{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T14:17:25Z","timestamp":1726064245772},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,15]],"date-time":"2018-11-15T00:00:00Z","timestamp":1542240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation for Science and Technology and by the European Regional Development Fund","award":["P2020 SAICTPAC\/0011\/2015"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user\u2019s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.<\/jats:p>","DOI":"10.3390\/s18113953","type":"journal-article","created":{"date-parts":[[2018,11,15]],"date-time":"2018-11-15T16:32:47Z","timestamp":1542299567000},"page":"3953","source":"Crossref","is-referenced-by-count":42,"title":["A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8665-4667","authenticated-orcid":false,"given":"Bruno","family":"Abade","sequence":"first","affiliation":[{"name":"Department of Informatics Engineering, University of Coimbra, Polo II-Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0167-511X","authenticated-orcid":false,"given":"David","family":"Perez Abreu","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering, University of Coimbra, Polo II-Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6760-4675","authenticated-orcid":false,"given":"Marilia","family":"Curado","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering, University of Coimbra, Polo II-Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The Internet of Things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.pmcj.2006.12.001","article-title":"How smart are our environments? An updated look at the state of the art","volume":"3","author":"Cook","year":"2007","journal-title":"Pervasive Mob. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5369","DOI":"10.1109\/IECON.2014.7049320","article-title":"Environmental sensing by wearable device for indoor activity and location estimation","volume":"Volume 1","author":"Jin","year":"2014","journal-title":"Proceedings of the IECON 2014\u201440th Annual Conference of the IEEE Industrial Electronics Society"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Imanishi, T., Tennekoon, R., Palensky, P., and Nishi, H. (2015, January 9\u201312). Enhanced building thermal model by using CO2 based occupancy data. Proceedings of the IECON 2015\u201441st Annual Conference of the IEEE Industrial Electronics Society, Yokohama, Japan.","DOI":"10.1109\/IECON.2015.7392578"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.pmcj.2009.04.001","article-title":"Ambient intelligence: Technologies, applications, and opportunities","volume":"5","author":"Cook","year":"2009","journal-title":"Pervasive Mob. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TCE.2006.273150","article-title":"A pyroelectric infrared sensor-based indoor location-aware system for the smart home","volume":"52","author":"Lee","year":"2006","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.enbuild.2015.02.028","article-title":"Occupancy measurement in commercial office buildings for demand-driven control applications\u2014A survey and detection system evaluation","volume":"93","author":"Labeodan","year":"2015","journal-title":"Energy Build."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hahnel, D., Burgard, W., Fox, D., Fishkin, K., and Philipose, M. (May, January 26). Mapping and localization with RFID technology. Proceedings of the ICRA \u201904 2004 IEEE International Conference on Robotics and Automation, New Orleans, LA, USA.","DOI":"10.1109\/ROBOT.2004.1307283"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.aei.2011.02.004","article-title":"Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment","volume":"25","author":"Li","year":"2011","journal-title":"Adv. Eng. Inf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.autcon.2012.02.013","article-title":"Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations","volume":"24","author":"Li","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huh, J.H., and Seo, K. (2017). An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems. Sensors, 17.","DOI":"10.3390\/s17122917"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Filippoupolitis, A., Oliff, W., and Loukas, G. (2016, January 14\u201316). Bluetooth Low Energy Based Occupancy Detection for Emergency Management. Proceedings of the 2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), Granada, Spain.","DOI":"10.1109\/IUCC-CSS.2016.013"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1109\/JSAC.2015.2430272","article-title":"Occupancy Estimation Using Only WiFi Power Measurements","volume":"33","author":"Depatla","year":"2015","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Balaji, B., Xu, J., Nwokafor, A., Gupta, R., and Agarwal, Y. (2013, January 11\u201315). Sentinel: Occupancy Based HVAC Actuation Using Existing WiFi Infrastructure Within Commercial Buildings. Proceedings of the SenSys \u201913 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy.","DOI":"10.1145\/2517351.2517370"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TPAMI.2007.1174","article-title":"Multicamera People Tracking with a Probabilistic Occupancy Map","volume":"30","author":"Fleuret","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10851-010-0258-7","article-title":"Sparsity Driven People Localization with a Heterogeneous Network of Cameras","volume":"41","author":"Alahi","year":"2011","journal-title":"J. Math. Imaging Vis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and Shenoy, P. (2013, January 11\u201315). Non-Intrusive Occupancy Monitoring Using Smart Meters. Proceedings of the 5th BuildSys\u201913 ACM Workshop on Embedded Systems for Energy-Efficient Buildings, Roma, Italy.","DOI":"10.1145\/2528282.2528294"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1109\/TMC.2017.2684806","article-title":"Virtual Occupancy Sensing: Using Smart Meters to Indicate Your Presence","volume":"16","author":"Jin","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.enbuild.2010.01.016","article-title":"An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network","volume":"42","author":"Dong","year":"2010","journal-title":"Energy Build."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.enbuild.2014.09.002","article-title":"CO2 sensors for occupancy estimations: Potential in building automation applications","volume":"84","author":"Gruber","year":"2014","journal-title":"Energy Build."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.buildenv.2016.06.039","article-title":"Development of an occupancy prediction model using indoor environmental data based on machine learning techniques","volume":"107","author":"Ryu","year":"2016","journal-title":"Build. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.enbuild.2015.11.071","article-title":"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models","volume":"112","author":"Candanedo","year":"2016","journal-title":"Energy Build."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.enbuild.2016.07.026","article-title":"Estimating occupancy in heterogeneous sensor environment","volume":"129","author":"Amayri","year":"2016","journal-title":"Energy Build."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1109\/TSMCC.2007.905750","article-title":"Survey of Wireless Indoor Positioning Techniques and Systems","volume":"37","author":"Liu","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s12243-010-0220-0","article-title":"Scene analysis indoor positioning enhancements","volume":"66","author":"Papapostolou","year":"2011","journal-title":"Ann. Telecommun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Parsons, K. (2014). Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance, CRC Press. [3rd ed.].","DOI":"10.1201\/b16750"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Paradiso, J. (2006, January 24\u201328). Systems for Human-powered Mobile Computing. Proceedings of the DAC \u201906, 43rd Annual Design Automation Conference, San Francisco, CA, USA.","DOI":"10.1145\/1146909.1147074"},{"key":"ref_28","unstructured":"AINSI\/ASHRAE (2013). AINSI\/ASHRAE Standard 62.1\u20142013 Ventilation for Acceptable Indoor Air Quality, ASHRAE. Standard."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/S1001-0742(11)60812-7","article-title":"Measurement of air exchange rates in different indoor environments using continuous CO2 sensors","volume":"24","author":"You","year":"2012","journal-title":"J. Environ. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1289\/ehp.117-a20","article-title":"Missing the dark: Health effects of light pollution","volume":"117","author":"Chepesiuk","year":"2009","journal-title":"Environ. Health Perspect."},{"key":"ref_31","unstructured":"Jensen, K., Arens, E., and Zagreus, L. (2005, January 4\u20139). Acoustical Quality in Office Workstations, as Assessed by Occupant Surveys. Proceedings of the 10th International Conference on Indoor Air Quality and Climate (Indoor Air 2005), Beijing, China."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Clarke, B., Fokoue, E., and Zhang, H.H. (2009). Principles and Theory for Data Mining and Machine Learning, Springer Science & Business Media. [1st ed.].","DOI":"10.1007\/978-0-387-98135-2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Perez, D., Astor, M., Abreu, D.P., and Scalise, E. (2017, January 4\u20138). Intrusion detection in computer networks using hybrid machine learning techniques. Proceedings of the 2017 XLIII Latin American Computer Conference (CLEI), Cordoba, Argentina.","DOI":"10.1109\/CLEI.2017.8226392"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A Few Useful Things to Know About Machine Learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Abade, B. (2018). Abade002\/A-non-intrusive-approach-for-indoor-occupancy-detection-in-Smart-Environments: Second Release!. Abade002.","DOI":"10.3390\/s18113953"},{"key":"ref_36","first-page":"37","article-title":"Evaluation: From precision, recall and fmeasure to roc, informedness, markedness and correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_37","unstructured":"Bishop, C. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics), Springer. [1st ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","article-title":"Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation","volume":"32","author":"Rodriguez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: an overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","article-title":"Machine Learning in Automated Text Categorization","volume":"34","author":"Sebastiani","year":"2002","journal-title":"ACM Comput. Surv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T16:36:41Z","timestamp":1718296601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,15]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113953"],"URL":"https:\/\/doi.org\/10.3390\/s18113953","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,15]]}}}