{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:21:50Z","timestamp":1775067710333,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T00:00:00Z","timestamp":1604275200000},"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":["NRF-2017R1A5A1015596"],"award-info":[{"award-number":["NRF-2017R1A5A1015596"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.<\/jats:p>","DOI":"10.3390\/s20216253","type":"journal-article","created":{"date-parts":[[2020,11,2]],"date-time":"2020-11-02T09:04:46Z","timestamp":1604307886000},"page":"6253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4105-5118","authenticated-orcid":false,"given":"Unang","family":"Sunarya","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea"},{"name":"School of Applied Science, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-3082","authenticated-orcid":false,"given":"Yuli","family":"Sun Hariyani","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea"},{"name":"School of Applied Science, Telkom University, Bandung 40257, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taeheum","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongryun","family":"Roh","sequence":"additional","affiliation":[{"name":"Human Convergence Technology R&amp;D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joonho","family":"Hyeong","sequence":"additional","affiliation":[{"name":"Human Convergence Technology R&amp;D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Illsoo","family":"Sohn","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Seoul National University of Science and Technology, Seoul 01811, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sayup","family":"Kim","sequence":"additional","affiliation":[{"name":"Human Convergence Technology R&amp;D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheolsoo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1016\/j.pain.2011.09.019","article-title":"The population prevalence of foot and ankle pain in middle and old age: A systematic review","volume":"152","author":"Thomas","year":"2011","journal-title":"Pain"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1002\/acr.22079","article-title":"Association of Planus Foot Posture and Pronated Foot Function With Foot Pain: The Framingham Foot Study","volume":"65","author":"Menz","year":"2013","journal-title":"Arthritis Care Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.foot.2017.03.001","article-title":"Defining excessive, over, or hyper-pronation: A quandary","volume":"31","author":"Horwood","year":"2017","journal-title":"Foot"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.future.2018.02.009","article-title":"Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease","volume":"83","author":"Abdulhay","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3389\/fpsyg.2017.00817","article-title":"Inertial sensors to assess gait quality in patients with neurological disorders: A systematic review of technical and analytical challenges","volume":"8","author":"Vienne","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jdiacomp.2012.10.013","article-title":"Increased gait variability in diabetes mellitus patients with neuropathic pain","volume":"27","author":"Lalli","year":"2013","journal-title":"J. Diabetes Complicat."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.gaitpost.2013.05.013","article-title":"Falls classification using tri-axis accelerometers during the five-times-sit-to-stand test","volume":"38","author":"Doheny","year":"2013","journal-title":"Gait Posture"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, K., Redmond, S.J., and Lovell, N.H. (2016). Monitoring for elderly care: The role of wearable sensors in fall detection and fall prediction research. Tele Medicine and Electronic Medicine, CRC Press.","DOI":"10.1201\/b19210-33"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.neucom.2015.05.061","article-title":"Improving fall detection by the use of depth sensor and accelerometer","volume":"168","author":"Kwolek","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1097\/BCO.0000000000000386","article-title":"Applications of gait analysis in pediatric orthopaedics","volume":"27","author":"Feng","year":"2016","journal-title":"Curr. Orthop. Pract."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Brunnekreef, J.J., Van Uden, C.J., van Moorsel, S., and Kooloos, J.G. (2005). Reliability of videotaped observational gait analysis in patients with orthopedic impairments. BMC Musculoskelet. Disord., 6.","DOI":"10.1186\/1471-2474-6-17"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1302\/0301-620X.94B4.27927","article-title":"Validation of a standardised gait score to predict the healing of tibial fractures","volume":"94","author":"Macri","year":"2012","journal-title":"J. Bone Jt. Surg. Br. Vol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-54399-1","article-title":"Open-source remote gait analysis: A post-surgery patient monitoring application","volume":"9","author":"Gurchiek","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_14","first-page":"1048","article-title":"Gait Analysis Using Wearable Sensor Inertial in a Child with CP after Orthopedic Surgery: Case Report","volume":"2","author":"Junior","year":"2018","journal-title":"Clin. Case Rep. Int. Physiother. Case Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1016\/j.jbiomech.2004.02.047","article-title":"Quantification of human motion: Gait analysis\u2014benefits and limitations to its application to clinical problems","volume":"37","author":"Simon","year":"2004","journal-title":"J. Biomech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TBME.2008.2003293","article-title":"A Strategy for Identifying Locomotion Modes Using Surface Electromyography","volume":"56","author":"Huang","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.bspc.2018.08.030","article-title":"Walking gait event detection based on electromyography signals using artificial neural network","volume":"47","author":"Nazmi","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mazzetta, I., Zampogna, A., Suppa, A., Gumiero, A., Pessione, M., and Irrera, F. (2019). Wearable sensors system for an improved analysis of freezing of gait in Parkinson\u2019s disease using electromyography and inertial signals. Sensors, 19.","DOI":"10.3390\/s19040948"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., and Yagi, Y. (2015). Similar gait action recognition using an inertial sensor. Pattern Recognit.","DOI":"10.1016\/j.patcog.2014.10.012"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9029","DOI":"10.1109\/JSEN.2016.2616163","article-title":"A low cost alternative to monitor human gait temporal parameters\u2013wearable wireless gyroscope","volume":"16","author":"Gouwanda","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, S.Y., and Kwon, G.I. (2014). Gravity Removal and Vector Rotation Algorithm for Step counting using a 3-axis MEMS accelerometer. J. Korea Soc. Comput. Inf.","DOI":"10.9708\/jksci.2014.19.5.043"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fujiwara, S., Sato, S., Sugawara, A., Nishikawa, Y., Koji, T., Nishimura, Y., and Ogasawara, K. (2020). The Coefficient of Variation of Step Time Can Overestimate Gait Abnormality: Test-Retest Reliability of Gait-Related Parameters Obtained with a Tri-Axial Accelerometer in Healthy Subjects. Sensors, 20.","DOI":"10.3390\/s20030577"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Buckley, C., Mic\u00f3-Amigo, M.E., Dunne-Willows, M., Godfrey, A., Hickey, A., Lord, S., Rochester, L., Del Din, S., and Moore, S.A. (2020). Gait Asymmetry Post-Stroke: Determining Valid and Reliable Methods Using a Single Accelerometer Located on the Trunk. Sensors, 20.","DOI":"10.3390\/s20010037"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Min, S.D., and Kwon, C.K. (2012). Step Counts and Posture Monitoring System using Insole Type Textile Capacitive Pressure Sensor for Smart Gait Analysis. J. Korea Soc. Comput. Inf.","DOI":"10.9708\/jksci.2012.17.8.107"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lou, C., Wang, S., Liang, T., Pang, C., Huang, L., Run, M., and Liu, X. (2017). A graphene-based flexible pressure sensor with applications to plantar pressure measurement and gait analysis. Materials, 10.","DOI":"10.3390\/ma10091068"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.orgel.2017.11.033","article-title":"Development of wearable and flexible insole type capacitive pressure sensor for continuous gait signal analysis","volume":"53","author":"Park","year":"2018","journal-title":"Org. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016). A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington\u2019s disease patients. Sensors, 16.","DOI":"10.3390\/s16010134"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"163180","DOI":"10.1109\/ACCESS.2019.2950254","article-title":"Abnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Song, F., Guo, Z., and Mei, D. (2010, January 12\u201314). Feature selection using principal component analysis. Proceedings of the 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization, Yichang, China.","DOI":"10.1109\/ICSEM.2010.14"},{"key":"ref_31","unstructured":"Vaughan, C. (1992). Dynamics of Human Gait. Dynamics of Human Gait, Kiboho. [2nd ed.]. Chapter 2."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1054\/foot.1999.0502","article-title":"Pronation and supination of the foot: Confused terminology","volume":"9","author":"McDonald","year":"1999","journal-title":"Foot"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Aggarwal, A., Gupta, R., and Agarwal, R. (2018, January 2\u20134). Design and Development of Integrated Insole System for Gait Analysis. Proceedings of the 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India.","DOI":"10.1109\/IC3.2018.8530543"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cho, T., Sunarya, U., Yeo, M., Hwang, B., and Koo, Y.S. (2019). Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy. MDPI Electron., 8.","DOI":"10.3390\/electronics8121461"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Peker, M., Arslan, A., \u015een, B., \u00c7elebi, F.V., and But, A. (2015, January 2\u20134). A novel hybrid method for determining the depth of anesthesia level: Combining ReliefF feature selection and random forest algorithm (ReliefF+RF). Proceedings of the 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), Madrid, Spain.","DOI":"10.1109\/INISTA.2015.7276737"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/JSYST.2013.2286539","article-title":"Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method","volume":"8","author":"Liu","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lei, S. (2012, January 23\u201325). A Feature Selection Method Based on Information Gain and Genetic Algorithm. Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering, Hangzhou, China.","DOI":"10.1109\/ICCSEE.2012.97"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, X., Wang, J., and Gong, Y. (2010, January 21\u201323). Research on speaker feature dimension reduction based on CCA and PCA. Proceedings of the 2010 International Conference on Wireless Communications & Signal Processing (WCSP), Suzhou, China.","DOI":"10.1109\/WCSP.2010.5632605"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1198\/106186006X94072","article-title":"Unsupervised Learning With Random Forest Predictors","volume":"15","author":"Shi","year":"2006","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_40","unstructured":"Liaw, A., and Wiener, M. (2002). Classification and Regression by randomForest. R News, 1609\u20133631."},{"key":"ref_41","unstructured":"Raschka, S. (2015). Python Machine Learning, Packt Publishing."},{"key":"ref_42","unstructured":"Harrison, M. (2019). Machine Learning Pocket Reference, O\u2019Reilly Media, Inc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TBME.2005.845241","article-title":"Support Vector Machines for Automated Gait Classification","volume":"52","author":"Begg","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_44","unstructured":"Olson, D.L., and Delen, D. (2008). Advanced Data Mining Techniques, Springer. Number January."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rehman, A., Khan, A., Ali, M.A., Khan, M.U., Khan, S.U., and Ali, L. (2020, January 12\u201313). Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction. Proceedings of the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey.","DOI":"10.1109\/ICECCE49384.2020.9179199"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Abdallah Bashir, A. (2012). Comparative study on classification performance between support vector machine and logistic regression. Int. J. Mach. Learn. Cybern., 4.","DOI":"10.1007\/s13042-012-0068-x"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1016\/j.joca.2016.03.008","article-title":"Gait analysis methods for rodent models of arthritic disorders: Reviews and recommendations","volume":"24","author":"Lakes","year":"2016","journal-title":"Osteoarthr. Cartil."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dom\u00ednguez-Morales, M.J., Luna-Perej\u00f3n, F., Mir\u00f3-Amarante, L., Hern\u00e1ndez-Vel\u00e1zquez, M., and Sevillano-Ramos, J.L. (2019). Smart footwear insole for recognition of foot pronation and supination using neural networks. Appl. Sci., 9.","DOI":"10.3390\/app9193970"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zhang, Y., Yang, Q., Deng, B., and Wang, H. (2020). Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network. Sensors, 20.","DOI":"10.3390\/s20195466"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e959","DOI":"10.3928\/01477447-20151020-02","article-title":"Gait Analysis Using a Support Vector Machine for Lumbar Spinal Stenosis","volume":"38","author":"Hayashi","year":"2015","journal-title":"Orthopedics"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/TBME.2007.905388","article-title":"Automatic Classification of Asymptomatic and Osteoarthritis Knee Gait Patterns Using Kinematic Data Features and the Nearest Neighbor Classifier","volume":"55","author":"Mezghani","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zeng, W., Li, J., Wang, Q., Liu, F., and Wang, Y. (2017, January 26\u201328). Classification of gait patterns of anterior cruciate ligament deficient knees using gait analysis via deterministic learning. Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China.","DOI":"10.23919\/ChiCC.2017.8029099"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, Y., and Bhanu, B. (2010, January 23\u201326). Age Classification Base on Gait Using HMM. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.934"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Eskofier, B., Lee, S., Baron, M., Simon, A., Martindale, C., Ga\u00dfner, H., and Klucken, J. (2017). An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring. Appl. Sci., 7.","DOI":"10.3390\/app7100986"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Devi Das, K., Saji, A.J., and Kumar, C.S. (2017, January 20\u201321). Frequency analysis of gait signals for detection of neurodegenerative diseases. Proceedings of the 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, India.","DOI":"10.1109\/ICCPCT.2017.8074273"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/JBHI.2014.2325413","article-title":"Gait and Balance Analysis for Patients With Alzheimer\u2019s Disease Using an Inertial-Sensor-Based Wearable Instrument","volume":"18","author":"Hsu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:13Z","timestamp":1760178493000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,2]]},"references-count":56,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216253"],"URL":"https:\/\/doi.org\/10.3390\/s20216253","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,2]]}}}