1","unstructured":"Android Developer. 2025. NotificationManager. Retrieved August 1 2025 from https:\/\/developer.android.com\/reference\/android\/app\/NotificationManager"},{"key":"e_1_2_1_25_1","volume-title":"Understand the UI Layer. Retrieved","author":"Developer Android","year":"2025","unstructured":"Android Developer. 2025. Understand the UI Layer. Retrieved August 1, 2025 from https:\/\/developer.android.com\/topic\/architecture\/ui-layer"},{"key":"e_1_2_1_26_1","unstructured":"Android Developer. 2025. UsageStatsManager. Retrieved August 1 2025 from https:\/\/developer.android.com\/reference\/android\/app\/usage\/UsageStatsManager#constants_1"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681984"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 6th annual ACM international workshop on web information and data management. 128\u2013135","author":"El-Sayed Maged","year":"2004","unstructured":"Maged El-Sayed, Carolina Ruiz, and Elke A Rundensteiner. 2004. FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs. In Proceedings of the 6th annual ACM international workshop on web information and data management. 128\u2013135."},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations. 26\u201335","author":"Ezeife Christie I","year":"2005","unstructured":"Christie I Ezeife, Yi Lu, and Yi Liu. 2005. PLWAP sequential mining: open source code. In Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations. 26\u201335."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/s24248112"},{"key":"e_1_2_1_31_1","first-page":"54","article-title":"A survey of sequential pattern mining","volume":"1","author":"Fournier-Viger Philippe","year":"2017","unstructured":"Philippe Fournier-Viger, Jerry Chun-Wei Lin, Rage Uday Kiran, Yun Sing Koh, and Rincy Thomas. 2017. A survey of sequential pattern mining. Data Science and Pattern Recognition 1, 1 (2017), 54\u201377.","journal-title":"Data Science and Pattern Recognition"},{"key":"e_1_2_1_32_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","author":"Friedman Jerome H","year":"2001","unstructured":"Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189\u20131232."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00013"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMSNETS.2019.8711078"},{"key":"e_1_2_1_35_1","volume-title":"proceedings of the 17th international conference on data engineering. IEEE Piscataway, NJ, USA, 215\u2013224","author":"Han Jiawei","year":"2001","unstructured":"Jiawei Han, Jian Pei, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Meichun Hsu. 2001. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In proceedings of the 17th international conference on data engineering. IEEE Piscataway, NJ, USA, 215\u2013224."},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems","volume":"1","author":"Hao Wei","year":"2025","unstructured":"Wei Hao, Zixi Wang, Lauren Hong, Lingxiao Li, Nader Karayanni, AnMei Dasbach-Prisk, Chengzhi Mao, Junfeng Yang, and Asaf Cidon. 2025. Nazar: Monitoring and Adapting ML Models on Mobile Devices. In Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1. 746\u2013761."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1177\/1745691616650285"},{"key":"e_1_2_1_38_1","volume-title":"2011 IEEE 12th International Conference on Mobile Data Management","volume":"2","author":"Hassani Marwan","year":"2011","unstructured":"Marwan Hassani and Thomas Seidl. 2011. Towards a mobile health context prediction: Sequential pattern mining in multiple streams. In 2011 IEEE 12th International Conference on Mobile Data Management, Vol. 2. IEEE, 55\u201357."},{"key":"e_1_2_1_39_1","volume-title":"Learning with feature evolvable streams. Advances in Neural Information Processing Systems 30","author":"Hou Bo-Jian","year":"2017","unstructured":"Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou. 2017. Learning with feature evolvable streams. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3022227.3022265"},{"key":"e_1_2_1_41_1","doi-asserted-by":"crossref","first-page":"100398","DOI":"10.1016\/j.chbr.2024.100398","article-title":"Social media use among Australian university students: Understanding links with stress and mental health","volume":"14","author":"Hurley Emma C","year":"2024","unstructured":"Emma C Hurley, Ian R Williams, Adrian J Tomyn, and Lena Sanci. 2024. Social media use among Australian university students: Understanding links with stress and mental health. Computers in Human Behavior Reports 14 (2024), 100398.","journal-title":"Computers in Human Behavior Reports"},{"key":"e_1_2_1_42_1","volume-title":"2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 222\u2013228","author":"Jaques Natasha","year":"2015","unstructured":"Natasha Jaques, Sara Taylor, Asaph Azaria, Asma Ghandeharioun, Akane Sano, and Rosalind Picard. 2015. Predicting students' happiness from physiology, phone, mobility, and behavioral data. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 222\u2013228."},{"key":"e_1_2_1_43_1","volume-title":"Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201318","author":"Jung Gyuwon","year":"2024","unstructured":"Gyuwon Jung, Sangjun Park, and Uichin Lee. 2024. DeepStress: Supporting Stressful Context Sensemaking in Personal Informatics Systems Using a Quasi-experimental Approach. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201318."},{"key":"e_1_2_1_44_1","volume-title":"Narae Cha, Auk Kim, Ahsan Habib Khandoker, Leontios Hadjileontiadis, Heepyung Kim, Yong Jeong, and Uichin Lee.","author":"Kang Soowon","year":"2023","unstructured":"Soowon Kang, Woohyeok Choi, Cheul Young Park, Narae Cha, Auk Kim, Ahsan Habib Khandoker, Leontios Hadjileontiadis, Heepyung Kim, Yong Jeong, and Uichin Lee. 2023. K-emophone: A mobile and wearable dataset with in-situ emotion, stress, and attention labels. Scientific data 10, 1 (2023), 351."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3501944"},{"key":"e_1_2_1_46_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017), 3146\u20133154."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491102.3517701"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351249"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42488-024-00119-y"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC.2012.6181098"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201325","author":"Lee Hansoo","year":"2024","unstructured":"Hansoo Lee, Auk Kim, SangWon Bae, and Uichin Lee. 2024. S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled Environment. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201325."},{"key":"e_1_2_1_52_1","first-page":"1","article-title":"A systematic survey on android api usage for data-driven analytics with smartphones","volume":"55","author":"Lee Hansoo","year":"2022","unstructured":"Hansoo Lee, Joonyoung Park, and Uichin Lee. 2022. A systematic survey on android api usage for data-driven analytics with smartphones. Comput. Surveys 55, 5 (2022), 1\u201338.","journal-title":"Comput. Surveys"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2007.08.020"},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services. 359\u2013368","author":"Lettner Florian","year":"2014","unstructured":"Florian Lettner, Christian Grossauer, and Clemens Holzmann. 2014. Mobile interaction analysis: towards a novel concept for interaction sequence mining. In Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services. 359\u2013368."},{"key":"e_1_2_1_55_1","volume-title":"Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201318","author":"Lim Jieun","year":"2024","unstructured":"Jieun Lim, Youngji Koh, Auk Kim, and Uichin Lee. 2024. Exploring Context-Aware Mental Health Self-Tracking Using Multimodal Smart Speakers in Home Environments. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1\u201318."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370290"},{"key":"e_1_2_1_57_1","volume-title":"Efficacy of mental health smartphone apps on stress levels: a meta-analysis of randomised controlled trials. Health psychology review 18, 4","author":"Linardon Jake","year":"2024","unstructured":"Jake Linardon, Joseph Firth, John Torous, Mariel Messer, and Matthew Fuller-Tyszkiewicz. 2024. Efficacy of mental health smartphone apps on stress levels: a meta-analysis of randomised controlled trials. Health psychology review 18, 4 (2024), 839\u2013852."},{"key":"e_1_2_1_58_1","volume-title":"2014 IEEE International Conference on Granular Computing (GrC). IEEE, 185\u2013190","author":"Hsueh-Chan Lu Eric","year":"2014","unstructured":"Eric Hsueh-Chan Lu, Yi-Wei Lin, and Jing-Bin Ciou. 2014. Mining mobile application sequential patterns for usage prediction. In 2014 IEEE International Conference on Granular Computing (GrC). IEEE, 185\u2013190."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-018-0322-9"},{"key":"e_1_2_1_60_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12874-025-02474-9","article-title":"Assessing the representativeness of large medical data using population stability index","volume":"25","author":"Lu Sheng-Chieh","year":"2025","unstructured":"Sheng-Chieh Lu, Wenye Song, Andre Pfob, and Chris Gibbons. 2025. Assessing the representativeness of large medical data using population stability index. BMC Medical Research Methodology 25, 1 (2025), 44.","journal-title":"BMC Medical Research Methodology"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/1824795.1824798"},{"key":"e_1_2_1_62_1","volume-title":"Text entry systems: Mobility, accessibility, universality","author":"MacKenzie I Scott","unstructured":"I Scott MacKenzie and Kumiko Tanaka-Ishii. 2010. Text entry systems: Mobility, accessibility, universality. Elsevier."},{"key":"e_1_2_1_63_1","volume-title":"Stress Top Reasons Students Consider Leaving. Retrieved","author":"Marken Stephanie","year":"2025","unstructured":"Stephanie Marken. 2024. Mental Health, Stress Top Reasons Students Consider Leaving. Retrieved August 1, 2025 from https:\/\/news.gallup.com\/poll\/644645\/mental-health-stress-top-reasons-students-consider-leaving.aspx"},{"key":"e_1_2_1_64_1","doi-asserted-by":"crossref","first-page":"2313","DOI":"10.1109\/TMC.2020.2974834","article-title":"Unobtrusive stress assessment using smartphones","volume":"20","author":"Maxhuni Alban","year":"2020","unstructured":"Alban Maxhuni, Pablo Hernandez-Leal, Eduardo F Morales, L Enrique Sucar, Venet Osmani, and Oscar Mayora. 2020. Unobtrusive stress assessment using smartphones. IEEE Transactions on Mobile Computing 20, 6 (2020), 2313\u20132325.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3659591"},{"key":"e_1_2_1_66_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130948","article-title":"MyTraces: Investigating correlation and causation between users' emotional states and mobile phone interaction","volume":"1","author":"Mehrotra Abhinav","year":"2017","unstructured":"Abhinav Mehrotra, Fani Tsapeli, Robert Hendley, and Mirco Musolesi. 2017. MyTraces: Investigating correlation and causation between users' emotional states and mobile phone interaction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1\u201321.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Shikha Mehta et al. 2017. Concept drift in streaming data classification: algorithms platforms and issues. Procedia computer science 122 (2017) 804\u2013811.","DOI":"10.1016\/j.procs.2017.11.440"},{"key":"e_1_2_1_68_1","first-page":"161","article-title":"Digital phenotyping for mental health of college students: a clinical review","volume":"23","author":"Melcher Jennifer","year":"2020","unstructured":"Jennifer Melcher, Ryan Hays, and John Torous. 2020. Digital phenotyping for mental health of college students: a clinical review. BMJ Ment Health 23, 4 (2020), 161\u2013166.","journal-title":"BMJ Ment Health"},{"key":"e_1_2_1_69_1","volume-title":"How to handle schema drift in Azure Data Factory Data Flow. Retrieved","year":"2025","unstructured":"Microsoft. 2025. How to handle schema drift in Azure Data Factory Data Flow. Retrieved August 1, 2025 from https:\/\/learn.microsoft.com\/en-us\/azure\/data-factory\/concepts-data-flow-schema-drift"},{"key":"e_1_2_1_70_1","volume-title":"Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: A systematic literature review. Journal of Biomedical Informatics","author":"Moura Ivan","year":"2022","unstructured":"Ivan Moura, Ariel Teles, Davi Viana, Jean Marques, Luciano Coutinho, and Francisco Silva. 2022. Digital phenotyping of mental health using multimodal sensing of multiple situations of interest: A systematic literature review. Journal of Biomedical Informatics (2022), 104278."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12668-013-0089-2"},{"key":"e_1_2_1_72_1","volume-title":"Proceedings of the 2014 acm international joint conference on pervasive and ubiquitous computing: Adjunct publication. 1005\u20131014","author":"Mukherji Abhishek","year":"2014","unstructured":"Abhishek Mukherji, Vijay Srinivasan, and Evan Welbourne. 2014. Adding intelligence to your mobile device via on-device sequential pattern mining. In Proceedings of the 2014 acm international joint conference on pervasive and ubiquitous computing: Adjunct publication. 1005\u20131014."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv165"},{"key":"e_1_2_1_74_1","unstructured":"Skogby Steinholtz Olof. 2018. A comparative study of black-box optimization algorithms for tuning of hyper-parameters in deep neural networks."},{"key":"e_1_2_1_75_1","unstructured":"OpenAI. 2024. GPT-4o. Retrieved August 1 2025 from https:\/\/openai.com\/index\/hello-gpt-4o\/"},{"key":"e_1_2_1_76_1","volume-title":"text-embedding-3-large. Retrieved","author":"AI.","year":"2025","unstructured":"OpenAI. 2024. text-embedding-3-large. Retrieved August 1, 2025 from https:\/\/platform.openai.com\/docs\/models\/text-embedding-3-large"},{"key":"e_1_2_1_77_1","volume-title":"Pacific-Asia conference on knowledge discovery and data mining. Springer, 396\u2013407","author":"Pei Jian","year":"2000","unstructured":"Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, and Hua Zhu. 2000. Mining access patterns efficiently from web logs. In Pacific-Asia conference on knowledge discovery and data mining. Springer, 396\u2013407."},{"key":"e_1_2_1_78_1","volume-title":"emotional, and behavioral correlates of fear of missing out. Computers in human behavior 29, 4","author":"Przybylski Andrew K","year":"2013","unstructured":"Andrew K Przybylski, Kou Murayama, Cody R DeHaan, and Valerie Gladwell. 2013. Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior 29, 4 (2013), 1841\u20131848."},{"key":"e_1_2_1_79_1","volume-title":"Failing loudly: An empirical study of methods for detecting dataset shift. Advances in Neural Information Processing Systems 32","author":"Rabanser Stephan","year":"2019","unstructured":"Stephan Rabanser, Stephan G\u00fcnnemann, and Zachary Lipton. 2019. Failing loudly: An empirical study of methods for detecting dataset shift. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/2750858.2805840"},{"key":"e_1_2_1_81_1","volume-title":"Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084","author":"Reimers Nils","year":"2019","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)."},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300655"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/BSN.2015.7299420"},{"key":"e_1_2_1_85_1","volume-title":"Stress recognition using wearable sensors and mobile phones. In 2013 Humaine association conference on affective computing and intelligent interaction","author":"Sano Akane","unstructured":"Akane Sano and Rosalind W Picard. 2013. Stress recognition using wearable sensors and mobile phones. In 2013 Humaine association conference on affective computing and intelligent interaction. IEEE, 671\u2013676."},{"key":"e_1_2_1_86_1","volume-title":"Managing schema evolution in NoSQL data stores. arXiv preprint arXiv:1308.0514","author":"Scherzinger Stefanie","year":"2013","unstructured":"Stefanie Scherzinger, Meike Klettke, and Uta St\u00f6rl. 2013. Managing schema evolution in NoSQL data stores. arXiv preprint arXiv:1308.0514 (2013)."},{"key":"e_1_2_1_87_1","unstructured":"M Scott Lee Su-In et al. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017) 4765\u20134774."},{"key":"e_1_2_1_88_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.future.2024.07.010","article-title":"Efficient and scalable covariate drift detection in machine learning systems with serverless computing","volume":"161","author":"Sisniega Jaime Cespedes","year":"2024","unstructured":"Jaime Cespedes Sisniega, Vicente Rodr\u00edguez, German Molto, and \u00c1lvaro L\u00f3pez Garc\u00eda. 2024. Efficient and scalable covariate drift detection in machine learning systems with serverless computing. Future Generation Computer Systems 161 (2024), 174\u2013188.","journal-title":"Future Generation Computer Systems"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0014140"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632052"},{"key":"e_1_2_1_91_1","volume-title":"Most frequently used smartphone apps in South Korea as of","year":"2020","unstructured":"Statista. 2020. Most frequently used smartphone apps in South Korea as of September 2020, by category. Retrieved August 1, 2025 from https:\/\/www.statista.com\/statistics\/897227\/south-korea-frequently-used-smartphone-apps-by-category\/"},{"key":"e_1_2_1_92_1","volume-title":"Most used smartphone functions in South Korea from 2014 to","year":"2017","unstructured":"Statista. 2020. Most used smartphone functions in South Korea from 2014 to 2017. Retrieved August 1, 2025 from https:\/\/www.statista.com\/statistics\/953819\/south-korea-mainly-used-smartphone-functions\/"},{"key":"e_1_2_1_93_1","volume-title":"The Chromium Projects. Retrieved","author":"Projects The Chromium","year":"2025","unstructured":"The Chromium Projects. 2008. The Chromium Projects. Retrieved August 1, 2025 from https:\/\/www.chromium.org\/chromium-projects\/"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1145\/3398069"},{"key":"e_1_2_1_95_1","doi-asserted-by":"crossref","unstructured":"John Torous Mathew V Kiang Jeanette Lorme Jukka-Pekka Onnela et al. 2016. New tools for new research in psychiatry: a scalable and customizable platform to empower data driven smartphone research. JMIR mental health 3 2 (2016) e5165.","DOI":"10.2196\/mental.5165"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755828"},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123988"},{"key":"e_1_2_1_98_1","volume-title":"Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)","author":"Venkatesh Dilip","year":"2024","unstructured":"Dilip Venkatesh and Sundaresan Raman. 2024. Bits pilani at semeval-2024 task 1: Using text-embedding-3-large and labse embeddings for semantic textual relatedness. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024). 865\u2013868."},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-017-1108-z"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351274"},{"key":"e_1_2_1_101_1","first-page":"1","article-title":"Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students","volume":"5","author":"Xu Xuhai","year":"2021","unstructured":"Xuhai Xu, Prerna Chikersal, Janine M Dutcher, Yasaman S Sefidgar, Woosuk Seo, Michael J Tumminia, Daniella K Villalba, Sheldon Cohen, Kasey G Creswell, J David Creswell, et al. 2021. Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (2021), 1\u201327.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_102_1","volume-title":"SPADE: An efficient algorithm for mining frequent sequences. Machine learning 42","author":"Zaki Mohammed J","year":"2001","unstructured":"Mohammed J Zaki. 2001. SPADE: An efficient algorithm for mining frequent sequences. Machine learning 42 (2001), 31\u201360."},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3678578"},{"key":"e_1_2_1_104_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3161414","article-title":"Moodexplorer: Towards compound emotion detection via smartphone sensing","volume":"1","author":"Zhang Xiao","year":"2018","unstructured":"Xiao Zhang, Wenzhong Li, Xu Chen, and Sanglu Lu. 2018. Moodexplorer: Towards compound emotion detection via smartphone sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 1\u201330.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_105_1","volume-title":"International Conference on Machine Learning. PMLR, 11317\u201311327","author":"Zhang Zhen-Yu","year":"2020","unstructured":"Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, and Zhi-Hua Zhou. 2020. Learning with feature and distribution evolvable streams. In International Conference on Machine Learning. PMLR, 11317\u201311327."}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770644","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:50:19Z","timestamp":1764705019000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"references-count":105,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12,2]]}},"alternative-id":["10.1145\/3770644"],"URL":"https:\/\/doi.org\/10.1145\/3770644","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]},"assertion":[{"value":"2025-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}