{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T12:11:37Z","timestamp":1790770297362,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"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>One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone\u2019s accelerometers.<\/jats:p>","DOI":"10.3390\/s20010214","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T03:28:53Z","timestamp":1578022133000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Smartphone Location Recognition: A Deep Learning-Based Approach"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7846-0654","authenticated-orcid":false,"given":"Itzik","family":"Klein","sequence":"first","affiliation":[{"name":"Huawei, Tel-Aviv Research Center and Department of Marine Technologies, University of Haifa, Haifa 3498838, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Titterton, D.H., and Weston, J.L. (2004). Strapdown Inertial Navigation Technology, The Institution of Electrical Engineers. [2nd ed.].","DOI":"10.1049\/PBRA017E"},{"key":"ref_2","unstructured":"Farrell, J.A. (2008). Aided Navigation GPS with High Rate Sensors, McGraw-Hill."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCG.2005.140","article-title":"Pedestrian tracking with shoe-mounted inertial sensors","volume":"6","author":"Foxlin","year":"2005","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1109\/TBME.2010.2060723","article-title":"Zero-velocity detection: An algorithm evaluation","volume":"57","author":"Skog","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","unstructured":"Beauregard, S., and Haas, H. (2006, January 16). Pedestrian Dead Reckoning: A Basis for Personal Positioning. Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, Hannover, Germany."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Manos, A., Klein, I., and Hazan, T. (2018, January 24\u201327). Gravity Direction Estimation and Heading Determination for Pedestrian Navigation. Proceedings of the Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533689"},{"key":"ref_7","unstructured":"Weinberg, H. (2002). Using the ADXL202 in Pedometer and Personal Navigation Applications, Analog Devices. Appl. Note 602."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7577","DOI":"10.1109\/JSEN.2018.2861395","article-title":"Pedestrian dead reckoning with smartphone mode recognition","volume":"18","author":"Klein","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_9","first-page":"48","article-title":"Human activity recognition and pattern discovery","volume":"31","author":"Kim","year":"2009","journal-title":"IEEE Pervasive Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1089\/tmj.2011.0245","article-title":"Smartphone-based mobile health monitoring","volume":"18","author":"Lee","year":"2012","journal-title":"Telemed. e-Health"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s00779-014-0813-0","article-title":"Smart homes and their users: A systematic analysis and key challenges","volume":"19","author":"Wilson","year":"2015","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TITB.2007.899496","article-title":"Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions","volume":"12","author":"Ermes","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_13","unstructured":"Qian, L., Ma, J., Yin, G.R., Liu, P., and Pei, P. (2013, January 28\u201331). An Improved Indoor Localization Method Using Smartphone Inertial Sensors. Proceedings of the Indoor Positioning Indoor Navigation (IPIN), Montbeliard-Belfort, France."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1002\/navi.120","article-title":"Online motion mode recognition for portable navigation using low-cost sensors","volume":"62","author":"Elhoushi","year":"2015","journal-title":"Navig. J. Inst. Navig."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1108\/SR-11-2017-0245","article-title":"An overview of human activity recognition based on smartphone","volume":"39","author":"Yuan","year":"2019","journal-title":"Sens. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/TITS.2016.2617200","article-title":"A survey on approaches of motion mode recognition using sensors","volume":"18","author":"Elhoushi","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1109\/JSEN.2017.2692263","article-title":"SmartMTra: Robust Indoor Trajectory Tracing Using Smartphones","volume":"17","author":"Zhang","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.3390\/s130201539","article-title":"Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users","volume":"13","author":"Susi","year":"2013","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/JSEN.2015.2510364","article-title":"A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones","volume":"16","author":"Tian","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, R., and Wang, B. (2016). PACP: A position-independent activity recognition method using smartphone sensors. Information, 7.","DOI":"10.3390\/info7040072"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/JSEN.2016.2635691","article-title":"UMOISP: Usage mode and orientation invariant smartphone pedometer","volume":"17","author":"Kumar","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Klein, I., Solaz, Y., and Ohayon, G. (2018). Smartphone Motion Mode Recognition. Proceedings, 2.","DOI":"10.1109\/ICSEE.2018.8646011"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Klein, I., Solaz, Y., and Alaluf, R. (2018, January 12\u201314). Robust Smartphone Mode Recognition. Proceedings of the 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE), Eilat, Israel.","DOI":"10.1109\/ICSEE.2018.8646011"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Q., Ye, L., Luo, H., Men, A., Zhao, F., and Ou, C. (2019). Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition. Remote Sens., 11.","DOI":"10.3390\/rs11091140"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1109\/JSEN.2019.2926124","article-title":"An Experimental Heuristic Approach to Multi-Pose Pedestrian Dead Reckoning without Using Magnetometers for Indoor Localization","volume":"19","author":"Lee","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., and Roggen, D. (2016). Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_28","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press."},{"key":"ref_29","unstructured":"(2019, November 15). Keras: The Python Deep Learning library. Available online: https:\/\/keras.io."},{"key":"ref_30","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."},{"key":"ref_31","unstructured":"Hinton, G., Srivastava, N., and Swersky, K. (2019, December 29). Lecture 6\u2014A Separate, Adaptive Learning Rate for Each Connection. Available online: https:\/\/www.cs.toronto.edu\/~tijmen\/csc321\/slides\/lecture_slides_lec6.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yan, H., Shan, Q., and Furukawa, Y. (2018, January 8\u201314). RIDI: Robust IMU double integration. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_38"},{"key":"ref_33","unstructured":"Chen, C., Zhao, P., Lu, C.X., Wang, W., Markham, A., and Trigoni, N. (2018). OxIOD: The Dataset for Deep Inertial Odometry. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M., Clegg, R.G., Cavallaro, A., and Haddadi, H. (2019, January 15\u201318). Mobile Sensor Data Anonymization. Proceedings of the International Conference on Internet of Things Design; Implementation, Montreal, QC, Canada.","DOI":"10.1145\/3302505.3310068"},{"key":"ref_35","unstructured":"Kwapisz, J.R., Weiss, G.M., and Moore, S.A. (2010, January 25). Activity Recognition using Cell Phone Accelerometers. Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington, DC, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201318). On-body localization of wearable devices: An investigation of position-aware activity recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.3390\/s140610146","article-title":"Fusion of smartphone motion sensors for physical activity recognition","volume":"14","author":"Shoaib","year":"2014","journal-title":"Sensors"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:46:55Z","timestamp":1760190415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,30]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010214"],"URL":"https:\/\/doi.org\/10.3390\/s20010214","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,30]]}}}