{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:31:13Z","timestamp":1768829473900,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"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\/501100003725","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"the Korean government (MOTIE)","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":"the Korean government (MOTIE)","doi-asserted-by":"publisher","award":["P0020536"],"award-info":[{"award-number":["P0020536"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003661","name":"the Korean government (MOTIE)","doi-asserted-by":"publisher","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"MSIT (Ministry of Science and ICT), Korea","doi-asserted-by":"publisher","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"MSIT (Ministry of Science and ICT), Korea","doi-asserted-by":"publisher","award":["P0020536"],"award-info":[{"award-number":["P0020536"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"MSIT (Ministry of Science and ICT), Korea","doi-asserted-by":"publisher","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Indoor pedestrian localization has been the subject of a great deal of recent research. Various studies have employed pedestrian dead reckoning, which determines pedestrian positions by transforming data collected through sensors into pedestrian gait information. Although several studies have recently applied deep learning to moving object distance estimations using naturally collected everyday life data, this data collection approach requires a long time, resulting in a lack of data for specific labels or a significant data imbalance problem for specific labels. In this study, to compensate for the problems of the existing PDR, a method based on transfer learning and data augmentation is proposed for estimating moving object distances for pedestrians. Consistent high-performance moving object distance estimation is achieved using only a small training dataset, and the problem of the concentration of training data only on labels within a certain range is solved using window warping and scaling methods. The training dataset consists of the three-axes values of the accelerometer sensor and the pedestrian\u2019s movement speed calculated based on GPS coordinates. All data and GPS coordinates are collected through the smartphone. A performance evaluation of the proposed moving pedestrian distance estimation system shows a high distance error performance of 3.59 m with only approximately 17% training data compared to other moving object distance estimation techniques.<\/jats:p>","DOI":"10.3390\/rs15082122","type":"journal-article","created":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T01:36:45Z","timestamp":1681781805000},"page":"2122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Transfer Learning Approach for Indoor Localization with Small Datasets"],"prefix":"10.3390","volume":"15","author":[{"given":"Jeonghyeon","family":"Yoon","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea"}]},{"given":"Jisoo","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3512-5407","authenticated-orcid":false,"given":"Seungku","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liebner, M., Klanner, F., and Stiller, C. (2013, January 23\u201326). Active Safety for Vulnerable Road Users Based on Smartphone Position Data. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia.","DOI":"10.1109\/IVS.2013.6629479"},{"key":"ref_2","first-page":"26","article-title":"The Role and Major Issues of Location Recognition Technology under the COVID-19 Pandemic Situation","volume":"27","author":"Lee","year":"2020","journal-title":"J-KICS"},{"key":"ref_3","first-page":"63","article-title":"Indoor Positioning Technique Using the Landmark Based on Relative AP Signal Strengths","volume":"25","author":"Kim","year":"2020","journal-title":"JKSCI"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alarifi, A., Al-Salman, A., Alsaleh, M., Alnafessah, A., Al-Hadhrami, S., Al-Ammar, M.A., and Al-Khalifa, H.S. (2016). Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors, 16.","DOI":"10.3390\/s16050707"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7990","DOI":"10.1109\/JSEN.2022.3156971","article-title":"Robust UWB Indoor Localization for NLOS Scenes via Learning Spatial-Temporal Features","volume":"22","author":"Yang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/SURV.2012.121912.00075","article-title":"A Survey of Indoor Inertial Positioning Systems for Pedestrians","volume":"15","author":"Harle","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., and Park, Y. (2020). Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges. Electronics, 9.","DOI":"10.3390\/electronics9060891"},{"key":"ref_8","first-page":"521","article-title":"A Study on Indoor Positioning Based on Pedestrian Dead Reckoning Using Inertial Measurement Unit","volume":"17","author":"Lee","year":"2021","journal-title":"J. Korean Soc. Emerg. Med. Dis. Inf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1109\/JSEN.2020.3014955","article-title":"Pedestrian Dead Reckoning with Wearable Sensors: A Systematic Review","volume":"21","author":"Hou","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jimenez, A.R., Seco, F., Prieto, C., and Guevara, J. (2009, January 26\u201328). A Comparison of Pedestrian Dead Reckoning Algorithms Using a Low-cost MEMS IMU. Proceedings of the IEEE International Symposium on Intelligent Signal Processing, Budapest, Hungary.","DOI":"10.1109\/WISP.2009.5286542"},{"key":"ref_11","first-page":"1","article-title":"Using the ADXL202 in Pedometer and Personal Navigation Applications","volume":"2","author":"Harvey","year":"2002","journal-title":"Analog Devices AN-602 Appl. Note 2.2"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.14198\/JoPha.2009.3.1.05","article-title":"Pedestrian Tracking Using Inertial Sensors","volume":"3","author":"Alonso","year":"2009","journal-title":"JoPha"},{"key":"ref_13","unstructured":"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_14","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":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_15","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":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"85706","DOI":"10.1109\/ACCESS.2020.2993534","article-title":"StepNet-Deep Learning Approaches for Step Length Estimation","volume":"8","author":"Klein","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, L., and Liu, Y. (2020, January 13\u201315). An ANN Based Human Walking Distance Estimation with and Inertial Measurement Unit. Proceedings of the ICARCV, Shenzhen, China.","DOI":"10.1109\/ICARCV50220.2020.9305498"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, J., 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_19","doi-asserted-by":"crossref","unstructured":"Yoon, J.H., and Kim, S.K. (2022). Practical and Accurate Indoor Localization System Using Deep Learning. Sensors, 22.","DOI":"10.3390\/s22186764"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Rashid, K.M., and Louis, J. (2019, January 21\u201324). Window-Warping: A Time Series Data Augmentation of IMU Data for Construction Equipment Activity Identification. Proceedings of the International Symposium on Automation and Robotics in Construction, Banff, AB, Canada.","DOI":"10.22260\/ISARC2019\/0087"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201317). Data Augmentation of Wearable Sensor Data for Parkinson\u2019s Disease Monitoring Using Convolutional Neural Networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, UK.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_24","unstructured":"Cui, Z., Chen, W., and Chen, Y. (2016). Multi-Scale Convolutional Neural Networks for Time Series Classification. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Learning Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_26","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.A. (2018, January 10\u201313). Transfer Learning for Time Series Classification. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA."},{"key":"ref_27","first-page":"270","article-title":"A Survey on Deep Transfer Learning","volume":"11141","author":"Tan","year":"2018","journal-title":"IEEE Int. Conf. Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey on Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_29","first-page":"405","article-title":"A Sentiment Analysis Model for Small-scale Unstructured Policy Data Using Transfer Learning","volume":"31","author":"Ahn","year":"2020","journal-title":"J. Korean Data Inf. Sci. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamaria, J., Duan, Y., and Oleiwi, S.R. (2020). Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study. Appl. Sci., 10.","DOI":"10.3390\/app10134523"},{"key":"ref_31","first-page":"91","article-title":"Pedestrian Classification Using CNN\u2019s Deep Features and Transfer Learning","volume":"20","author":"Chung","year":"2019","journal-title":"J. Internet Comput. Serv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2122\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:17:36Z","timestamp":1760123856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2122"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,17]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082122"],"URL":"https:\/\/doi.org\/10.3390\/rs15082122","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,17]]}}}