{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T10:47:53Z","timestamp":1781174873601,"version":"3.54.1"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T00:00:00Z","timestamp":1739318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSF","award":["1722792, 23091241, 2211302, 2211888, 2213636, and 2105494"],"award-info":[{"award-number":["1722792, 23091241, 2211302, 2211888, 2213636, and 2105494"]}]},{"DOI":"10.13039\/100006751","name":"US Army","doi-asserted-by":"crossref","award":["W911NF-17-2-0196"],"award-info":[{"award-number":["W911NF-17-2-0196"]}],"id":[{"id":"10.13039\/100006751","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM J. Comput. Sustain. Soc."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            With sleep deprivation being a public health concern, sleep monitoring technology, mainly through consumer-grade wearables, has shown value among users to better understand their most fundamental measure of health. Unfortunately, utilizing wearable technology is bound to the conditions of users owning these devices and using them at bedtime every night. While wearables can deliver highly personalized sleep insights to users, they inadvertently affect the ability of sleep monitoring solutions to reach unprivileged sections of society due to added costs and device accessibility. With our primary motivation to promote sleep monitoring for public health use cases at the population scale, we developed\n            <jats:italic>WiSleep<\/jats:italic>\n            , a sleep monitoring system that infers sleep duration from solely relying on a user\u2019s smartphone without requiring a wearable device. Unlike prior efforts that use supervised learning methods and require labeled training data to train sleep models, our method is based on unsupervised learning, which enables easy deployment to new population groups or new regions without a need for labeled data collection and training. Specifically, we employ the smartphone activity of the user, represented by time series of WiFi network event rates, as input data to infer the user\u2019s sleep duration (i.e., sleep time and wake time) through an unsupervised Bayesian change point detection ensemble model. Our evaluation shows\n            <jats:italic>WiSleep<\/jats:italic>\n            \u2019s efficacy in being a low-cost accessible sleep monitoring approach. We present results that yield comparable performance to prior techniques, particularly those requiring new users\u2019 labeled data to achieve model personalization. System evaluation from a user study achieved an average of 93.68% accuracy within 59 minutes of sleep time error, 31 minutes of wake time error, and 57 minutes of sleep duration error by utilizing coarse-grained time series data. We demonstrate the application of our technique to predict sleep for 1,000 anonymous users and enable population-scale analytics with low computational overhead.\n          <\/jats:p>","DOI":"10.1145\/3705722","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T11:24:17Z","timestamp":1733829857000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["WiSleep: Smartphone-driven Sleep Population Monitoring with Unsupervised Learning"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-4479","authenticated-orcid":false,"given":"Priyanka","family":"Mary Mammen","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4520-9783","authenticated-orcid":false,"given":"Camellia","family":"Zakaria","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-370X","authenticated-orcid":false,"given":"Tergel","family":"Molom-Ochir","sequence":"additional","affiliation":[{"name":"Duke University, Durham, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3427-3333","authenticated-orcid":false,"given":"Amee","family":"Trivedi","sequence":"additional","affiliation":[{"name":"AT&amp;T Inc, Dallas, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5435-1901","authenticated-orcid":false,"given":"Prashant","family":"Shenoy","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-9902","authenticated-orcid":false,"given":"Rajesh","family":"Balan","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing","author":"Abdullah Saeed","year":"2014","unstructured":"Saeed Abdullah, Mark Matthews, Elizabeth L. Murnane, Geri Gay, and Tanzeem Choudhury. 2014. Towards circadian computing: Early to bed and early to rise makes some of us unhealthy and sleep deprived. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing."},{"key":"e_1_3_2_3_2","volume-title":"Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem","year":"2006","unstructured":"Bruce M. Altevogt and Harvey R. Colten. 2006. Sleep Disorders and Sleep Deprivation: An Unmet Public Health Problem. National Academies Press."},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1145\/3038912.3052637","volume-title":"Proceedings of the 26th International Conference on World Wide Web","author":"Althoff Tim","year":"2017","unstructured":"Tim Althoff, Eric Horvitz, Ryen W. White, and Jamie Zeitzer. 2017. Harnessing the web for population-scale physiological sensing: A case study of sleep and performance. In Proceedings of the 26th International Conference on World Wide Web. 113\u2013122."},{"issue":"3","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"333","DOI":"10.5664\/jcsm.26597","article-title":"Insomnia and daytime napping in older adults","volume":"2","author":"Ancoli-Israel Sonia","year":"2006","unstructured":"Sonia Ancoli-Israel and Jennifer L. Martin. 2006. Insomnia and daytime napping in older adults. Journal of Clinical Sleep Medicine 2, 3 (2006), 333\u2013342.","journal-title":"Journal of Clinical Sleep Medicine"},{"key":"e_1_3_2_6_2","unstructured":"Apple. 2020. Home Page. Retrieved July 8 2020 from https:\/\/www.apple.com"},{"key":"e_1_3_2_7_2","article-title":"ArubaOS 6.5.x Syslog Messages, Reference Guide","author":"Aruba Networks","year":"2016","unstructured":"Aruba Networks. 2016. ArubaOS 6.5.x Syslog Messages, Reference Guide. Retrieved December 13, 2024 from https:\/\/www.hpe.com\/psnow\/doc\/c05321932","journal-title":"Retrieved December 13, 2024 from https:\/\/www.hpe.com\/psnow\/doc\/c05321932"},{"issue":"3","key":"e_1_3_2_8_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/07448480109596017","article-title":"Sleep habits and patterns of college students: A preliminary study","volume":"50","author":"Jr. Walter C. Buboltz","year":"2001","unstructured":"Walter C. Buboltz Jr., Franklin Brown, and Barlow Soper. 2001. Sleep habits and patterns of college students: A preliminary study. Journal of American College Health 50, 3 (2001), 131\u2013135.","journal-title":"Journal of American College Health"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Mary A. Carskadon and William C. Dement. 2005. Normal human sleep: An overview. In Principles and Practice of Sleep Medicine (4th ed.) Meir H. Kryger Thomas Roth and William Dement (Eds.). Saunders 13\u201323.","DOI":"10.1016\/B0-72-160797-7\/50009-4"},{"issue":"3","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3392049","article-title":"iSleep: A smartphone system for unobtrusive sleep quality monitoring","volume":"16","author":"Chang Xiangmao","year":"2020","unstructured":"Xiangmao Chang, Cheng Peng, Guoliang Xing, Tian Hao, and Gang Zhou. 2020. iSleep: A smartphone system for unobtrusive sleep quality monitoring. ACM Transactions on Sensor Networks 16, 3 (2020), 1\u201332.","journal-title":"ACM Transactions on Sensor Networks"},{"key":"e_1_3_2_11_2","first-page":"145","volume-title":"Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops","author":"Chen Zhenyu","year":"2013","unstructured":"Zhenyu Chen, Mu Lin, Fanglin Chen, Nicholas D. Lane, Giuseppe Cardone, Rui Wang, Tianxing Li, Yiqiang Chen, Tanzeem Choudhury, and Andrew T. Campbell. 2013. Unobtrusive sleep monitoring using smartphones. In Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops. IEEE, 145\u2013152."},{"issue":"4","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/00031305.1995.10476177","article-title":"Understanding the Metropolis-Hastings algorithm","volume":"49","author":"Chib Siddhartha","year":"1995","unstructured":"Siddhartha Chib and Edward Greenberg. 1995. Understanding the Metropolis-Hastings algorithm. American Statistician 49, 4 (1995), 327\u2013335.","journal-title":"American Statistician"},{"issue":"1","key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"e0169901","DOI":"10.1371\/journal.pone.0169901","article-title":"SensibleSleep: A Bayesian model for learning sleep patterns from smartphone events","volume":"12","author":"Cuttone Andrea","year":"2017","unstructured":"Andrea Cuttone, Per B\u00e6kgaard, Vedran Sekara, H\u00e5kan Jonsson, Jakob Eg Larsen, and Sune Lehmann. 2017. SensibleSleep: A Bayesian model for learning sleep patterns from smartphone events. PLoS One 12, 1 (2017), e0169901.","journal-title":"PLoS One"},{"issue":"1","key":"e_1_3_2_14_2","article-title":"SensibleSleep: A Bayesian model for learning sleep patterns from smartphone events","volume":"12","author":"Cuttone Andrea","year":"2017","unstructured":"Andrea Cuttone, Per Bakgaard, Vedran Sekara, H\u00e5kan Jonsson, Jakob Eg Larsen, and Sune Lehmann. 2017. SensibleSleep: A Bayesian model for learning sleep patterns from smartphone events. PLoS One 12, 1 (2017), e0169901.","journal-title":"PLoS One"},{"key":"e_1_3_2_15_2","article-title":"Global mobile consumer trends, 2nd edition: Mobile continues its global reach into all aspects of consumers\u2019 lives","author":"Deloitte","year":"2017","unstructured":"Deloitte. 2017. Global mobile consumer trends, 2nd edition: Mobile continues its global reach into all aspects of consumers\u2019 lives. Deloitte Report. Retrieved December 13, 2024 from https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/technology-media-telecommunications\/us-global-mobile-consumer-survey-second-edition.pdf","journal-title":"Deloitte Report. Retrieved December 13, 2024 from https:\/\/www2.deloitte.com\/content\/dam\/Deloitte\/us\/Documents\/technology-media-telecommunications\/us-global-mobile-consumer-survey-second-edition.pdf"},{"key":"e_1_3_2_16_2","first-page":"265","volume-title":"Proceedings of the 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI \u201918)","author":"El-Khadiri Yassine","year":"2018","unstructured":"Yassine El-Khadiri, Gabriel Corona, C\u00e9dric Rose, and Fran\u00e7ois Charpillet. 2018. Sleep activity recognition using binary motion sensors. In Proceedings of the 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI \u201918). IEEE, 265\u2013269."},{"issue":"2","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"250","DOI":"10.15288\/jsad.2012.73.250","article-title":"Leisure activities, the social weekend, and alcohol use: Evidence from a daily study of first-year college students","volume":"73","author":"Finlay Andrea K.","year":"2012","unstructured":"Andrea K. Finlay, Nilam Ram, Jennifer L. Maggs, and Linda L. Caldwell. 2012. Leisure activities, the social weekend, and alcohol use: Evidence from a daily study of first-year college students. Journal of Studies on Alcohol and Drugs 73, 2 (2012), 250\u2013259.","journal-title":"Journal of Studies on Alcohol and Drugs"},{"key":"e_1_3_2_18_2","unstructured":"Fitbit Inc. 2020. Home Page. Retrieved December 13 2024 from https:\/\/www.fitbit.com"},{"issue":"42","key":"e_1_3_2_19_2","first-page":"1175","article-title":"Perceived insufficient rest or sleep among adults\u2014United States, 2008.","volume":"58","author":"Control Centers for Disease","year":"2009","unstructured":"Centers for Disease Control and Prevention (CDC). 2009. Perceived insufficient rest or sleep among adults\u2014United States, 2008. MMWR: Morbidity and Mortality Weekly Report 58, 42 (2009), 1175.","journal-title":"MMWR: Morbidity and Mortality Weekly Report"},{"issue":"1","key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/insr.12243","article-title":"Bayesian model averaging: A systematic review and conceptual classification","volume":"86","author":"Fragoso Tiago M.","year":"2017","unstructured":"Tiago M. Fragoso, Wesley Bertoli, and Francisco Louzada. 2017. Bayesian model averaging: A systematic review and conceptual classification. International Statistical Review 86, 1 (December 2017), 1\u201328.","journal-title":"International Statistical Review"},{"issue":"12","key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.5664\/jcsm.3272","article-title":"The sleep and technology use of Americans: Findings from the National Sleep Foundation\u2019s 2011 Sleep in America poll","volume":"9","author":"Gradisar Michael","year":"2013","unstructured":"Michael Gradisar, Amy R. Wolfson, Allison G. Harvey, Lauren Hale, Russell Rosenberg, and Charles A. Czeisler. 2013. The sleep and technology use of Americans: Findings from the National Sleep Foundation\u2019s 2011 Sleep in America poll. Journal of Clinical Sleep Medicine 9, 12 (2013), 1291\u20131299.","journal-title":"Journal of Clinical Sleep Medicine"},{"issue":"3","key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1542\/peds.2014-1697","article-title":"School start times for adolescents","volume":"134","year":"2014","unstructured":"Adolescent Sleep Working Group. 2014. School start times for adolescents. Pediatrics 134, 3 (2014), 642\u2013649.","journal-title":"Pediatrics"},{"issue":"6","key":"e_1_3_2_23_2","first-page":"1514","article-title":"Sleep Hunter: Towards fine grained sleep stage tracking with smartphones","volume":"15","author":"Gu Weixi","year":"2015","unstructured":"Weixi Gu, Longfei Shangguan, Zheng Yang, and Yunhao Liu. 2015. Sleep Hunter: Towards fine grained sleep stage tracking with smartphones. IEEE Transactions on Mobile Computing 15, 6 (2015), 1514\u20131527.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_2_24_2","first-page":"1","volume-title":"Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems","author":"Hao Tian","year":"2013","unstructured":"Tian Hao, Guoliang Xing, and Gang Zhou. 2013. iSleep: Unobtrusive sleep quality monitoring using smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems. 1\u201314."},{"key":"e_1_3_2_25_2","first-page":"94","volume-title":"Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services","author":"Hong Hande","year":"2016","unstructured":"Hande Hong, Chengwen Luo, and Mun Choon Chan. 2016. SocialProbe: Understanding social interaction through passive WiFi monitoring. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services. 94\u2013103."},{"key":"e_1_3_2_26_2","volume-title":"Proceedings of the ACM Interact. Mob. Wearable Ubiquitous Technology","author":"Hsu Chen-Yu","year":"2017","unstructured":"Chen-Yu Hsu, Aayush Ahuja, Shichao Yue, Rumen Hristov, Zachary Kabelac, and Dina Katabi. 2017. Zero-effort in-home sleep and insomnia monitoring using radio signals. In Proceedings of the ACM Interact. Mob. Wearable Ubiquitous Technology."},{"issue":"14","key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"6631","DOI":"10.3390\/s23146631","article-title":"Packets-to-prediction: An unobtrusive mechanism for identifying coarse-grained sleep patterns with WiFi MAC layer traffic","volume":"23","author":"Jaisinghani Dheryta","year":"2023","unstructured":"Dheryta Jaisinghani and Nishtha Phutela. 2023. Packets-to-prediction: An unobtrusive mechanism for identifying coarse-grained sleep patterns with WiFi MAC layer traffic. Sensors 23, 14 (2023), 6631.","journal-title":"Sensors"},{"key":"e_1_3_2_28_2","first-page":"9","volume-title":"Proceedings of the 18th AGILE International Conference on Geographic Information Science","author":"Kalogianni Eftychia","year":"2015","unstructured":"Eftychia Kalogianni, R. Sileryte, Marco Lam, Kaixuan Zhou, Martijn Van der Ham, S. Van der Spek, and E. Verbree. 2015. Passive WiFi monitoring of the rhythm of the campus. In Proceedings of the 18th AGILE International Conference on Geographic Information Science. 9\u201314."},{"issue":"9","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"9739","DOI":"10.1109\/JSEN.2023.3262747","article-title":"Pressure-sensor-based sleep status and quality evaluation system","volume":"23","author":"Kau Lih-Jen","year":"2023","unstructured":"Lih-Jen Kau, Mao-Yin Wang, and Houcheng Zhou. 2023. Pressure-sensor-based sleep status and quality evaluation system. IEEE Sensors Journal 23, 9 (2023), 9739\u20139754.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_30_2","volume-title":"Proceedings of the 30th IEEE Conference on Tools with Artificial Intelligence","author":"Khadiri Yassine El","year":"2018","unstructured":"Yassine El Khadiri, Gabriel Corona, Ceedric Rose, and Francois Charpillet. 2018. Sleep activity recognition using binary motion sensors. In Proceedings of the 30th IEEE Conference on Tools with Artificial Intelligence."},{"key":"e_1_3_2_31_2","first-page":"1","volume-title":"Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM \u201917)","author":"Khan Usman Mahmood","year":"2017","unstructured":"Usman Mahmood Khan, Zain Kabir, Syed Ali Hassan, and Syed Hassan Ahmed. 2017. A deep learning framework using passive WiFi sensing for respiration monitoring. In Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM \u201917). IEEE, 1\u20136."},{"issue":"9","key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1093\/aje\/kwp023","article-title":"Sleep duration in the United States: A cross-sectional population-based study","volume":"169","author":"Krueger Patrick M.","year":"2009","unstructured":"Patrick M. Krueger and Elliot M. Friedman. 2009. Sleep duration in the United States: A cross-sectional population-based study. American Journal of Epidemiology 169, 9 (2009), 1052\u20131063.","journal-title":"American Journal of Epidemiology"},{"issue":"7","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1080\/07420528.2020.1754848","article-title":"A novel machine learning unsupervised algorithm for sleep\/wake identification using actigraphy","volume":"37","author":"Li Xinyue","year":"2020","unstructured":"Xinyue Li, Yunting Zhang, Fan Jiang, and Hongyu Zhao. 2020. A novel machine learning unsupervised algorithm for sleep\/wake identification using actigraphy. Chronobiology International 37, 7 (2020), 1002\u20131015.","journal-title":"Chronobiology International"},{"issue":"1","key":"e_1_3_2_34_2","first-page":"189","article-title":"SleepSense: A noncontact and cost-effective sleep monitoring system","volume":"11","author":"Lin Feng","year":"2016","unstructured":"Feng Lin, Yan Zhuang, Chen Song, Aosen Wang, Yiran Li, Changzhan Gu, Changzhi Li, and Wenyao Xu. 2016. SleepSense: A noncontact and cost-effective sleep monitoring system. IEEE Transactions on Biomedical Circuits and Systems 11, 1 (2016), 189\u2013202.","journal-title":"IEEE Transactions on Biomedical Circuits and Systems"},{"issue":"1","key":"e_1_3_2_35_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0165-1781(99)00119-5","article-title":"Sleep loss and daytime sleepiness in the general adult population of Japan","volume":"93","author":"Liu Xianchen","year":"2000","unstructured":"Xianchen Liu, Makoto Uchiyama, Keiko Kim, Masako Okawa, Kayo Shibui, Yoshihisa Kudo, Yuriko Doi, Masumi Minowa, and Ryuji Ogihara. 2000. Sleep loss and daytime sleepiness in the general adult population of Japan. Psychiatry Research 93, 1 (2000), 1\u201311.","journal-title":"Psychiatry Research"},{"key":"e_1_3_2_36_2","volume-title":"Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS \u201922): Workshop on Learning from Time Series for Health","author":"Mammen Priyanka Mary","year":"2022","unstructured":"Priyanka Mary Mammen and Prashant Shenoy. 2022. Are you asleep when your phone is asleep? Semi-supervised methods to infer sleep from smart devices. In Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS \u201922): Workshop on Learning from Time Series for Health. 1\u20135."},{"key":"e_1_3_2_37_2","first-page":"322","volume-title":"Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare","author":"Mammen Priyanka Mary","year":"2023","unstructured":"Priyanka Mary Mammen, Camellia Zakaria, and Prashant Shenoy. 2023. Personalized sleep monitoring using smartphones and semi-supervised learning. In Proceedings of the International Conference on Pervasive Computing Technologies for Healthcare. 322\u2013338."},{"issue":"4","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s40012-023-00389-8","article-title":"SleepLess: Personalized sleep monitoring using smartphones and semi-supervised learning","volume":"11","author":"Mammen Priyanka Mary","year":"2023","unstructured":"Priyanka Mary Mammen, Camellia Zakaria, and Prashant Shenoy. 2023. SleepLess: Personalized sleep monitoring using smartphones and semi-supervised learning. CSI Transactions on ICT 11, 4 (2023), 203\u2013219.","journal-title":"CSI Transactions on ICT"},{"key":"e_1_3_2_39_2","first-page":"477","volume-title":"Proceedings of the SIGCHI Conference on Human Factors in Computing Systems","author":"Min Jun-Ki","year":"2014","unstructured":"Jun-Ki Min, Afsaneh Doryab, Jason Wiese, Shahriyar Amini, John Zimmerman, and Jason I. Hong. 2014. Toss \u2018N\u2019 Turn: Smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 477\u2013486."},{"key":"e_1_3_2_40_2","first-page":"230","volume-title":"Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems (SenSys \u201916)","author":"Nguyen Anh","year":"2016","unstructured":"Anh Nguyen, Raghda Alqurashi, Zohreh Raghebi, Farnoush Banaei Kashani, Ann C. Halbower, and Tam Vu. 2016. A lightweight and inexpensive in-ear sensing system for automatic whole-night sleep stage monitoring. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems (SenSys \u201916). ACM, 230\u2013244."},{"issue":"3","key":"e_1_3_2_41_2","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1055\/s-2002-34317","article-title":"Knowledge and attitudes of primary care physicians toward sleep and sleep disorders","volume":"6","author":"Papp Klara K.","year":"2002","unstructured":"Klara K. Papp, Carolyn E. Penrod, and Kingman P. Strohl. 2002. Knowledge and attitudes of primary care physicians toward sleep and sleep disorders. Sleep and Breathing 6, 3 (2002), 103\u2013109.","journal-title":"Sleep and Breathing"},{"issue":"3","key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1177\/1556264615593494","article-title":"Sharing public health research data: Toward the development of ethical data-sharing practice in low- and middle-income settings","volume":"10","author":"Parker Michael","year":"2015","unstructured":"Michael Parker and Susan Bull. 2015. Sharing public health research data: Toward the development of ethical data-sharing practice in low- and middle-income settings. Journal of Empirical Research on Human Research Ethics 10, 3 (2015), 217\u2013224.","journal-title":"Journal of Empirical Research on Human Research Ethics"},{"key":"e_1_3_2_43_2","article-title":"Raising awareness of sleep as a healthy behavior","volume":"10","author":"Perry Geraldine S.","year":"2013","unstructured":"Geraldine S. Perry, Susheel P. Patil, and Letitia R. Presley-Cantrell. 2013. Raising awareness of sleep as a healthy behavior. Preventing Chronic Disease 10 (2013), E133.","journal-title":"Preventing Chronic Disease"},{"key":"e_1_3_2_44_2","unstructured":"Tyler Schmall. 2018. Americans spend half their lives in front of screens. New York Post. Retrieved August 13 2018 from https:\/\/nypost.com\/2018\/08\/13\/americans-spend-half-their-lives-in-front-of-screens\/"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/2750858.2804280","volume-title":"Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing","author":"Rahman Tauhidur","year":"2015","unstructured":"Tauhidur Rahman, Alexander T. Adams, Ruth Vinisha Ravichandran, Mi Zhang, Shwetak N. Patel, Julie A. Kientz, and Tanzeem Choudhury. 2015. DoppleSleep: A contactless unobtrusive sleep sensing system using short-range Doppler radar. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 39\u201350."},{"key":"e_1_3_2_46_2","first-page":"1","volume-title":"Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments","author":"Rashid Haroon","year":"2017","unstructured":"Haroon Rashid, Pushpendra Singh, and Krithi Ramamritham. 2017. Revisiting selection of residential consumers for demand response programs. In Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments. 1\u20134."},{"key":"e_1_3_2_47_2","first-page":"1194","volume-title":"Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM \u201915)","author":"Ren Yanzhi","year":"2015","unstructured":"Yanzhi Ren, Chen Wang, Jie Yang, and Yingying Chen. 2015. Fine-grained sleep monitoring: Hearing your breathing with smartphones. In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM \u201915). IEEE, 1194\u20131202."},{"issue":"1","key":"e_1_3_2_48_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1097\/JOM.0b013e3181c78c30","article-title":"The cost of poor sleep: Workplace productivity loss and associated costs","volume":"52","author":"Rosekind Mark R.","year":"2010","unstructured":"Mark R. Rosekind, Kevin B. Gregory, Melissa M. Mallis, Summer L. Brandt, Brian Seal, and Debra Lerner. 2010. The cost of poor sleep: Workplace productivity loss and associated costs. Journal of Occupational and Environmental Medicine 52, 1 (2010), 91\u201398.","journal-title":"Journal of Occupational and Environmental Medicine"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1145\/2398776.2398808","volume-title":"Proceedings of the 2012 Internet Measurement Conference","author":"Sommers Joel","year":"2012","unstructured":"Joel Sommers and Paul Barford. 2012. Cell vs. WiFi: On the performance of metro area mobile connections. In Proceedings of the 2012 Internet Measurement Conference. 301\u2013314."},{"issue":"2","key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.3122\/jabfm.2008.02.070167","article-title":"How is your sleep: A neglected topic for health care screening","volume":"21","author":"Sorscher Adam J.","year":"2008","unstructured":"Adam J. Sorscher. 2008. How is your sleep: A neglected topic for health care screening. Journal of the American Board of Family Medicine 21, 2 (2008), 141\u2013148.","journal-title":"Journal of the American Board of Family Medicine"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.maturitas.2018.03.016","article-title":"The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review","volume":"112","author":"Straiton Nicola","year":"2018","unstructured":"Nicola Straiton, Muaddi Alharbi, Adrian Bauman, Lis Neubeck, Janice Gullick, Ravinay Bhindi, and Robyn Gallagher. 2018. The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review. Maturitas 112 (2018), 85\u201393.","journal-title":"Maturitas"},{"key":"e_1_3_2_52_2","article-title":"United Nations Sustainable Development","author":"United Nations.","unstructured":"United Nations. n.d. United Nations Sustainable Development. Retrieved December 13, 2024 from https:\/\/www.un.org\/sustainabledevelopment\/","journal-title":"Retrieved December 13, 2024 from https:\/\/www.un.org\/sustainabledevelopment\/"},{"key":"e_1_3_2_53_2","article-title":"Addressing data quality challenges in observational ambulatory studies: Analysis, methodologies and practical solutions for wrist-worn wearable monitoring","author":"Donckt Jonas Van Der","year":"2024","unstructured":"Jonas Van Der Donckt, Nicolas Vandenbussche, Jeroen Van Der Donckt, Stephanie Chen, Marija Stojchevska, Mathias De Brouwer, Bram Steenwinckel, Koen Paemeleire, Femke Ongenae, and Sofie Van Hoecke. 2024. Addressing data quality challenges in observational ambulatory studies: Analysis, methodologies and practical solutions for wrist-worn wearable monitoring. arXiv preprint arXiv:2401.13518 (2024).","journal-title":"arXiv preprint arXiv:2401.13518"},{"key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.chb.2014.12.039","article-title":"Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender","volume":"45","author":"Deursen Alexander J. A. M. Van","year":"2015","unstructured":"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\u2013420.","journal-title":"Computers in Human Behavior"},{"issue":"4","key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3287073","article-title":"Large-scale automatic depression screening using meta-data from WiFi infrastructure","volume":"2","author":"Ware Shweta","year":"2018","unstructured":"Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander Russell, and Bing Wang. 2018. Large-scale automatic depression screening using meta-data from WiFi infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 4 (2018), 1\u201327.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_56_2","article-title":"A widely applicable Bayesian information criterion","volume":"14","author":"Watanabe Sumio","year":"2013","unstructured":"Sumio Watanabe. 2013. A widely applicable Bayesian information criterion. Journal of Machine Learning Research 14 (2013), 867\u2013897.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3359139","article-title":"StressMon: Scalable detection of perceived stress and depression using passive sensing of changes in work routines and group interactions","volume":"3","author":"Zakaria Camellia","year":"2019","unstructured":"Camellia Zakaria, Rajesh Balan, and Youngki Lee. 2019. StressMon: Scalable detection of perceived stress and depression using passive sensing of changes in work routines and group interactions. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1\u201329.","journal-title":"Proceedings of the ACM on Human-Computer Interaction"},{"issue":"4","key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3569489","article-title":"SleepMore: Inferring sleep duration at scale via multi-device WiFi sensing","volume":"6","author":"Zakaria Camellia","year":"2023","unstructured":"Camellia Zakaria, Gizem Yilmaz, Priyanka Mary Mammen, Michael Chee, Prashant Shenoy, and Rajesh Balan. 2023. SleepMore: Inferring sleep duration at scale via multi-device WiFi sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1\u201332.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_3_2_59_2","volume-title":"Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops","author":"Zhenyu Chen","year":"2013","unstructured":"Chen Zhenyu, Nicholas Lane, Guiseppe Cardone, Mu Lin, Tanzeem Choudhury, and Andrew Campbell. 2013. Unobtrusive sleep monitoring using smartphones. In Proceedings of the 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops."}],"container-title":["ACM Journal on Computing and Sustainable Societies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705722","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3705722","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:12Z","timestamp":1750295892000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3705722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,12]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3,31]]}},"alternative-id":["10.1145\/3705722"],"URL":"https:\/\/doi.org\/10.1145\/3705722","relation":{},"ISSN":["2834-5533"],"issn-type":[{"value":"2834-5533","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,12]]},"assertion":[{"value":"2024-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}