Karim Assi
Lakmal Meegahapola
ABSTRACT
Smartphones enable understanding human behavior with activity recognition to support people’s daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people’s daily lives, and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and ∼ 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.
Footnotes
1 While we acknowledge that cultures can be multidimensional and exist in tension with each other and in plurality within the same country [131], some prior studies in mobile sensing, psychology, and sociology have used “culture” as a proxy to refer to the country of data collection [47, 52, 89, 118]. However, in this study, for consistency, we use “country” (a more specific geographic region) as the unit of analysis that could affect phone usage behavior and sensing data. We also used the term “geographic” rarely, when appropriate and when referring to regions (i.e., Europe).
Footnote2 The dataset is planned to be released for research purposes after the end of the project, by complying with all regulations governing the data collection protocol within and outside the European Union. Hence, future plans for dataset access will be made available on the project website: https://www.internetofus.eu/
Footnote3 We conducted experiments with different time windows between 5 minutes and 25 minutes. We did not go beyond 25 minutes because it would lead to overlapping sensor data segments, hence leaking data between data points. 20-minute window performed the best out of the examined time windows. For brevity, we only present results with the 20-minute window. Shorter windows might not have performed reasonably because they do not capture enough contextual information to make the inference. Prior work too has shown that large time windows might be suitable to detect binary activities [6, 9, 71]
Footnote4 We also tried mean imputation, user-based mean imputation, most frequent value imputation, last observation carried forward (LOCF) imputation, in addition to kNN. However, we obtained the best results for inferences with kNN. In addition, using kNN is common in studies that used passive sensing [94, 129, 132, 136]. Hence, we only reported results obtained with kNN.
Footnote5 We initially tried out other model types such as Gradient Boosting and XGBoost in addition to the reported models. Results for these models were not reported considering their performance and page limits. All these model types are commonly used in small mobile sensing datasets that are in tabular format [9, 74, 77]
Footnote6 https://huyenchip.com/2022/02/07/data-distribution-shifts-and-monitoring.html#data-shifts
Footnote
Supplemental Material
- Daniel A Adler, Fei Wang, David C Mohr, and Tanzeem Choudhury. 2022. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. Plos one 17, 4 (2022), e0266516.Google Scholar
Cross Ref
- Dhoha A Alsaleh, Michael T Elliott, Frank Q Fu, and Ramendra Thakur. 2019. Cross-cultural differences in the adoption of social media. Journal of Research in Interactive Marketing (2019).Google Scholar
- Tim Althoff, Rok Sosič, Jennifer L Hicks, Abby C King, Scott L Delp, and Jure Leskovec. 2017. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 7663 (2017), 336–339.Google Scholar
- Carlos Avilés-Cruz, Andrés Ferreyra-Ramírez, Arturo Zúñiga-López, and Juan Villegas-Cortéz. 2019. Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition. Sensors 19, 7 (March 2019), 1556. https://doi.org/10.3390/s19071556Google ScholarCross Ref
- Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C Puyana, Ryan Kurtz, Tammy Chung, and Anind K Dey. 2017. Detecting drinking episodes in young adults using smartphone-based sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (2017), 1–36.Google ScholarDigital Library
- Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (June 2017), 1–36. https://doi.org/10.1145/3090051Google ScholarDigital Library
- Ling Bao and Stephen S. Intille. 2004. Activity Recognition from User-Annotated Acceleration Data. In Pervasive Computing, Alois Ferscha and Friedemann Mattern (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1–17.Google Scholar
- Naomi S. Baron and Ylva Hård af Segerstad. 2010. Cross-cultural patterns in mobile-phone use: public space and reachability in Sweden, the USA and Japan. New Media & Society 12, 1 (2010), 13–34. https://doi.org/10.1177/1461444809355111 arXiv:https://doi.org/10.1177/1461444809355111Google ScholarCross Ref
- Joan-Isaac Biel, Nathalie Martin, David Labbe, and Daniel Gatica-Perez. 2018. Bites’n’Bits: Inferring Eating Behavior from Contextual Mobile Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (Jan. 2018), 1–33. https://doi.org/10.1145/3161161Google ScholarDigital Library
- Ulf Blanke and Bernt Schiele. 2009. Daily Routine Recognition through Activity Spotting. In Location and Context Awareness, Tanzeem Choudhury, Aaron Quigley, Thomas Strang, and Koji Suginuma (Eds.). Vol. 5561. Springer Berlin Heidelberg, Berlin, Heidelberg, 192–206. https://doi.org/10.1007/978-3-642-01721-6_12 Series Title: Lecture Notes in Computer Science.Google ScholarDigital Library
- Henrik Blunck, Niels Olof Bouvin, Tobias Franke, Kaj Grønbæk, Mikkel B Kjaergaard, Paul Lukowicz, and Markus Wüstenberg. 2013. On heterogeneity in mobile sensing applications aiming at representative data collection. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. 1087–1098.Google ScholarDigital Library
- Emma Bouton-Bessac, Lakmal Meegahapola, and Daniel Gatica-Perez. 2022. Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers. Proceedings of the 16th EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2022).Google Scholar
- Leo Breiman (Ed.). 1998. Classification and regression trees(1. crc press repr ed.). Chapman & Hall/CRC, Boca Raton, Fla.Google Scholar
- Leo Breiman. 2001. Random Forests. Machine Learning 45, 1 (2001), 5–32. https://doi.org/10.1023/A:1010933404324Google ScholarDigital Library
- Naomi Canton. 2012. Cell phone culture: How cultural differences affect mobile use. https://edition.cnn.com/2012/09/27/tech/mobile-culture-usage/index.htmlGoogle Scholar
- Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 1293–1304.Google ScholarDigital Library
- Pew Research Center. 2021. Mobile Fact Sheet. https://www.pewresearch.org/internet/fact-sheet/mobile/Google Scholar
- Pew Research Centre. 2017. Adapt your app by understanding what users are doing. Retrieved December 12, 2022 from https://www.pewresearch.org/fact-tank/2017/09/13/about-6-in-10-young-adults-in-u-s-primarily-use-online-streaming-to-watch-tv/Google Scholar
- Youngjae Chang, Akhil Mathur, Anton Isopoussu, Junehwa Song, and Fahim Kawsar. 2020. A systematic study of unsupervised domain adaptation for robust human-activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–30.Google ScholarDigital Library
- Yiqiang Chen, Xin Qin, Jindong Wang, Chaohui Yu, and Wen Gao. 2020. Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems 35, 4 (2020), 83–93.Google ScholarCross Ref
- Matty Chiva. 1997. Cultural aspects of meals and meal frequency. British Journal of Nutrition 77, S1 (1997), S21–S28.Google ScholarCross Ref
- François Chollet 2015. Keras. https://keras.io.Google Scholar
- Tanzeem Choudhury, Gaetano Borriello, Sunny Consolvo, Dirk Haehnel, Beverly Harrison, Bruce Hemingway, Jeffrey Hightower, Predrag "Pedja" Klasnja, Karl Koscher, Anthony LaMarca, James A. Landay, Louis LeGrand, Jonathan Lester, Ali Rahimi, Adam Rea, and Danny Wyatt. 2008. The Mobile Sensing Platform: An Embedded Activity Recognition System. IEEE Pervasive Computing 7, 2 (April 2008), 32–41. https://doi.org/10.1109/MPRV.2008.39Google ScholarDigital Library
- Daniel G Cobian, Nicole S Daehn, Paul A Anderson, and Bryan C Heiderscheit. 2013. Active cervical and lumbar range of motion during performance of activities of daily living in healthy young adults. Spine 38, 20 (2013), 1754–1763.Google ScholarCross Ref
- Lynn Coorevits and Tanguy Coenen. 2016. The rise and fall of wearable fitness trackers. Academy of Management Proceedings 2016, 1 (Jan. 2016), 17305. https://doi.org/10.5465/ambpp.2016.17305abstractGoogle ScholarCross Ref
- Victor P Cornet and Richard J Holden. 2018. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of biomedical informatics 77 (2018), 120–132.Google ScholarCross Ref
- AI Cultures. 2022. Neurips 2022 workshop on AI Cultures. Retrieved February 12, 2023 from https://ai-cultures.github.io/Google Scholar
- Anthony Cuthbertson. 2019. Self-driving cars are be more likely to drive into black people, study claims. https://www.independent.co.uk/life-style/gadgets-and-tech/news/self-driving-car-crash-racial-bias-black-people-study-a8810031.htmlGoogle Scholar
- Vedant Das Swain, Victor Chen, Shrija Mishra, Stephen M Mattingly, Gregory D Abowd, and Munmun De Choudhury. 2022. Semantic Gap in Predicting Mental Wellbeing through Passive Sensing. In CHI Conference on Human Factors in Computing Systems. 1–16.Google ScholarDigital Library
- Yves-Alexandre de Montjoye, Laura Radaelli, Vivek Kumar Singh, and Alex “Sandy” Pentland. 2015. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 347, 6221 (2015), 536–539. https://doi.org/10.1126/science.1256297 arXiv:https://www.science.org/doi/pdf/10.1126/science.1256297Google ScholarCross Ref
- Michael B Del Rosario, Kejia Wang, Jingjing Wang, Ying Liu, Matthew Brodie, Kim Delbaere, Nigel H Lovell, Stephen R Lord, and Stephen J Redmond. 2014. A comparison of activity classification in younger and older cohorts using a smartphone. Physiological Measurement 35, 11 (Nov. 2014), 2269–2286. https://doi.org/10.1088/0967-3334/35/11/2269Google ScholarCross Ref
- Chengbin Deng, Weiying Lin, Xinyue Ye, Zhenlong Li, Ziang Zhang, and Ganggang Xu. 2018. Social media data as a proxy for hourly fine-scale electric power consumption estimation. Environment and Planning A: Economy and Space 50, 8 (2018), 1553–1557.Google ScholarCross Ref
- Stefan Dernbach, Barnan Das, Narayanan C. Krishnan, Brian L. Thomas, and Diane J. Cook. 2012. Simple and Complex Activity Recognition through Smart Phones. In 2012 Eighth International Conference on Intelligent Environments. IEEE, Guanajuato, Mexico, 214–221. https://doi.org/10.1109/IE.2012.39Google ScholarDigital Library
- M Brent Donnellan, Frederick L Oswald, Brendan M Baird, and Richard E Lucas. 2006. The mini-IPIP scales: tiny-yet-effective measures of the Big Five factors of personality.Psychological assessment 18, 2 (2006), 192.Google Scholar
- Nathan Eagle and Alex (Sandy) Pentland. 2006. Reality mining: sensing complex social systems. Personal and Ubiquitous Computing 10, 4 (May 2006), 255–268. https://doi.org/10.1007/s00779-005-0046-3Google ScholarDigital Library
- Hady Elsahar and Matthias Gallé. 2019. To annotate or not? predicting performance drop under domain shift. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2163–2173.Google ScholarCross Ref
- Charlie Giattino Esteban Ortiz-Ospina and Max Roser. 2020. Time Use. Our World in Data (2020).Google Scholar
- Jonny Farringdon, Andrewj Moore, Nancy Tilbury, James Church, and Pieter D. Biemond. 1999. Wearable Sensor Badge & Sensor Jacket for Context Awareness.Google Scholar
- Kimberly Fisher and John Robinson. 2010. Daily routines in 22 countries diary evidence of average daily time spent in thirty activities. Technical Report. University of Oxford, Centre for Time Use Research.https://www.timeuse.org/sites/default/files/public/ctur_technical_paper/869/CTUR_Technical_Paper_2010-01.pdfGoogle Scholar
- Fausto Giunchiglia, Enrico Bignotti, and Mattia Zeni. 2017. Personal context modelling and annotation. In 2017 IEEE international conference on pervasive computing and communications workshops (PerCom workshops). IEEE, 117–122.Google ScholarCross Ref
- Fausto Giunchiglia, Ivano Bison, Matteo Busso, Ronald Chenu-Abente, Marcelo Rodas, Mattia Zeni, Can Gunel, Giuseppe Veltri, Amalia De Götzen, Peter Kun, Amarsanaa Ganbold, Altangerel Chagnaa, George Gaskell, Sally Stares, Miriam Bidoglia, Luca Cernuzzi, Alethia Hume, Jose Luis Zarza, Hao Xu, Donglei Song, Shyam Diwakar, Chaitanya Nutakki, Salvador Ruiz Correa, Andrea-Rebeca Mendoza, Lakmal Meegahapola, and Daniel Gatica-Perez. 2022. A worldwide diversity pilot on daily routines and social practices (2020-2021). University of Trento Technical Report - DataScientia dataset descriptors. https://iris.unitn.it/handle/11572/338382.Google Scholar
- Taesik Gong, Yewon Kim, Adiba Orzikulova, Yunxin Liu, Sung Ju Hwang, Jinwoo Shin, and Sung-Ju Lee. 2021. DAPPER: Performance Estimation of Domain Adaptation in Mobile Sensing. arXiv preprint arXiv:2111.11053(2021).Google Scholar
- Zhiqiang Gong, Ping Zhong, and Weidong Hu. 2019. Diversity in Machine Learning. IEEE Access 7(2019), 64323–64350. https://doi.org/10.1109/ACCESS.2019.2917620Google ScholarCross Ref
- Google. 2022. Adapt your app by understanding what users are doing. Retrieved February 12, 2022 from https://developers.google.com/location-context/activity-recognitionGoogle Scholar
- Valdiney V Gouveia, Taciano L Milfont, and Valeschka M Guerra. 2014. Functional theory of human values: Testing its content and structure hypotheses. Personality and individual differences 60 (2014), 41–47.Google Scholar
- Trevor Hastie, Saharon Rosset, Ji Zhu, and Hui Zou. 2009. Multi-class AdaBoost. Statistics and Its Interface 2, 3 (2009), 349–360. https://doi.org/10.4310/SII.2009.v2.n3.a8Google ScholarCross Ref
- Jia He and Fons van de Vijver. 2012. Bias and equivalence in cross-cultural research. Online readings in psychology and culture 2, 2 (2012), 2307–0919.Google Scholar
- Ida Marie Henriksen, Marianne Skaar, and Aksel Tjora. 2020. The Constitutive Practices of Public Smartphone Use. Societies 10, 4 (2020). https://doi.org/10.3390/soc10040078Google ScholarCross Ref
- Seyed Amir Hoseini-Tabatabaei, Alexander Gluhak, and Rahim Tafazolli. 2013. A survey on smartphone-based systems for opportunistic user context recognition. ACM Computing Surveys (CSUR) 45, 3 (2013), 1–51.Google ScholarDigital Library
- Tâm Huynh, Mario Fritz, and Bernt Schiele. 2008. Discovery of activity patterns using topic models. In Proceedings of the 10th international conference on Ubiquitous computing - UbiComp ’08. ACM Press, Seoul, Korea, 10. https://doi.org/10.1145/1409635.1409638Google ScholarDigital Library
- Kimmo Karkkainen and Jungseock Joo. 2021. Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 1548–1558.Google ScholarCross Ref
- Mohammed Khwaja, Sumer S. Vaid, Sara Zannone, Gabriella M. Harari, A. Aldo Faisal, and Aleksandar Matic. 2019. Modeling Personality vs. Modeling Personalidad: In-the-wild Mobile Data Analysis in Five Countries Suggests Cultural Impact on Personality Models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (Sept. 2019), 1–24. https://doi.org/10.1145/3351246Google ScholarDigital Library
- Hae-Young Kim. 2014. Analysis of variance (ANOVA) comparing means of more than two groups. Restorative dentistry & endodontics 39, 1 (2014), 74–77.Google Scholar
- Gierad Laput and Chris Harrison. 2019. Sensing Fine-Grained Hand Activity with Smartwatches. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–13. https://doi.org/10.1145/3290605.3300568Google ScholarDigital Library
- Gierad Laput and Chris Harrison. 2019. Sensing fine-grained hand activity with smartwatches. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–13.Google ScholarDigital Library
- Tong Li, Mingyang Zhang, Yong Li, Eemil Lagerspetz, Sasu Tarkoma, and Pan Hui. 2021. The Impact of Covid-19 on Smartphone Usage. IEEE Internet of Things Journal 8, 23 (Dec. 2021), 16723–16733. https://doi.org/10.1109/JIOT.2021.3073864Google ScholarCross Ref
- Robert LiKamWa, Yunxin Liu, Nicholas D. Lane, and Lin Zhong. 2013. MoodScope: building a mood sensor from smartphone usage patterns. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services - MobiSys ’13. ACM Press, Taipei, Taiwan, 389. https://doi.org/10.1145/2462456.2464449Google ScholarDigital Library
- Soo Ling Lim, Peter J Bentley, Natalie Kanakam, Fuyuki Ishikawa, and Shinichi Honiden. 2014. Investigating country differences in mobile app user behavior and challenges for software engineering. IEEE Transactions on Software Engineering 41, 1 (2014), 40–64.Google ScholarDigital Library
- Rex Liu, Albara Ah Ramli, Huanle Zhang, Erik Henricson, and Xin Liu. 2021. An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence. arXiv:2103.15990 [cs, eess] (Aug. 2021). http://arxiv.org/abs/2103.15990 arXiv:2103.15990.Google Scholar
- Tiffany Liu, Javier Hernandez, Mar Gonzalez-Franco, Antonella Maselli, Melanie Kneisel, Adam Glass, Jarnail Chudge, and Amos Miller. 2022. Characterizing and Predicting Engagement of Blind and Low-Vision People with an Audio-Based Navigation App. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22). Association for Computing Machinery, New York, NY, USA, Article 411, 7 pages. https://doi.org/10.1145/3491101.3519862Google ScholarDigital Library
- Olatz Lopez-Fernandez, Daria J. Kuss, Lucia Romo, Yannick Morvan, Laurence Kern, Pierluigi Graziani, Amélie Rousseau, Hans-Jürgen Rumpf, Anja Bischof, Ann-Kathrin Gässler, Adriano Schimmenti, Alessia Passanisi, Niko Männikkö, Maria Kääriänen, Zsolt Demetrovics, Orsolya Király, Mariano Chóliz, Juan José Zacarés, Emilia Serra, Mark D. Griffiths, Halley M. Pontes, Bernadeta Lelonek-Kuleta, Joanna Chwaszcz, Daniele Zullino, Lucien Rochat, Sophia Achab, and Joël Billieux. 2017. Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey. Journal of Behavioral Addictions 6, 2 (2017), 168 – 177. https://doi.org/10.1556/2006.6.2017.020Google ScholarCross Ref
- Amanda D Lotz. 2021. In between the global and the local: Mapping the geographies of Netflix as a multinational service. International Journal of Cultural Studies 24, 2 (2021), 195–215.Google ScholarCross Ref
- Yawei Luo, Liang Zheng, Tao Guan, Junqing Yu, and Yi Yang. 2019. Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2507–2516.Google ScholarCross Ref
- Stephen Makonin, Lyn Bartram, and Fred Popowich. 2013. A Smarter Smart Home: Case Studies of Ambient Intelligence. IEEE Pervasive Computing 12, 1 (2013), 58–66. https://doi.org/10.1109/MPRV.2012.58Google ScholarCross Ref
- Akhil Mathur, Anton Isopoussu, Nadia Berthouze, Nicholas D Lane, and Fahim Kawsar. 2019. Unsupervised domain adaptation for robust sensory systems. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. 505–509.Google ScholarDigital Library
- Akhil Mathur, Lakshmi Manasa Kalanadhabhatta, Rahul Majethia, and Fahim Kawsar. 2017. Moving Beyond Market Research: Demystifying Smartphone User Behavior in India. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (Sept. 2017), 1–27. https://doi.org/10.1145/3130947Google ScholarDigital Library
- F Joseph McClernon and Romit Roy Choudhury. 2013. I am your smartphone, and I know you are about to smoke: the application of mobile sensing and computing approaches to smoking research and treatment. Nicotine & tobacco research 15, 10 (2013), 1651–1654.Google Scholar
- Lakmal Meegahapola, Wageesha Bangamuarachchi, Anju Chamantha, Salvador Ruiz-Correa, Indika Perera, and Daniel Gatica-Perez. 2022. Sensing Eating Events in Context: A Smartphone-Only Approach. IEEE Access 10, ARTICLE (2022).Google Scholar
- Lakmal Meegahapola, William Droz, Peter Kun, Amalia de Götzen, Chaitanya Nutakki, Shyam Diwakar, Salvador Ruiz Correa, Donglei Song, Hao Xu, Miriam Bidoglia, George Gaskell, Altangerel Chagnaa, Amarsanaa Ganbold, Tsolmon Zundui, Carlo Caprini, Daniele Miorandi, Alethia Hume, Jose Luis Zarza, Luca Cernuzzi, Ivano Bison, Marcelo Rodas Britez, Matteo Busso, Ronald Chenu-Abente, Can Günel, Fausto Giunchiglia, Laura Schelenz, and Daniel Gatica-Perez. 2023. Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 4, Article 176 (jan 2023), 32 pages.Google ScholarDigital Library
- Lakmal Meegahapola and Daniel Gatica-Perez. 2020. Smartphone Sensing for the Well-Being of Young Adults: A Review. IEEE Access 9(2020), 3374–3399.Google ScholarCross Ref
- Lakmal Meegahapola, Florian Labhart, Thanh-Trung Phan, and Daniel Gatica-Perez. 2021. Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 3 (Sept. 2021), 1–26. https://doi.org/10.1145/3478126Google ScholarDigital Library
- Lakmal Meegahapola, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2020. Alone or With Others? Understanding Eating Episodes of College Students with Mobile Sensing. In 19th International Conference on Mobile and Ubiquitous Multimedia. ACM, Essen Germany, 162–166. https://doi.org/10.1145/3428361.3428463Google ScholarDigital Library
- Lakmal Meegahapola, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2020. Protecting Mobile Food Diaries from Getting too Personal. In 19th International Conference on Mobile and Ubiquitous Multimedia. ACM, Essen Germany, 212–222. https://doi.org/10.1145/3428361.3428468Google ScholarDigital Library
- Lakmal Meegahapola, Salvador Ruiz-Correa, Viridiana del Carmen Robledo-Valero, Emilio Ernesto Hernandez-Huerfano, Leonardo Alvarez-Rivera, Ronald Chenu-Abente, and Daniel Gatica-Perez. 2021. One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-Reports. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 1 (March 2021), 1–28. https://doi.org/10.1145/3448120Google ScholarDigital Library
- Abhinav Mehrotra and Mirco Musolesi. 2017. Intelligent notification systems: A survey of the state of the art and research challenges. arXiv preprint arXiv:1711.10171(2017).Google Scholar
- Kathryn Mercer, Lora Giangregorio, Eric Schneider, Parmit Chilana, Melissa Li, and Kelly Grindrod. 2016. Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. JMIR mHealth and uHealth 4, 1 (Jan. 2016), e7. https://doi.org/10.2196/mhealth.4225Google ScholarCross Ref
- Mike A Merrill and Tim Althoff. 2022. Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets. arXiv preprint arXiv:2205.13607(2022).Google Scholar
- Daniel Miller, Laila Abed Rabho, Patrick Awondo, Maya de Vries, Marília Duque, Pauline Garvey, Laura Haapio-Kirk, Charlotte Hawkins, Alfonso Otaegui, Shireen Walton, and Xinyuan Wang. 2021. The Global Smartphone: Beyond a youth technology. UCL Press. http://www.jstor.org/stable/j.ctv1b0fvh1Google Scholar
- Chan Naseeb and Bilal Al Saeedi. 2020. Activity recognition for locomotion and transportation dataset using deep learning. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. ACM, Virtual Event Mexico, 329–334. https://doi.org/10.1145/3410530.3414348Google ScholarDigital Library
- Tobias Nef, Prabitha Urwyler, Marcel Büchler, Ioannis Tarnanas, Reto Stucki, Dario Cazzoli, René Müri, and Urs Mosimann. 2015. Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data. Sensors 15, 5 (May 2015), 11725–11740. https://doi.org/10.3390/s150511725Google ScholarCross Ref
- Justin J. Nelson and Christopher M. Pieper. 2022. “Maladies of Infinite Aspiration”: Smartphones, Meaning-Seeking, and Anomigenesis. Sociological Perspectives (Aug 2022), 073112142211142. https://doi.org/10.1177/07311214221114296Google ScholarCross Ref
- Subigya Nepal, Shayan Mirjafari, Gonzalo J Martinez, Pino Audia, Aaron Striegel, and Andrew T Campbell. 2020. Detecting job promotion in information workers using mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–28. https://doi.org/10.1145/3414118Google ScholarDigital Library
- Johnny Nhan, Kendra Bowen, and Aaron Bartula. 2020. A comparison of a public and private university of the effects of low-cost streaming services and income on movie piracy. Technology in Society 60(2020), 101213.Google ScholarCross Ref
- Mikio Obuchi, Jeremy F Huckins, Weichen Wang, Alex daSilva, Courtney Rogers, Eilis Murphy, Elin Hedlund, Paul Holtzheimer, Shayan Mirjafari, and Andrew Campbell. 2020. Predicting brain functional connectivity using mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1–22. https://doi.org/10.1145/3381001Google ScholarDigital Library
- Hyungik Oh, Laleh Jalali, and Ramesh Jain. 2015. An intelligent notification system using context from real-time personal activity monitoring. In 2015 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 1–6.Google Scholar
- Jay A. Olson, Dasha A. Sandra, Élissa S. Colucci, Alain Al Bikaii, Denis Chmoulevitch, Johnny Nahas, Amir Raz, and Samuel P.L. Veissière. 2022. Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Computers in Human Behavior 129 (2022), 107138. https://doi.org/10.1016/j.chb.2021.107138Google ScholarDigital Library
- S. Park, C. Gopalsamy, R. Rajamanickam, and S. Jayaraman. 1999. The Wearable Motherboard: a flexible information infrastructure or sensate liner for medical applications. Studies in Health Technology and Informatics 62 (1999), 252–258.Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.Google ScholarDigital Library
- Le Vy Phan, Nick Modersitzki, Kim Karen Gloystein, and Sandrine Müller. 2022. Mobile Sensing Around the Globe: Considerations for Cross-Cultural Research. preprint. PsyArXiv. https://doi.org/10.31234/osf.io/q8c7yGoogle ScholarCross Ref
- Thanh-Trung Phan, Florian Labhart, Skanda Muralidhar, and Daniel Gatica-Perez. 2020. Understanding Heavy Drinking at Night through Smartphone Sensing and Active Human Engagement. In Proceedings of the 14th EAI International Conference on Pervasive Computing Technologies for Healthcare. 211–222.Google ScholarDigital Library
- Vinodkumar Prabhakaran, Rida Qadri, and Ben Hutchinson. 2022. Cultural Incongruencies in Artificial Intelligence. arXiv preprint arXiv:2211.13069(2022).Google Scholar
- Angshu Rai, Zhixian Yan, Dipanjan Chakraborty, Tri Kurniawan Wijaya, and Karl Aberer. 2012. Mining complex activities in the wild via a single smartphone accelerometer. In Proceedings of the sixth international workshop on knowledge discovery from sensor data. 43–51.Google ScholarDigital Library
- Inioluwa Deborah Raji and Joy Buolamwini. 2019. Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. ACM, Honolulu HI USA, 429–435. https://doi.org/10.1145/3306618.3314244Google ScholarDigital Library
- Haroon Rashid, Sanjana Mendu, Katharine E Daniel, Miranda L Beltzer, Bethany A Teachman, Mehdi Boukhechba, and Laura E Barnes. 2020. Predicting subjective measures of social anxiety from sparsely collected mobile sensor data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1–24.Google ScholarDigital Library
- Zubair Ahmed Ratan, Sojib Bin Zaman, Sheikh Mohammed Shariful Islam, and Hassan Hosseinzadeh. 2021. Smartphone overuse: A hidden crisis in COVID-19. Health Policy and Technology 10, 1 (March 2021), 21–22. https://doi.org/10.1016/j.hlpt.2021.01.002Google ScholarCross Ref
- Ashwini S Rathod, Abhishek Ingole, Abhay Gaidhane, and Sonali G Choudhari. 2022. Psychological Morbidities Associated With Excessive Usage of Smartphones Among Adolescents and Young Adults: A Review. Cureus (Oct 2022). https://doi.org/10.7759/cureus.30756Google ScholarCross Ref
- Simon Rump. 2011. What kind of thief are you? Linking perceptions, personality traits and music taste to illegal downloading-how preferences, traits and notions affect online crime. B.S. thesis. University of Twente.Google Scholar
- Heba Saadeh, Reem Q. Al Fayez, Assem Al Refaei, Nour Shewaikani, Hamzah Khawaldah, Sobuh Abu-Shanab, and Maysa Al-Hussaini. 2021. Smartphone Use Among University Students During COVID-19 Quarantine: An Ethical Trigger. Frontiers in Public Health 9 (July 2021), 600134. https://doi.org/10.3389/fpubh.2021.600134Google ScholarCross Ref
- Saguna Saguna, Arkady Zaslavsky, and Dipanjan Chakraborty. 2013. Complex activity recognition using context-driven activity theory and activity signatures. ACM Transactions on Computer-Human Interaction (TOCHI) 20, 6(2013), 1–34.Google ScholarDigital Library
- Darshan Santani, Trinh-Minh-Tri Do, Florian Labhart, Sara Landolt, Emmanuel Kuntsche, and Daniel Gatica-Perez. 2018. DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones. IEEE Transactions on Mobile Computing 17, 10 (Oct. 2018), 2279–2292. https://doi.org/10.1109/TMC.2018.2797901Google ScholarCross Ref
- Jeffer Eidi Sasaki, Amanda M. Hickey, John W. Staudenmayer, Dinesh John, Jane A. Kent, and Patty S. Freedson. 2016. Performance of Activity Classification Algorithms in Free-Living Older Adults. Medicine & Science in Sports & Exercise 48, 5 (May 2016), 941–950. https://doi.org/10.1249/MSS.0000000000000844Google ScholarCross Ref
- Laura Schelenz, Ivano Bison, Matteo Busso, Amalia de Götzen, Daniel Gatica-Perez, Fausto Giunchiglia, Lakmal Meegahapola, and Salvador Ruiz-Correa. 2021. The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (Virtual Event, USA) (AIES ’21). Association for Computing Machinery, New York, NY, USA, 905–915. https://doi.org/10.1145/3461702.3462595Google ScholarDigital Library
- Laura Schelenz, Ivano Bison, Matteo Busso, Amalia de Götzen, Daniel Gatica-Perez, Fausto Giunchiglia, Lakmal Meegahapola, and Salvador Ruiz-Correa. 2021. The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. ACM, Virtual Event USA, 905–915. https://doi.org/10.1145/3461702.3462595Google ScholarDigital Library
- Shalom H Schwartz. 1994. Are there universal aspects in the structure and contents of human values?Journal of social issues 50, 4 (1994), 19–45.Google Scholar
- Shalom H Schwartz, Gila Melech, Arielle Lehmann, Steven Burgess, Mari Harris, and Vicki Owens. 2001. Extending the cross-cultural validity of the theory of basic human values with a different method of measurement. Journal of cross-cultural psychology 32, 5 (2001), 519–542.Google ScholarCross Ref
- Scikit-Learn. 2022. Scikit-Learn Metrics - AUROC. Retrieved February 14, 2022 from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.htmlGoogle Scholar
- Scikit-Learn. 2022. Scikit-Learn Metrics - F1-Score. Retrieved February 14, 2022 from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.htmlGoogle Scholar
- Sandra Servia-Rodríguez, Kiran K. Rachuri, Cecilia Mascolo, Peter J. Rentfrow, Neal Lathia, and Gillian M. Sandstrom. 2017. Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Perth Australia, 103–112. https://doi.org/10.1145/3038912.3052618Google ScholarDigital Library
- Craig JR Sewall, Todd M Bear, John Merranko, and Daniel Rosen. 2020. How psychosocial well-being and usage amount predict inaccuracies in retrospective estimates of digital technology use. Mobile Media & Communication 8, 3 (2020), 379–399.Google ScholarCross Ref
- Pekka Siirtola, Perttu Laurinen, Juha Roning, and Hannu Kinnunen. 2011. Efficient accelerometer-based swimming exercise tracking. In 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, Paris, France, 156–161. https://doi.org/10.1109/CIDM.2011.5949430Google ScholarCross Ref
- Dale Southerton, Cecilla Díaz-Méndez, Alan Warde, 2012. Behavioural change and the temporal ordering of eating practices: A UK–Spain comparison. The International Journal of Sociology of Agriculture and Food 19, 1(2012), 19–36.Google Scholar
- Stephanie Stockwell, Mike Trott, Mark Tully, Jae Shin, Yvonne Barnett, Laurie Butler, Daragh McDermott, Felipe Schuch, and Lee Smith. 2021. Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review. BMJ Open Sport & Exercise Medicine 7, 1 (Jan. 2021), e000960. https://doi.org/10.1136/bmjsem-2020-000960Google ScholarCross Ref
- Marcin Straczkiewicz, Peter James, and Jukka-Pekka Onnela. 2021. A systematic review of smartphone-based human activity recognition methods for health research. npj Digital Medicine 4, 1 (Dec. 2021), 148. https://doi.org/10.1038/s41746-021-00514-4Google ScholarCross Ref
- Marcin Straczkiewicz, Peter James, and Jukka-Pekka Onnela. 2021. A systematic review of smartphone-based human activity recognition methods for health research. NPJ Digital Medicine 4, 1 (2021), 1–15.Google ScholarCross Ref
- Sining Sun, Binbin Zhang, Lei Xie, and Yanning Zhang. 2017. An unsupervised deep domain adaptation approach for robust speech recognition. Neurocomputing 257(2017), 79–87.Google ScholarCross Ref
- Kirsi Tirri and Petri Nokelainen. 2008. Identification of multiple intelligences with the Multiple Intelligence Profiling Questionnaire III. Psychology Science 50, 2 (2008), 206.Google Scholar
- Taylor Tobin. 2018. What time people typically eat dinner in 12 different places around the world. https://www.insider.com/dinner-times-around-the-world-2018-11Google Scholar
- Fons Van de Vijver and Norbert K Tanzer. 2004. Bias and equivalence in cross-cultural assessment: An overview. European Review of Applied Psychology 54, 2 (2004), 119–135.Google ScholarCross Ref
- Alexander J.A.M. van Deursen, Colin L. Bolle, Sabrina M. Hegner, and Piet A.M. Kommers. 2015. Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Computers in Human Behavior 45 (2015), 411–420. https://doi.org/10.1016/j.chb.2014.12.039Google ScholarDigital Library
- Vincent T. van Hees, Rajna Golubic, Ulf Ekelund, and Søren Brage. 2013. Impact of study design on development and evaluation of an activity-type classifier. Journal of Applied Physiology 114, 8 (April 2013), 1042–1051. https://doi.org/10.1152/japplphysiol.00984.2012Google ScholarCross Ref
- Rui Wang, Weichen Wang, Min SH Aung, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A Scherer, 2017. Predicting symptom trajectories of schizophrenia using mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1–24. https://doi.org/10.1145/3130976Google ScholarDigital Library
- Rui Wang, Weichen Wang, Mikio Obuchi, Emily Scherer, Rachel Brian, Dror Ben-Zeev, Tanzeem Choudhury, John Kane, Martar Hauser, Megan Walsh, 2020. On predicting relapse in schizophrenia using mobile sensing in a randomized control trial. In 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, 1–8. https://doi.org/10.1109/PerCom45495.2020.9127365Google ScholarCross Ref
- Sun-Chong Wang. 2003. Artificial neural network. In Interdisciplinary computing in java programming. Springer, 81–100.Google Scholar
- Weichen Wang, Subigya Nepal, Jeremy F Huckins, Lessley Hernandez, Vlado Vojdanovski, Dante Mack, Jane Plomp, Arvind Pillai, Mikio Obuchi, Alex daSilva, 2022. First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1–32. https://doi.org/10.1145/3543194Google ScholarDigital Library
- Xinxi Wang, David Rosenblum, and Ye Wang. 2012. Context-Aware Mobile Music Recommendation for Daily Activities. In Proceedings of the 20th ACM International Conference on Multimedia (Nara, Japan) (MM ’12). Association for Computing Machinery, New York, NY, USA, 99–108. https://doi.org/10.1145/2393347.2393368Google ScholarDigital Library
- Joshua M Wiener, Raymond J Hanley, Robert Clark, and Joan F Van Nostrand. 1990. Measuring the activities of daily living: Comparisons across national surveys. Journal of gerontology 45, 6 (1990), S229–S237.Google ScholarCross Ref
- Garrett Wilson, Janardhan Rao Doppa, and Diane J Cook. 2022. Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition. arXiv preprint arXiv:2207.04367(2022).Google Scholar
- Xuhai Xu, Xin Liu, Han Zhang, Weichen Wang, Subigya Nepal, Yasaman Sefidgar, Woosuk Seo, Kevin S Kuehn, Jeremy F Huckins, Margaret E Morris, 2023. GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1–34.Google ScholarDigital Library
- Xuhai Xu, Jennifer Mankoff, and Anind K Dey. 2021. Understanding practices and needs of researchers in human state modeling by passive mobile sensing. CCF Transactions on Pervasive Computing and Interaction 3, 4(2021), 344–366.Google ScholarCross Ref
- Rong Yang and Baowei Wang. 2016. PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors. Information 7, 4 (Dec. 2016), 72. https://doi.org/10.3390/info7040072Google ScholarCross Ref
- Nira Yuval-Davis. 2004. Gender and nation. Routledge.Google Scholar
- Jingwen Zhang, Dingwen Li, Ruixuan Dai, Heidy Cos, Gregory A Williams, Lacey Raper, Chet W Hammill, and Chenyang Lu. 2022. Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2 (2022), 1-27. Google ScholarDigital Library
- Shichao Zhang. 2012. Nearest neighbor selection for iteratively kNN imputation. Journal of Systems and Software 85, 11 (2012), 2541–2552.Google ScholarDigital Library
- Wanyi Zhang, Qiang Shen, Stefano Teso, Bruno Lepri, Andrea Passerini, Ivano Bison, and Fausto Giunchiglia. 2021. Putting human behavior predictability in context. EPJ Data Science 10, 1 (2021), 42.Google ScholarCross Ref
- Chen Zheng, Wendy Yajun Huang, Sinead Sheridan, Cindy Hui-Ping Sit, Xiang-Ke Chen, and Stephen Heung-Sang Wong. 2020. COVID-19 Pandemic Brings a Sedentary Lifestyle in Young Adults: A Cross-Sectional and Longitudinal Study. International Journal of Environmental Research and Public Health 17, 17 (Aug. 2020), 6035. https://doi.org/10.3390/ijerph17176035Google ScholarCross Ref
- Yuchao Zhou, Suparna De, Wei Wang, Ruili Wang, and Klaus Moessner. 2018. Missing data estimation in mobile sensing environments. IEEE Access 6(2018), 69869–69882.Google ScholarCross Ref
- Ran Zhu, Zhuoling Xiao, Ying Li, Mingkun Yang, Yawen Tan, Liang Zhou, Shuisheng Lin, and Hongkai Wen. 2019. Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning. IEEE Access 7(2019), 75490–75499. https://doi.org/10.1109/ACCESS.2019.2922104Google ScholarCross Ref
- Zhendong Zhuang and Yang Xue. 2019. Sport-Related Human Activity Detection and Recognition Using a Smartwatch. Sensors 19, 22 (Nov. 2019), 5001. https://doi.org/10.3390/s19225001Google ScholarCross Ref
Index Terms
- Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
Recommendations
Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries
Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health ...
Smartphone-based monitoring system for activities of daily living for elderly people and their relatives etc.
UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publicationWe developed a smartphone-based monitoring system to allay the anxiety of elderly people and that of their relatives, friends and caregivers by unobtrusively monitoring an elderly person's activities of daily living. A smartphone of the elderly person ...
Recognising activities of daily living from patterns of object use
To provide assistance and support to the elderly disabled and cognitively impaired, the recognition of their activities of daily living (ADL) must be accurate and precise with regards to the object use for the activity situations. Current ...
Comments