{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:07:26Z","timestamp":1778825246693,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,30]],"date-time":"2019-06-30T00:00:00Z","timestamp":1561852800000},"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>In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms.<\/jats:p>","DOI":"10.3390\/s19132899","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T03:23:59Z","timestamp":1561951439000},"page":"2899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition"],"prefix":"10.3390","volume":"19","author":[{"given":"Kuk Ho","family":"Heo","sequence":"first","affiliation":[{"name":"School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 702-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seol Young","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 702-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8171-195X","authenticated-orcid":false,"given":"Soon Ju","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 702-701, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jie, Y., Pei, J.Y., Jun, L., Yun, G., and Wei, X. (2013). Smart Home System Based on Iot Technologies, 2013 International Conference on Computational and Information Sciences, IEEE.","DOI":"10.1109\/ICCIS.2013.468"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.jclepro.2016.10.006","article-title":"A review of Internet of Things for smart home: Challenges and solutions","volume":"140","author":"Stojkoska","year":"2017","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fujdiak, R., Slacik, J., Orgon, M., Mlynek, P., Misurec, J., Hallon, J., and Halgos, J. (2018). Investigation of Power Line Communication and Wi-Fi Co-existence in Smart Home, 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), IEEE.","DOI":"10.1109\/ICUMT.2018.8631197"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Masek, P., Zeman, K., Kuder, Z., Hosek, J., Andreev, S., Fujdiak, R., and Kropfl, F. (2015). Wireless M-BUS: An Attractive M2M Technology for 5G-Grade Home Automation, International Internet of Things Summit, Springer.","DOI":"10.1007\/978-3-319-47063-4_13"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Masek, P., Fujdiak, R., Zeman, K., Hosek, J., and Muthanna, A. (2016). Remote Networking Technology for IoT: Cloud-Based Access for AllJoyn-Enabled Devices, Proceedings of the 18th Conference of Open Innovations Association FRUCT, FRUCT Oy.","DOI":"10.1109\/FRUCT-ISPIT.2016.7561528"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Del Campo, A., Gambi, E., Montanini, L., Perla, D., Raffaeli, L., and Spinsante, S. (2016). MQTT in AAL Systems for Home Monitoring of People with Dementia, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE.","DOI":"10.1109\/PIMRC.2016.7794566"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shin, D.-G., and Jun, M.-S. (2015). Home IoT Device Certification through Speaker Recognition, 2015 17th International Conference on Advanced Communication Technology (ICACT), IEEE.","DOI":"10.1109\/ICACT.2015.7224867"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","article-title":"An introduction to biometric recognition","volume":"14","author":"Jain","year":"2004","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","first-page":"43","article-title":"A survey of wearable biometric recognition systems","volume":"49","author":"Blasco","year":"2016","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_10","unstructured":"Delac, K., and Grgic, M. (2004). A Survey of Biometric Recognition Methods, Proceedings Elmar-2004: 46th International Symposium on Electronics in Marine, IEEE."},{"key":"ref_11","first-page":"2","article-title":"Comparison of various biometric methods","volume":"2","author":"Saini","year":"2014","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_12","first-page":"6048213","article-title":"Self-organizing wearable device platform for assisting and reminding humans in real time","volume":"2016","author":"Park","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1016\/j.sigpro.2007.04.004","article-title":"Overview of total least-squares methods","volume":"87","author":"Markovsky","year":"2007","journal-title":"Signal Process."},{"key":"ref_14","unstructured":"Yager, R.R., and Zadeh, L.A. (2012). An Introduction to Fuzzy Logic Applications in Intelligent Systems, Springer Science & Business Media."},{"key":"ref_15","unstructured":"Zhang, H. (2004, January 12\u201314). The optimality of naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004), Miami Beach, FL, USA."},{"key":"ref_16","first-page":"1","article-title":"k-Nearest neighbour classifiers","volume":"34","author":"Cunningham","year":"2007","journal-title":"Mult. Classif. Syst."},{"key":"ref_17","unstructured":"Yegnanarayana, B. (2009). Artificial Neural Networks, PHI Learning Pvt. Ltd."},{"key":"ref_18","unstructured":"DiGregorio, D.R. (2016). System and Method for Improving Speech Recognition Accuracy in a Work Environment. (No. 9,236,050), U.S. Patent."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mehrabani, M., Bangalore, S., and Stern, B. (2015). Personalized Speech Recognition for Internet of Things, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), IEEE.","DOI":"10.1109\/WF-IoT.2015.7389082"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wi, D., Kwon, H., Park, J., Kang, S., and Lee, J. (2018). Opportunistic and Location-Based Collaboration Architecture among Mobile Assets and Fixed Manufacturing Processes. Sensors, 18.","DOI":"10.3390\/s18082703"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MPRV.2006.2","article-title":"An introduction to RFID technology","volume":"5","author":"Want","year":"2006","journal-title":"IEEE Pervasive Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1109\/10.880106","article-title":"Footprint-based personal recognition","volume":"47","author":"Nakajima","year":"2000","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","first-page":"1393","article-title":"Person recognition method using sequential walking footprints via overlapped foot shape and center-of-pressure trajectory","volume":"87","author":"Jung","year":"2004","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_24","first-page":"630","article-title":"Self-organizing middleware platform based on overlay network for real-time transmission of mobile patients vital signal stream","volume":"38","author":"Kang","year":"2013","journal-title":"J. Korean Inst. Commun. Inf. Sci."},{"key":"ref_25","unstructured":"Weisstein, E.W. (2019, June 13). Rotation matrix. Available online: http:\/\/mathworld.wolfram.com\/RotationMatrix.html."},{"key":"ref_26","unstructured":"Cormen, T.H., Leiserson, C.E., Rivest, R.L., and Stein, C. (2009). Introduction to Algorithms, MIT Press."},{"key":"ref_27","first-page":"1","article-title":"The method of least squares","volume":"114","author":"Miller","year":"2006","journal-title":"Math. Dep. Brown Univ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal Component Analysis, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1038\/nbt0308-303","article-title":"What is principal component analysis?","volume":"26","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"ref_31","unstructured":"Vapnyarskii, I. (2001). Lagrange Multipliers. Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer."},{"key":"ref_32","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_33","unstructured":"Kontorovich, A., and Weiss, R. (2015). A bayes consistent 1-NN classifier. Artificial Intelligence and Statistics, Springer."},{"key":"ref_34","unstructured":"Mancuhan, K., and Clifton, C. (2016). K-Nearest Neighbor Classification Using Anatomized Data. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1109\/4235.850656","article-title":"Dimensionality reduction using genetic algorithms","volume":"4","author":"Raymer","year":"2000","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pulgar, F.J., Charte, F., Rivera, A.J., and del Jesus, M.J. (2018). AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction. arXiv.","DOI":"10.2991\/ijcis.2018.125905686"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1109\/TPWRD.2005.855482","article-title":"Best ANN structures for fault location in single-and double-circuit transmission lines","volume":"20","author":"Gracia","year":"2005","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.measurement.2016.04.052","article-title":"Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis","volume":"90","author":"Illias","year":"2016","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"236084","DOI":"10.1155\/2015\/236084","article-title":"Efficient ELM-based two stages query processing optimization for big data","volume":"2015","author":"Ding","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_40","first-page":"1","article-title":"RETRACTED: Comparison of running time between C4. 5 and k-nearest neighbor (k-NN) algorithm on deciding mainstay area clustering","volume":"2","author":"Ismanto","year":"2016","journal-title":"Int. J. Adv. Intell. 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