{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T19:10:14Z","timestamp":1771787414043,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,26]],"date-time":"2019-01-26T00:00:00Z","timestamp":1548460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor\/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor\/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.<\/jats:p>","DOI":"10.3390\/s19030511","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T03:40:55Z","timestamp":1548733255000},"page":"511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Mobile User Indoor-Outdoor Detection through Physical Daily Activities"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-2020","authenticated-orcid":false,"given":"Aghil","family":"Esmaeili Kelishomi","sequence":"first","affiliation":[{"name":"MOE Key Laboratory for Intelligent and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2976-5229","authenticated-orcid":false,"given":"A.H.S.","family":"Garmabaki","sequence":"additional","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}]},{"given":"Mahdi","family":"Bahaghighat","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Raja University, 34148 Qazvin, Iran"}]},{"given":"Jianmin","family":"Dong","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory for Intelligent and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"key":"ref_1","unstructured":"(2018, November 07). Smartphone Users Worldwide 2014\u20132020. Available online: https:\/\/www.statista.com\/statistics\/330695\/number-of-smartphone-users-worldwide\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/MCOM.2010.5560598","article-title":"A survey of mobile phone sensing","volume":"48","author":"Lane","year":"2010","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, J., Tse, N.C.F., Poon, T.Y., and Chan, J.Y.C. (2018). A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing. Sensors, 18.","DOI":"10.3390\/s18113591"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.2753\/MIS0742-1222260305","article-title":"The role of push-pull technology in privacy calculus: The case of location-based services","volume":"26","author":"Xu","year":"2009","journal-title":"Manag. Inf. Syst."},{"key":"ref_5","unstructured":"van den Berg, J., K\u00f6bben, B., van der Drift, S., and Wismans, L. (2018, January 15\u201317). Towards a Dynamic Isochrone Map: Adding Spatiotemporal Traffic and Population Data. Proceedings of the LBS 2018: 14th International Conference on Location Based Services, Zurich, Switzerland."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chin, W.H., Fan, Z., and Haines, R.J. (2014). Emerging technologies and research challenges for 5G wireless networks. 21, 106\u2013112.","DOI":"10.1109\/MWC.2014.6812298"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, H., Wu, M., Li, P., Zhu, F., and Wang, R. (2018). An RFID Indoor Positioning Algorithm Based on Support Vector Regression. Sensors, 18.","DOI":"10.3390\/s18051504"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/MCOM.2015.7060497","article-title":"WiFi-based indoor positioning","volume":"53","author":"Yang","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rana, S., Prieto, J., Dey, M., Dudley, S., and Corchado, J. (2018). A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building. Sensors, 18.","DOI":"10.3390\/s18113766"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/ACCESS.2015.2441694","article-title":"Energy-efficient indoor localization of smart hand-held devices using Bluetooth","volume":"3","author":"Gu","year":"2015","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kang, J., Seo, J., and Won, Y. (2018). Ephemeral ID Beacon-Based Improved Indoor Positioning System. Symmetry, 10.","DOI":"10.3390\/sym10110622"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Song, J., Jeong, H., Hur, S., and Park, Y. (2014, January 27\u201330). Improved indoor position estimation algorithm based on geo-magnetism intensity. Proceedings of the 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea.","DOI":"10.1109\/IPIN.2014.7275555"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chintalapudi, K., Iyer, A.P., and Padmanabhan, V.N. (2010, January 20\u201324). Indoor localization without the pain. Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, Chicago, IL, USA.","DOI":"10.1145\/1859995.1860016"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/JIOT.2016.2553100","article-title":"An android-based mechanism for energy efficient localization depending on indoor\/outdoor context","volume":"4","author":"Capurso","year":"2017","journal-title":"IEEE Internet Thing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Radu, V., Katsikouli, P., Sarkar, R., and Marina, M.K. (2014, January 3\u20136). A semi-supervised learning approach for robust indoor-outdoor detection with smartphones. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, Memphis, TN, USA.","DOI":"10.1145\/2668332.2668347"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dhondge, K., Choi, B.Y., Song, S., and Park, H. (2014, January 4\u20137). Optical wireless authentication for smart devices using an onboard ambient light sensor. Proceedings of the 2014 23rd International Conference on Computer Communication and Networks (ICCCN), Shanghai, China.","DOI":"10.1109\/ICCCN.2014.6911803"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Spreitzer, R. (2014, January 3\u20137). Pin skimming: Exploiting the ambient-light sensor in mobile devices. Proceedings of the 4th ACM Workshop on Security and Privacy in Smartphones & Mobile Devices, Scottsdale, AZ, USA.","DOI":"10.1145\/2666620.2666622"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2632149","article-title":"IODetector","volume":"11","author":"Li","year":"2014","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., and Park, Y. (2018). MagIO: Magnetic Field Strength Based Indoor- Outdoor Detection with a Commercial Smartphone. Micromachines, 9.","DOI":"10.3390\/mi9100534"},{"key":"ref_20","unstructured":"Krumm, J., and Hariharan, R. (2004, January 23\u201324). Tempio: Inside\/outside classification with temperature. Proceedings of the Second International Workshop on Man-Machine Symbiotic Systems, Kyoto, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.icte.2016.02.001","article-title":"Sound based indoor and outdoor environment detection for seamless positioning handover","volume":"1","author":"Sung","year":"2015","journal-title":"ICT Express"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, W., Chang, Q., Li, Q., Shi, Z., and Chen, W. (2016). Indoor-outdoor detection using a smart phone sensor. Sensors, 16.","DOI":"10.3390\/s16101563"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1109\/JSEN.2016.2640358","article-title":"Detecting indoor\/outdoor places using WiFi signals and AdaBoost","volume":"17","author":"Canovas","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3684","DOI":"10.1109\/JSEN.2018.2810193","article-title":"SenseIO: Realistic Ubiquitous Indoor Outdoor Detection System Using Smartphones","volume":"18","author":"Ali","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, Y.-W., and Lin, C.-Y. (2018). An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network. Sensors, 18.","DOI":"10.3390\/s18051637"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, Q., Yang, X., and Deng, L. (2018). An IBeacon-Based Location System for Smart Home Control. Sensors, 18.","DOI":"10.3390\/s18061897"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1080\/10803548.2015.1029301","article-title":"Age and sex differences in ranges of motion and motion patterns","volume":"21","author":"Hwang","year":"2015","journal-title":"Int. J. Occup. Saf. Ergon."},{"key":"ref_28","unstructured":"Walter, D.J., Groves, P.D., Mason, R.J., Harrison, J., Woodward, J., and Wright, P. (2013, January 16\u201320). Novel environmental features for robust multisensor navigation. Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS), Nashville, TN, USA."},{"key":"ref_29","unstructured":"Groves, P.D., Martin, H., Voutsis, K., Walter, D., and Wang, L. (2013, January 16\u201320). Context detection, categorization and connectivity for advanced adaptive integrated navigation. Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS), Nashville, TN, USA."},{"key":"ref_30","unstructured":"Ravindranath, L., Newport, C., Balakrishnan, H., and Madden, S. (April, January 30). Improving wireless network performance using sensor hints. Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, Boston, MA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.pmcj.2010.01.001","article-title":"Providing user context for mobile and social networking applications","volume":"6","author":"Santos","year":"2010","journal-title":"Pervasive Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, M., Pathak, P.H., and Mohapatra, P. (2015, January 7\u201311). Monitoring building door events using barometer sensor in smartphones. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2804257"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ichino, H., Kaji, K., Sakurada, K., Hiroi, K., and Kawaguchi, N. (2016, January 12\u201316). HASC-PAC2016: Large scale human pedestrian activity corpus and its baseline recognition. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, Heidelberg, Germany.","DOI":"10.1145\/2968219.2968277"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pires, I., Garcia, N.M., Pombo, N., Fl\u00f3rez-Revuelta, F., and Spinsante, S. (2018). Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors, 18.","DOI":"10.20944\/preprints201801.0068.v1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/BF02162161","article-title":"Smoothing by spline functions","volume":"10","author":"Reinsch","year":"1967","journal-title":"Numer. Math."},{"key":"ref_36","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1109\/ACCESS.2017.2676168","article-title":"Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Damasevicius, R., Narayanan, M.R., Mathie, M., Lovell, N.H., and Celler, B.G. (2016). Human Activity Recognition in AAL Environments Using Random Projections. Comput. Math. Methods Med., 4073584.","DOI":"10.1155\/2016\/4073584"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Garcia, N.M., Pombo, N., and Fl\u00f3rez-Revuelta, F. (2016). From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices. Sensors, 16.","DOI":"10.3390\/s16020184"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ehatisham-Ul-Haq, M., Azam, M.A., Loo, J., Shuang, K., Islam, S., Naeem, U., and Amin, M. (2017). Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing. Sensors, 17.","DOI":"10.3390\/s17092043"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"pp","DOI":"10.1109\/TNNLS.2016.2551724","article-title":"Feature Selection Based on Structured Sparsity: A Comprehensive Study","volume":"28","author":"Gui","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1007515423169","article-title":"An empirical comparison of voting classification algorithms: Bagging, boosting, and variants","volume":"36","author":"Bauer","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kadavi, P., Lee, C.-W., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_48","first-page":"23","article-title":"Text dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian language","volume":"51","author":"Bahaghighat","year":"2012","journal-title":"Int. J. Comput. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/511\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:28:49Z","timestamp":1760185729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/511"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,26]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19030511"],"URL":"https:\/\/doi.org\/10.3390\/s19030511","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,26]]}}}