{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:44:27Z","timestamp":1773809067781,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,18]],"date-time":"2020-07-18T00:00:00Z","timestamp":1595030400000},"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","doi-asserted-by":"publisher","award":["2018R1A2B6001400"],"award-info":[{"award-number":["2018R1A2B6001400"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information and Communications Technology Planning  Evaluation","award":["2020-0-01463"],"award-info":[{"award-number":["2020-0-01463"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.<\/jats:p>","DOI":"10.3390\/s20144001","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:59:38Z","timestamp":1595242778000},"page":"4001","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-0627","authenticated-orcid":false,"given":"Jucheol","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nelson Hebert","family":"Minaya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhat Anh","family":"Le","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hee-Chan","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0462-0050","authenticated-orcid":false,"given":"Sang-Il","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Dankook University, Yongin-si 16890, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2018.01.007","article-title":"Biometric recognition by gait: A survey of modalities and features","volume":"167","author":"Connor","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1016\/j.patcog.2012.02.032","article-title":"Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors","volume":"45","author":"Choudhury","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.patcog.2007.11.021","article-title":"Gait analysis for human identification through manifold learning and HMM","volume":"41","author":"Cheng","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107069","DOI":"10.1016\/j.patcog.2019.107069","article-title":"A model-based gait recognition method with body pose and human prior knowledge","volume":"98","author":"Liao","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3758\/BF03212378","article-title":"Visual perception of biological motion and a model for its analysis","volume":"14","author":"Johansson","year":"1973","journal-title":"Percept. Psychophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"353","DOI":"10.3758\/BF03337021","article-title":"Recognizing friends by their walk: Gait perception without familiarity cues","volume":"9","author":"Cutting","year":"1977","journal-title":"Bull. Psychon. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1037\/0096-1523.4.3.357","article-title":"A biomechanical invariant for gait perception","volume":"4","author":"Cutting","year":"1978","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Manap, H.H., Tahir, N.M., and Yassin, A.I.M. (2011, January 14\u201317). Statistical analysis of parkinson disease gait classification using Artificial Neural Network. Proceedings of the 2011 Institute of Electrical and Electronics Engineers International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain.","DOI":"10.1109\/ISSPIT.2011.6151536"},{"key":"ref_9","first-page":"1794","article-title":"Classification of Parkinson\u2019s disease gait using spatial-temporal gait features","volume":"19","author":"Wahid","year":"2015","journal-title":"Inst. Electr. Electron. Eng. J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dehzangi, O., Taherisadr, M., and ChangalVala, R. (2017). IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors, 17.","DOI":"10.3390\/s17122735"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.gaitpost.2015.10.016","article-title":"Instrumented shoes for activity classification in the elderly","volume":"44","author":"Moufawad","year":"2016","journal-title":"Gait Posture"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.patcog.2017.09.005","article-title":"Idnet: Smartphone-based gait recognition with convolutional neural networks","volume":"74","author":"Gadaleta","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Choi, S.I., Moon, J., Park, H.C., and Choi, S.T. (2019). User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insole. Sensors, 19.","DOI":"10.3390\/s19173785"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3230633","article-title":"A survey on gait recognition","volume":"51","author":"Wan","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Choi, S.I., Lee, S.S., Park, H.C., and Kim, H. (2018, January 28\u201331). Gait type classification using smart insole sensors. Proceedings of the TENCON 2018\u20142018 Institute of Electrical and Electronics Engineers Region 10 Conference, Jeju, Korea.","DOI":"10.1109\/TENCON.2018.8650147"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"335","DOI":"10.2106\/00004623-196446020-00009","article-title":"Walking patterns of normal men","volume":"46","author":"Murray","year":"1964","journal-title":"J. Bone Jt. Surg."},{"key":"ref_18","unstructured":"Schalkoff, R.J. (1989). Digital Image Processing and Computer Vision, Wiley."},{"key":"ref_19","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014, January 8\u201313). Two-stream convolutional networks for action recognition in videos. Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1016\/j.jbiomech.2012.07.003","article-title":"Evaluation of gait and slip parameters for adults with intellectual disability","volume":"45","author":"Haynes","year":"2012","journal-title":"J. Biomech."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1056\/NEJMoa020441","article-title":"Abnormality of gait as a predictor of non-Alzheimer\u2019s dementia","volume":"347","author":"Verghese","year":"2002","journal-title":"N. Engl. J. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1097\/JGP.0b013e31821181c6","article-title":"Depressive symptoms and gait dysfunction in the elderly","volume":"20","author":"Brandler","year":"2012","journal-title":"Am. J. Geriatr. Psychiatry"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00508-016-1096-4","article-title":"Gait disorders in adults and the elderly","volume":"129","author":"Pirker","year":"2017","journal-title":"Wien. Klin. Wochenschr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nagano, H., Sarashina, E., Sparrow, W., Mizukami, K., and Begg, R. (2019). General Mental Health Is Associated with Gait Asymmetry. Sensors, 19.","DOI":"10.3390\/s19224908"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahad, M.A.R., Ngo, T.T., Antar, A.D., Ahmed, M., Hossain, T., Muramatsu, D., Makihara, Y., Inoue, S., and Yagi, Y. (2020). Wearable Sensor-Based Gait Analysis for Age and Gender Estimation. Sensors, 20.","DOI":"10.3390\/s20082424"},{"key":"ref_27","unstructured":"Zhang, B., Jiang, S., Wei, D., Marschollek, M., and Zhang, W. (June, January 30). State of the art in gait analysis using wearable sensors for healthcare applications. Proceedings of the 2012 Institute of Electrical and Electronics Engineers\/ACIS 11th International Conference on Computer and Information Science, Shanghai, China."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mendes, J.J.A., Vieira, M.E.M., Pires, M.B., and Stevan, S.L. (2016). Sensor fusion and smart sensor in sports and biomedical applications. Sensors, 16.","DOI":"10.3390\/s16101569"},{"key":"ref_29","unstructured":"Gouwanda, D., and Senanayake, S. (2008, January 25\u201328). Emerging trends of body-mounted sensors in sports and human gait analysis. Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, Kuala Lumpur, Malaysia."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lee, S.S., Choi, S.T., and Choi, S.I. (2019). Classification of Gait Type Based on Deep Learning Using Various Sensors with Smart Insole. Sensors, 19.","DOI":"10.3390\/s19081757"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kozlow, P., Abid, N., and Yanushkevich, S. (2018). Gait Type Analysis Using Dynamic Bayesian Networks. Sensors, 18.","DOI":"10.3390\/s18103329"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Niyogi, S.A., and Adelson, E.H. (1994, January 21\u201323). Analyzing and recognizing walking figures in XYT. Proceedings of the Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.1994.323868"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"\u015awito\u0144ski, A., Pola\u0144ski, A., and Wojciechowski, K. (2011, January 22\u201325). Human identification based on gait paths. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Ghent, Belgium.","DOI":"10.1007\/978-3-642-23687-7_48"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"563864","DOI":"10.1155\/2012\/563864","article-title":"Automatic human Gait imitation and recognition in 3D from monocular video with an uncalibrated camera","volume":"2012","author":"Yu","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tran, L., Yin, X., Atoum, Y., Liu, X., Wan, J., and Wang, N. (2019, January 15\u201320). Gait Recognition via Disentangled Representation Learning. Proceedings of the Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00484"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7884","DOI":"10.3390\/s130607884","article-title":"Gait-based person identification robust to changes in appearance","volume":"13","author":"Iwashita","year":"2013","journal-title":"Sensors"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moon, K.S., Lee, S.Q., Ozturk, Y., Gaidhani, A., and Cox, J.A. (2019). Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network. Sensors, 19.","DOI":"10.3390\/s19225024"},{"key":"ref_38","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TPAMI.2005.9","article-title":"Discriminative common vectors for face recognition","volume":"27","author":"Cevikalp","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TASSP.1978.1163154","article-title":"Cubic splines for image interpolation and digital filtering","volume":"26","author":"Hou","year":"1978","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_42","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA."},{"key":"ref_43","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_44","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(00)00122-2","article-title":"Monte Carlo cross validation","volume":"56","author":"Xu","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_46","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/4001\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:49:40Z","timestamp":1760176180000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/4001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,18]]},"references-count":46,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20144001"],"URL":"https:\/\/doi.org\/10.3390\/s20144001","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,18]]}}}