{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T10:22:03Z","timestamp":1773397323563,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation of China","award":["61762085"],"award-info":[{"award-number":["61762085"]}]},{"name":"the National College Student Innovation Training Program","award":["201810755037"],"award-info":[{"award-number":["201810755037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.<\/jats:p>","DOI":"10.3390\/s20051447","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T09:26:41Z","timestamp":1583486801000},"page":"1447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8158-2628","authenticated-orcid":false,"given":"Pan","family":"Huang","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Yanping","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Xiaoyi","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]},{"given":"Shuxian","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105878","DOI":"10.1016\/j.rmed.2020.105878","article-title":"Long-term effects of web-based pedometer-mediated intervention on COPD exacerbations","volume":"162","author":"Wan","year":"2020","journal-title":"Respir. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ye, H., Li, Y.X., Luo, H., Wang, J.X., Chen, W., and Zhang, Q. (2019). Hybrid Urban Canyon Pedestrian Navigation Scheme Combined PDR, GNSS and Beacon Based on Smartphone. Remote Sens., 11.","DOI":"10.3390\/rs11182174"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1166\/jmihi.2020.2867","article-title":"A Human Body Based on Sift-Neural Network Algorithm Attitude Recognition Method","volume":"10","author":"Wang","year":"2020","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.4218\/etrij.2018-0577","article-title":"Human activity recognition with analysis of angles between skeletal joints using a RGB-depth sensor","volume":"42","author":"Ince","year":"2020","journal-title":"Etri J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ravbar, P., Branson, K., and Simpson, J.H. (2019). An automatic behavior recognition system classifies animal behaviors using actions and their temporal context. J. Neurosci. Methods, 326.","DOI":"10.1016\/j.jneumeth.2019.108352"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9692","DOI":"10.1109\/TIE.2018.2881943","article-title":"Activity Recognition Using Temporal Optical Flow Convolutional Features and Multilayer LSTM","volume":"66","author":"Ullah","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"An, J., Yang, L., and Lee, J. (2019). Three-dimensional indoor location estimation using single inertial navigation system with linear regression. Meas. Sci. Technol., 30.","DOI":"10.1088\/1361-6501\/ab2526"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Diete, A., and Stuckenschmidt, H. (2019). Fusing Object Information and Inertial Data for Activity Recognition. Sensors, 19.","DOI":"10.3390\/s19194119"},{"key":"ref_9","first-page":"673","article-title":"State-Space based Linear Modeling for Human Activity Recognition in Smart Space","volume":"25","author":"Kabir","year":"2019","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2019.06.008","article-title":"A multimodal smartphone sensor system for behaviour measurement and health status inference","volume":"53","author":"Kelly","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1109\/JSEN.2019.2926124","article-title":"An Experimental Heuristic Approach to Multi-Pose PDR without Using Magnetometers for Indoor Localization","volume":"19","author":"Lee","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.inffus.2019.06.013","article-title":"Adaptive sliding window based activity recognition for assisted livings","volume":"53","author":"Ma","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TIE.2019.2897550","article-title":"A Double-Step Unscented Kalman Filter and HMM-Based Zero-Velocity Update for PDR Using MEMS Sensors","volume":"67","author":"Tong","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","first-page":"76","article-title":"Design of STC12 and RFID Bracelet Campus Card System","volume":"19","author":"Huang","year":"2019","journal-title":"Microcontrollers Embed. Syst. (Key Mag. China Technol.)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Boudet, S., de l\u2019Aulnoit, A.H., Demailly, R., Peyrodie, L., Beuscart, R., and de l\u2019Aulnoit, D.H. (2019). Fetal heart rate baseline computation with a weighted median filter. Comput. Biol. Med., 114.","DOI":"10.1016\/j.compbiomed.2019.103468"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s42161-019-00334-2","article-title":"Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines","volume":"101","author":"Huang","year":"2019","journal-title":"J. Plant. Pathol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s10115-018-1176-z","article-title":"RTCRelief-F: An effective clustering and ordering-based ensemble pruning algorithm for facial expression recognition","volume":"59","author":"Li","year":"2019","journal-title":"Knowl. Inf. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1007\/s00521-018-3939-6","article-title":"Plant disease recognition using fractional-order Zernike moments and SVM classifier","volume":"31","author":"Kaur","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, F.F., Wang, C.K., Wu, S.W., Liu, J., Xu, A.A., and Pan, X.Z. (2019). Estimating the soil salinity over partially vegetated surfaces from multispectral remote sensing image using non-negative matrix factorization. Geoderma, 354.","DOI":"10.1016\/j.geoderma.2019.113887"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.measurement.2019.06.050","article-title":"Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine","volume":"146","author":"Xiao","year":"2019","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vieira, P.M., Freitas, N.R., Valente, J., Vaz, I.F., Rolanda, C., and Lima, C.S. (2019). Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning. Med. Phys., 13.","DOI":"10.1002\/mp.13709"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lu, H.J., Zou, N., Jacobs, R., Afflerbach, B., Lu, X.G., and Morgans, D. (2019). Error assessment and optimal cross-validation approaches in machine learning applied to impurity diffusion. Comput. Mater. Sci., 169.","DOI":"10.1016\/j.commatsci.2019.06.010"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Cui, Y.X., Liu, Z.H., Zhao, Y., Hu, M., and Hu, J.J. (2020). Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comput. Mater. Sci., 171.","DOI":"10.1016\/j.commatsci.2019.109203"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/10798587.2016.1267245","article-title":"Electro-Mechanical Impedance Based Position Identification of Bolt Loosening Using LibSVM","volume":"24","author":"Zhang","year":"2018","journal-title":"Intell. Autom. Soft Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:47Z","timestamp":1760173487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,6]]},"references-count":24,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051447"],"URL":"https:\/\/doi.org\/10.3390\/s20051447","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,6]]}}}