{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T20:55:33Z","timestamp":1776113733675,"version":"3.50.1"},"reference-count":179,"publisher":"Association for Computing Machinery (ACM)","issue":"13s","license":[{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Saudi Arabian Cultural bureau in London"},{"name":"King Abdul Aziz University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Increasingly, buildings are being fitted with sensors for the needs of different sectors, such as education, industry and business. Using Internet of Things devices combined with analysis of data being generated by these devices, it is possible to infer a number of metrics, e.g., building occupancy and activities of occupants. The information thus gathered can be used to develop software applications to support energy management, occupant comfort, and space utilization. This survey explores the use of sensors in smart building environments, identifying different approaches to employ sensors in buildings. The most commonly used data-driven approaches for activity recognition in such buildings is also investigated, concluding by highlighting current research challenges and future research directions in this area.<\/jats:p>","DOI":"10.1145\/3596600","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T12:03:25Z","timestamp":1684238605000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["Sensing within Smart Buildings: A Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4477-7521","authenticated-orcid":false,"given":"Wael","family":"Alsafery","sequence":"first","affiliation":[{"name":"Cardiff University, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3597-2646","authenticated-orcid":false,"given":"Omer","family":"Rana","sequence":"additional","affiliation":[{"name":"Cardiff University, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0190-3346","authenticated-orcid":false,"given":"Charith","family":"Perera","sequence":"additional","affiliation":[{"name":"Cardiff University, UK"}]}],"member":"320","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"(n.d.). Connected campus. Retrieved January 26 2023 from https:\/\/cto.berkeley.edu\/innovation\/connected-campus."},{"key":"e_1_3_1_3_2","unstructured":"(n.d.). Sint-Maarten Hospital Belgium. Retrieved January 7 2023 from https:\/\/www.siemens.com\/global\/en\/products\/buildings\/references\/sint-maarten-hospital.html"},{"key":"e_1_3_1_4_2","unstructured":"MMR. 2020. Global Smart Building Market Key Trends (2017\u20132024)\u2014Market Size. Retrieved from https:\/\/www.maximizemarketresearch.com\/market-report\/global-smart-building-market-key-trends\/6854\/."},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2189204"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2991561.2991563"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2018.8650518"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3277868.3277875"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP45357.2019.8969297"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2014.6943521"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517370"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2802083.2808411"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSensT.2012.6461713"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3089351.3089355"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18072198"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2968057"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298698"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.01.083"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308143"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CCECE53047.2021.9569139"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/IAS48185.2021.9677162"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISSI.2018.8538176"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2019.05.016"},{"key":"e_1_3_1_24_2","article-title":"Smart security system for suspicious activity detection in volatile areas","author":"Chen Joy Iong Zong","year":"2020","unstructured":"Joy Iong Zong Chen. 2020. Smart security system for suspicious activity detection in volatile areas. J. Info. Technol. (2020).","journal-title":"J. Info. Technol."},{"key":"e_1_3_1_25_2","article-title":"Sensor-based activity recognition","author":"Chen Liming","year":"2012","unstructured":"Liming Chen, Jesse Hoey, Chris D. Nugent, Diane J. Cook, and Zhiwen Yu. 2012. Sensor-based activity recognition. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) (2012).","journal-title":"IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.)"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632103"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2711530"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2017.7917858"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSTCC.2019.8885985"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3191736"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.107066"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3410530.3414318"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2611779"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3311950"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.06.025"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/DEST.2012.6227924"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1109\/DEST.2013.6611339"},{"key":"e_1_3_1_38_2","article-title":"A survey on approaches of motion mode recognition using sensors","author":"Elhoushi Mostafa","year":"2016","unstructured":"Mostafa Elhoushi, Jacques Georgy, Aboelmagd Noureldin, and Michael J. Korenberg. 2016. A survey on approaches of motion mode recognition using sensors. IEEE Trans. Intell. Transport. Syst. (2016).","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267242.3267245"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC50000.2020.9219634"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/COINS49042.2020.9191417"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3081675"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3090075"},{"key":"e_1_3_1_44_2","article-title":"IoT based approach for load monitoring and activity recognition in smart homes","author":"Franco Patricia","year":"2021","unstructured":"Patricia Franco, Jose Manuel Martinez, Young-Chon Kim, and Mohamed A. Ahmed. 2021. IoT based approach for load monitoring and activity recognition in smart homes. IEEE Access (2021).","journal-title":"IEEE Access"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP46218.2019.8971134"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32785-9_7"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107676"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/PerComW.2012.6197536"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2993422.2993428"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3349624.3356765"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3218584"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.11.029"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2019.05.032"},{"key":"e_1_3_1_54_2","article-title":"Different approaches for human activity recognition: A survey","author":"Hussain Zawar","year":"2019","unstructured":"Zawar Hussain, Michael Sheng, and Wei Emma Zhang. 2019. Different approaches for human activity recognition: A survey. Retrieved from https:\/\/arXiv:1906.05074.","journal-title":"Retrieved from https:\/\/arXiv:1906.05074"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.217"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/2981548.2981554"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2968277"},{"key":"e_1_3_1_58_2","unstructured":"IEA. 2021. Buildings\u2014Topics. Retrieved from https:\/\/www.iea.org\/topics\/buildings."},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10207122"},{"key":"e_1_3_1_60_2","unstructured":"Aftab Jalia and Michael Ramage. (n.d.). The Edge Amsterdam 33. https:\/\/open.metu.edu.tr\/handle\/11511\/96369."},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/2935651.2935659"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3277593.3277633"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2019.01.023"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/WSC.2018.8632278"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2018.8581229"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/2532644"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01214-4"},{"key":"e_1_3_1_68_2","doi-asserted-by":"crossref","unstructured":"Aftab Khan James Nicholson Sebastian Mellor Daniel Jackson Karim Ladha Cassim Ladha Jon Hand Joseph Clarke Patrick Olivier and Thomas Pl\u00f6tz. 2014. Occupancy monitoring using environmental and context sensors and a hierarchical analysis framework. https:\/\/nrl.northumbria.ac.uk\/id\/eprint\/35912\/.","DOI":"10.1145\/2674061.2674080"},{"key":"e_1_3_1_69_2","article-title":"Method for long-term mapping of occupancy patterns in open-plan and single office spaces by using passive-infrared (PIR) sensors mounted below desks","author":"Khan Donya Sheikh","year":"2021","unstructured":"Donya Sheikh Khan, Jakub Kolarik, Christian Anker Hviid, and Peter Weitzmann. 2021. Method for long-term mapping of occupancy patterns in open-plan and single office spaces by using passive-infrared (PIR) sensors mounted below desks. Energy Build. (2021).","journal-title":"Energy Build."},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22207978"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3276774.3281017"},{"key":"e_1_3_1_72_2","first-page":"1","volume-title":"Proceedings of the International Symposium on Antennas and Propagation (ISAP\u201919)","author":"Kim Minseok","year":"2019","unstructured":"Minseok Kim, Takeshi Tasaki, and Satoshi Yamakawa. 2019. Millimeter-wave radio tomographic imaging technique using multipath components for indoor localization. In Proceedings of the International Symposium on Antennas and Propagation (ISAP\u201919). IEEE, 1\u20133."},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1177\/1420326X16665897"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1145\/2802083.2808400"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3123024.3124434"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2829268"},{"key":"e_1_3_1_77_2","volume-title":"Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom\u201918)","author":"Laidi Roufaida","year":"2018","unstructured":"Roufaida Laidi and Djamel Djenouri. 2018. UDEPLOY: User-driven learning for occupancy sensors DEPLOYment in smart buildings. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom\u201918). IEEE."},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0273-5"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300568"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/3006299.3006308"},{"key":"e_1_3_1_81_2","volume-title":"Proceedings of the IEEE SENSORS","author":"Li Haobo","year":"2018","unstructured":"Haobo Li, Aman Shrestha, Francesco Fioranelli, Julien Le Kernec, and Hadi Heidari. 2018. Hierarchical classification on multimodal sensing for human activity recognition and fall detection. In Proceedings of the IEEE SENSORS. IEEE."},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSENS.2017.8234179"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/IMBIOC.2019.8777855"},{"key":"e_1_3_1_84_2","article-title":"Multi-user activity recognition: Challenges and opportunities","author":"Li Qimeng","year":"2020","unstructured":"Qimeng Li, Raffaele Gravina, Ye Li, Saeed H. Alsamhi, Fangmin Sun, and Giancarlo Fortino. 2020. Multi-user activity recognition: Challenges and opportunities. Info. Fusion (2020).","journal-title":"Info. Fusion"},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994569"},{"key":"e_1_3_1_86_2","article-title":"A new modeling approach for short-term prediction of occupancy in residential buildings","author":"Li Zhaoxuan","year":"2017","unstructured":"Zhaoxuan Li and Bing Dong. 2017. A new modeling approach for short-term prediction of occupancy in residential buildings. Build. Environment (2017).","journal-title":"Build. Environment"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00752"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/EIT.2018.8500177"},{"key":"e_1_3_1_89_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448416"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2007.911671"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCNC.2015.7069473"},{"key":"e_1_3_1_92_2","article-title":"From action to activity: Sensor-based activity recognition","author":"Liu Ye","year":"2016","unstructured":"Ye Liu, Liqiang Nie, Li Liu, and David S. Rosenblum. 2016. From action to activity: Sensor-based activity recognition. Neurocomputing (2016).","journal-title":"Neurocomputing"},{"key":"e_1_3_1_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3207677.3277991"},{"key":"e_1_3_1_94_2","article-title":"Fusion of magnetic and visual sensors for indoor localization: Infrastructure-free and more effective","author":"Liu Zhenguang","year":"2016","unstructured":"Zhenguang Liu, Luming Zhang, Qi Liu, Yifang Yin, Li Cheng, and Roger Zimmermann. 2016. Fusion of magnetic and visual sensors for indoor localization: Infrastructure-free and more effective. IEEE Trans. Multimedia (2016).","journal-title":"IEEE Trans. Multimedia"},{"key":"e_1_3_1_95_2","doi-asserted-by":"publisher","DOI":"10.23919\/SpliTech49282.2020.9243754"},{"key":"e_1_3_1_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761484"},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01257-x"},{"key":"e_1_3_1_98_2","doi-asserted-by":"publisher","DOI":"10.3390\/s16060822"},{"key":"e_1_3_1_99_2","article-title":"Posture detection based on smart cushion for wheelchair users","author":"Ma Congcong","year":"2017","unstructured":"Congcong Ma, Wenfeng Li, Raffaele Gravina, and Giancarlo Fortino. 2017. Posture detection based on smart cushion for wheelchair users. Sensors (2017).","journal-title":"Sensors"},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2011.12.037"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1109\/ECTI-NCON.2018.8378302"},{"key":"e_1_3_1_102_2","doi-asserted-by":"publisher","DOI":"10.1109\/IGCC.2011.6008560"},{"key":"e_1_3_1_103_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.03.022"},{"key":"e_1_3_1_104_2","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430426"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1080\/17508975.2020.1765723"},{"key":"e_1_3_1_106_2","unstructured":"Mondher Bouazizi Tomoaki Ohtsuki and others. 2020. Detection of human activity based on hybrid deep learning model using a low-resolution infrared array sensor. IEICE Technical Report; IEICE Tech. Rep. 120 261 (2020) 99\u2013104."},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084926"},{"key":"e_1_3_1_108_2","doi-asserted-by":"publisher","DOI":"10.23919\/MVA51890.2021.9511390"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2723424"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2012.09.005"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/IAS44978.2020.9334850"},{"key":"e_1_3_1_112_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100312"},{"key":"e_1_3_1_113_2","article-title":"Fog\/edge computing-based IoT (FECIoT): Architecture, applications, and research issues","author":"Omoniwa Babatunji","year":"2018","unstructured":"Babatunji Omoniwa, Riaz Hussain, Muhammad Javed, Safdar Bouk, and Shahzad A. Malik. 2018. Fog\/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet Things J. (2018).","journal-title":"IEEE Internet Things J."},{"key":"e_1_3_1_114_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.2965559"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2014.060914.00089"},{"key":"e_1_3_1_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.08.039"},{"key":"e_1_3_1_117_2","doi-asserted-by":"publisher","DOI":"10.1109\/IOTSMS48152.2019.8939234"},{"key":"e_1_3_1_118_2","article-title":"Context aware computing for the internet of things: A survey","author":"Perera Charith","year":"2013","unstructured":"Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2013. Context aware computing for the internet of things: A survey. IEEE Commun. Surveys Tutor. (2013).","journal-title":"IEEE Commun. Surveys Tutor."},{"key":"e_1_3_1_119_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-00582-3"},{"key":"e_1_3_1_120_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1007\/1\/012162"},{"key":"e_1_3_1_121_2","doi-asserted-by":"publisher","DOI":"10.1109\/UEMCON47517.2019.8993008"},{"key":"e_1_3_1_122_2","article-title":"An indoor air quality and thermal comfort appraisal in a retrofitted university building via low-cost smart sensor","author":"Qabbal Loubna","year":"2021","unstructured":"Loubna Qabbal, Zohir Younsi, and Hassane Naji. 2021. An indoor air quality and thermal comfort appraisal in a retrofitted university building via low-cost smart sensor. Indoor Built Environ. (2021).","journal-title":"Indoor Built Environ."},{"key":"e_1_3_1_123_2","doi-asserted-by":"publisher","DOI":"10.1177\/1420326X211015717"},{"key":"e_1_3_1_124_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2926642"},{"key":"e_1_3_1_125_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCS.2017.29"},{"key":"e_1_3_1_126_2","volume-title":"Proceedings of the 4th International Conference of Computer and Informatics Engineering (IC2IE\u201921)","author":"Rahman Haolia","year":"2021","unstructured":"Haolia Rahman, Abdul Azis Abdillah, Asep Apriana, Devi Handaya, and Idrus Assagaf. 2021. Indoor \\(\\text{CO}_2\\) level-based occupancy estimation at low-scale occupant using statistical learning method. In Proceedings of the 4th International Conference of Computer and Informatics Engineering (IC2IE\u201921). IEEE."},{"key":"e_1_3_1_127_2","doi-asserted-by":"publisher","DOI":"10.1108\/SR-02-2020-0027"},{"key":"e_1_3_1_128_2","doi-asserted-by":"publisher","DOI":"10.1109\/INDICON47234.2019.9030326"},{"key":"e_1_3_1_129_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_3_1_130_2","doi-asserted-by":"publisher","DOI":"10.1109\/SIBGRAPI-T.2019.00010"},{"key":"e_1_3_1_131_2","doi-asserted-by":"publisher","DOI":"10.1109\/INSS.2010.5573462"},{"key":"e_1_3_1_132_2","article-title":"Occupancy sensing in buildings: A review of data analytics approaches","volume":"188","author":"Saha Homagni","year":"2019","unstructured":"Homagni Saha, Anthony R. Florita, Gregor P. Henze, and Soumik Sarkar. 2019. Occupancy sensing in buildings: A review of data analytics approaches. Energy Build. 188 (2019).","journal-title":"Energy Build."},{"key":"e_1_3_1_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT45199.2020.9087681"},{"key":"e_1_3_1_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCP.2016.7737171"},{"key":"e_1_3_1_135_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.06.003"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2017.8255036"},{"key":"e_1_3_1_137_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18471-z"},{"key":"e_1_3_1_138_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.03.069"},{"key":"e_1_3_1_139_2","doi-asserted-by":"publisher","DOI":"10.1109\/HealthCom.2016.7749439"},{"key":"e_1_3_1_140_2","doi-asserted-by":"publisher","DOI":"10.1145\/3341162.3344831"},{"key":"e_1_3_1_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/3349624.3356768"},{"key":"e_1_3_1_142_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20195497"},{"key":"e_1_3_1_143_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169976"},{"key":"e_1_3_1_144_2","article-title":"Indoor air-quality data-monitoring system: Long-term monitoring benefits","author":"Sun Shengjing","year":"2019","unstructured":"Shengjing Sun, Xiaochen Zheng, Javier Villalba-D\u00edez, and Joaqu\u00edn Ordieres-Mer\u00e9. 2019. Indoor air-quality data-monitoring system: Long-term monitoring benefits. Sensors (2019).","journal-title":"Sensors"},{"key":"e_1_3_1_145_2","article-title":"A change in granularity: Measure space utilization through smart technologies","author":"Tagliaro Chiara","year":"2020","unstructured":"Chiara Tagliaro, Yaoyi Zhou, and Ying Hua. 2020. A change in granularity: Measure space utilization through smart technologies. Facilities (2020).","journal-title":"Facilities"},{"key":"e_1_3_1_146_2","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300766"},{"key":"e_1_3_1_147_2","article-title":"Coherence constrained graph LSTM for group activity recognition","author":"Tang Jinhui","year":"2019","unstructured":"Jinhui Tang, Xiangbo Shu, Rui Yan, and Liyan Zhang. 2019. Coherence constrained graph LSTM for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2019).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_3_1_148_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2914577"},{"key":"e_1_3_1_149_2","article-title":"A survey of human-sensing: Methods for detecting presence, count, location, track, and identity","author":"Teixeira Thiago","year":"2010","unstructured":"Thiago Teixeira, Gershon Dublon, and Andreas Savvides. 2010. A survey of human-sensing: Methods for detecting presence, count, location, track, and identity. Comput. Surveys (2010).","journal-title":"Comput. Surveys"},{"key":"e_1_3_1_150_2","volume-title":"Building Simulation","author":"Tekler Zeynep Duygu","year":"2022","unstructured":"Zeynep Duygu Tekler, Eikichi Ono, Yuzhen Peng, Sicheng Zhan, Bertrand Lasternas, and Adrian Chong. 2022. ROBOD, room-level occupancy and building operation dataset. In Building Simulation. Springer."},{"key":"e_1_3_1_151_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMSNETS.2012.6151374"},{"key":"e_1_3_1_152_2","doi-asserted-by":"publisher","DOI":"10.1145\/3089341.3089345"},{"key":"e_1_3_1_153_2","doi-asserted-by":"publisher","DOI":"10.1109\/INCISCOS49368.2019.00042"},{"key":"e_1_3_1_154_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2530824"},{"key":"e_1_3_1_155_2","article-title":"Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network","author":"Uddin Md Zia","year":"2018","unstructured":"Md Zia Uddin and Mohammad Mehedi Hassan. 2018. Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network. IEEE Sensors J. (2018).","journal-title":"IEEE Sensors J."},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1109\/IRI.2018.00022"},{"key":"e_1_3_1_157_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2922409"},{"key":"e_1_3_1_158_2","doi-asserted-by":"publisher","DOI":"10.1109\/EuCAP.2014.6902229"},{"key":"e_1_3_1_159_2","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT.2019.8767217"},{"key":"e_1_3_1_160_2","article-title":"Demonstration of a micro-services based multi-purpose sensor platform for supporting ambient assisted living systems","author":"Wagner Stefan","year":"2019","unstructured":"Stefan Wagner, Esben Hunnerup, and Jorge Miranda. 2019. Demonstration of a micro-services based multi-purpose sensor platform for supporting ambient assisted living systems. Sens. Technol. Pervas. Healthcare: Eval. Design Senior Cit. Cont. Care (2019).","journal-title":"Sens. Technol. Pervas. Healthcare: Eval. Design Senior Cit. Cont. Care"},{"key":"e_1_3_1_161_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.02.010"},{"key":"e_1_3_1_162_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2018.07.007"},{"key":"e_1_3_1_163_2","article-title":"Generalizing from a few examples: A survey on few-shot learning","author":"Wang Yaqing","year":"2020","unstructured":"Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2020. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surveys (2020).","journal-title":"ACM Comput. Surveys"},{"key":"e_1_3_1_164_2","article-title":"Inferring occupant counts from Wi-Fi data in buildings through machine learning","author":"Wang Zhe","year":"2019","unstructured":"Zhe Wang, Tianzhen Hong, Mary Ann Piette, and Marco Pritoni. 2019. Inferring occupant counts from Wi-Fi data in buildings through machine learning. Build. Environ. (2019).","journal-title":"Build. Environ."},{"key":"e_1_3_1_165_2","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971429"},{"key":"e_1_3_1_166_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSE.2009.199"},{"key":"e_1_3_1_167_2","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2971424"},{"key":"e_1_3_1_168_2","article-title":"Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors","author":"Xi Xugang","year":"2017","unstructured":"Xugang Xi, Minyan Tang, Seyed M. Miran, and Zhizeng Luo. 2017. Evaluation of feature extraction and recognition for activity monitoring and fall detection based on wearable sEMG sensors. Sensors (2017).","journal-title":"Sensors"},{"key":"e_1_3_1_169_2","doi-asserted-by":"publisher","DOI":"10.1145\/3333581.3333582"},{"key":"e_1_3_1_170_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCA.2017.8003185"},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","DOI":"10.1145\/2821650.2821668"},{"key":"e_1_3_1_172_2","volume-title":"Proceedings of the Symposium on Simulation for Architecture and Urban Design","author":"Yang Zheng","year":"2012","unstructured":"Zheng Yang, Nan Li, Burcin Becerik-Gerber, and Michael Orosz. 2012. A multi-sensor based occupancy estimation model for supporting demand-driven HVAC operations. In Proceedings of the Symposium on Simulation for Architecture and Urban Design. Citeseer."},{"key":"e_1_3_1_173_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513388"},{"key":"e_1_3_1_174_2","article-title":"Device-free human activity recognition with low-resolution infrared array sensor using long short-term memory neural network","author":"Yin Cunyi","year":"2021","unstructured":"Cunyi Yin, Jing Chen, Xiren Miao, Hao Jiang, and Deying Chen. 2021. Device-free human activity recognition with low-resolution infrared array sensor using long short-term memory neural network. Sensors (2021).","journal-title":"Sensors"},{"key":"e_1_3_1_175_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCWD.2015.7231023"},{"key":"e_1_3_1_176_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2911558"},{"key":"e_1_3_1_177_2","article-title":"Thermal comfort modeling for smart buildings: A fine-grained deep learning approach","author":"Zhang Wei","year":"2018","unstructured":"Wei Zhang, Weizheng Hu, and Yonggang Wen. 2018. Thermal comfort modeling for smart buildings: A fine-grained deep learning approach. IEEE Internet Things J. (2018).","journal-title":"IEEE Internet Things J."},{"key":"e_1_3_1_178_2","doi-asserted-by":"publisher","DOI":"10.1145\/3359427.3361911"},{"key":"e_1_3_1_179_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2016.05.005"},{"key":"e_1_3_1_180_2","article-title":"Fusion of nonintrusive environmental sensors for occupancy detection in smart homes","author":"Zimmermann Lars","year":"2017","unstructured":"Lars Zimmermann, Robert Weigel, and Georg Fischer. 2017. Fusion of nonintrusive environmental sensors for occupancy detection in smart homes. IEEE Internet Things J. (2017).","journal-title":"IEEE Internet Things J."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3596600","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3596600","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:48:01Z","timestamp":1750178881000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3596600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,13]]},"references-count":179,"journal-issue":{"issue":"13s","published-print":{"date-parts":[[2023,12,31]]}},"alternative-id":["10.1145\/3596600"],"URL":"https:\/\/doi.org\/10.1145\/3596600","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,13]]},"assertion":[{"value":"2022-09-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-02","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}