{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:53:13Z","timestamp":1779889993575,"version":"3.53.1"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["P0020536"],"award-info":[{"award-number":["P0020536"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology (KIAT)","doi-asserted-by":"publisher","award":["P0020536"],"award-info":[{"award-number":["P0020536"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Indoor localization is an important technology for providing various location-based services to smartphones. Among the various indoor localization technologies, pedestrian dead reckoning using inertial measurement units is a simple and highly practical solution for indoor localization. In this study, we propose a smartphone-based indoor localization system using pedestrian dead reckoning. To create a deep learning model for estimating the moving speed, accelerometer data and GPS values were used as input data and data labels, respectively. This is a practical solution compared with conventional indoor localization mechanisms using deep learning. We improved the positioning accuracy via data preprocessing, data augmentation, deep learning modeling, and correction of heading direction. In a horseshoe-shaped indoor building of 240 m in length, the experimental results show a distance error of approximately 3 to 5 m.<\/jats:p>","DOI":"10.3390\/s22186764","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Practical and Accurate Indoor Localization System Using Deep Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4852-0657","authenticated-orcid":false,"given":"Jeonghyeon","family":"Yoon","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3512-5407","authenticated-orcid":false,"given":"Seungku","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Huang, H., and Gartner, G. (2018). Current Trends and Challenges in Location-Based Services. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060199"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3390\/fi13080210","article-title":"Survey of localization for internet of things nodes: Approaches, challenges and open issues","volume":"13","author":"Ghoprade","year":"2021","journal-title":"Future Internet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s11277-021-08209-5","article-title":"A Review of indoor localization techniques and wireless technologies","volume":"119","author":"Obeidat","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1109\/TWC.2009.080415","article-title":"UDP identification and error mitigation in ToA-based indoor localization systems using neural network architecture","volume":"8","author":"Heidari","year":"2009","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/LMWC.2005.855392","article-title":"Using enhanced-TDOA measurement for indoor positioning","volume":"15","author":"Bocquet","year":"2005","journal-title":"IEEE Microw. Wirel. Compon. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Diao, S., Luo, Q., Wang, C., and Ding, J. (2021, January 12\u201314). Enhancing Trilateration Localization by Adaptive Selecting Distances. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9391116"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1109\/TIE.2015.2509917","article-title":"Gradient-based fingerprinting for indoor localization and tracking","volume":"63","author":"Shu","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/JIOT.2017.2782479","article-title":"CSI amplitude fingerprinting-based NB-IoT indoor localization","volume":"5","author":"Song","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10639","DOI":"10.1109\/JIOT.2019.2940368","article-title":"Recurrent neural networks for accurate RSSI indoor localization","volume":"6","author":"Hoang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, H., Hartmann, Y., and Schultz, T. (2021, January 23\u201327). Motion Units: Generalized Sequence Modeling of Human Activities for Sensor-Based Activity Recognition. Proceedings of the 2021 29th European Signal Processing Conference (EUSIPCO), Dublin, Ireland.","DOI":"10.23919\/EUSIPCO54536.2021.9616298"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/LCOMM.2015.2496940","article-title":"A hybrid WiFi\/magnetic matching\/PDR approach for indoor navigation with smartphone sensors","volume":"20","author":"Li","year":"2015","journal-title":"IEEE Commun. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24595","DOI":"10.3390\/s150924595","article-title":"Integrated WiFi\/PDR\/Smartphone using an unscented kalman filter algorithm for 3D indoor localization","volume":"15","author":"Chen","year":"2015","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, J., Liu, C., Zhang, L., and Li, Z. (2016). Integrated WiFi\/PDR\/smartphone using an adaptive system noise extended Kalman filter algorithm for indoor localization. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5020008"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"13608","DOI":"10.1109\/JIOT.2021.3067515","article-title":"Indoor localization fusing wifi with smartphone inertial sensors using lstm networks","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_15","unstructured":"Godha, S., Lachapelle, G., and Cannon, M.E. (2006, January 26\u201329). Integrated GPS\/INS system for pedestrian navigation in a signal degraded environment. Proceedings of the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2006), Fort Worth, TX, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Constandache, I., Choudhury, R.R., and Rhee, I. (2010, January 14\u201319). Towards mobile phone localization without war-driving. Proceedings of the 2010 Proceedings IEEE INFOCOM, San Diego, CA, USA.","DOI":"10.1109\/INFCOM.2010.5462058"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2906","DOI":"10.1109\/JSEN.2014.2382568","article-title":"SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization","volume":"15","author":"Kang","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, W.H., Nam, S., Han, Y., and Lee, S.-J. (2012, January 9\u201312). Improved heading estimation for smartphone-based indoor positioning systems. Proceedings of the 2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications-(PIMRC), Sydney, Australia.","DOI":"10.1109\/PIMRC.2012.6362768"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1109\/TIM.2018.2871808","article-title":"Accurate step length estimation for pedestrian dead reckoning localization using stacked autoencoders","volume":"68","author":"Gu","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kang, J.H., Lee, J.B., and Eom, D.S. (2018). Smartphone-based traveled distance estimation using individual walking patterns for indoor localization. Sensors, 18.","DOI":"10.3390\/s18093149"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33178","DOI":"10.1109\/ACCESS.2020.2974038","article-title":"Smartphone-based Indoor Localization with Integrated Fingerprint Signal","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., Park, S., and Park, Y. (2019). DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier. Sensors, 20.","DOI":"10.3390\/s20010133"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Murata, M., Ahmetovic, D., Sato, D., Takagi, H., Kitani, K.M., and Asakawa, C. (2018, January 19\u201323). Smartphone-based indoor localization for blind navigation across building complexes. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), Athens, Greece.","DOI":"10.1109\/PERCOM.2018.8444593"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial neural networks: A tutorial","volume":"29","author":"Jain","year":"1996","journal-title":"IEEE Comput."},{"key":"ref_25","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11165","DOI":"10.1109\/ACCESS.2019.2891942","article-title":"An indoor position-estimation algorithm using smartphone IMU sensor data","volume":"7","author":"Poulose","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017, January 21\u201323). Understanding of a convolutional neural network. Proceedings of the International Conference on Engineering and Technology (ICET), Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_28","unstructured":"Chung, J., Culcehre, C., Cho, K.H., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Xu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_30","unstructured":"(2022, July 29). Rotation Vector. Available online: https:\/\/source.android.google.cn\/devices\/sensors\/sensor-types#rotation_vector."},{"key":"ref_31","unstructured":"(2022, July 29). Game Rotation Vector. Available online: https:\/\/source.android.google.cn\/devices\/sensors\/sensor-types#game_rotation_vector."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:25:10Z","timestamp":1760142310000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,7]]},"references-count":31,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186764"],"URL":"https:\/\/doi.org\/10.3390\/s22186764","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,7]]}}}