{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T20:30:06Z","timestamp":1777581006611,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T00:00:00Z","timestamp":1569024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key research project of science and technology from Ministry of Education of Hebei Province, China","award":["No. ZD2019010"],"award-info":[{"award-number":["No. ZD2019010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The purpose of activity recognition is to identify activities through a series of observations of the experimenter\u2019s behavior and the environmental conditions. In this study, through feature selection algorithms, we researched the effects of a large number of features on human activity recognition (HAR) assisted by an inertial measurement unit (IMU), and applied them to smartphones of the future. In the research process, we considered 585 features (calculated from tri-axial accelerometer and tri-axial gyroscope data). We comprehensively analyzed the features of signals and classification methods. Three feature selection algorithms were considered, and the combination effect between the features was used to select a feature set with a significant effect on the classification of the activity, which reduced the complexity of the classifier and improved the classification accuracy. We used five classification methods (support vector machine [SVM], decision tree, linear regression, Gaussian process, and threshold selection) to verify the classification accuracy. The activity recognition method we proposed could recognize six basic activities (BAs) (standing, going upstairs, going downstairs, walking, lying, and sitting) and postural transitions (PTs) (stand-to-sit, sit-to-stand, stand-to-lie, lie-to-stand, sit-to-lie, and lie-to-sit), with an average accuracy of 96.4%.<\/jats:p>","DOI":"10.3390\/info10100290","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T03:26:32Z","timestamp":1569209192000},"page":"290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0091-4182","authenticated-orcid":false,"given":"Shurui","family":"Fan","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"given":"Yating","family":"Jia","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"given":"Congyue","family":"Jia","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,21]]},"reference":[{"key":"ref_1","first-page":"2316757","article-title":"Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices","volume":"2016","author":"Lee","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, R., and Wang, B. (2016). PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors. Information, 7.","DOI":"10.3390\/info7040072"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"481580","DOI":"10.1155\/2013\/481580","article-title":"Physical activity recognition utilizing the built-in Kinematic sensors of a smartphone","volume":"9","author":"He","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.3390\/s16101752","article-title":"Design and Implementation of Foot-Mounted Inertial Sensor Based Wearable Electronic Device for Game Play Application","volume":"16","author":"Qifan","year":"2016","journal-title":"J. Sens."},{"key":"ref_5","first-page":"4073584","article-title":"Human Activity Recognition in AAL Environments Using Random Projections","volume":"2016","author":"Robertas","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sorkun, M.C., Dani\u015fman, A.E., and \u0130ncel, \u00d6.D. (2018, January 2\u20135). Human activity recognition with mobile phone sensors: Impact of sensors and window size. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404569"},{"key":"ref_7","first-page":"9753979","article-title":"User Satisfaction for an Augmented Reality Application to Support Productive Vocabulary Using Speech Recognition","volume":"2018","author":"Nurhazarifah","year":"2018","journal-title":"Adv. Multimed."},{"key":"ref_8","first-page":"140820","article-title":"Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework","volume":"2015","author":"Zheng","year":"2015","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Leonardis, G.D., Rosati, S., Balestra, G., Agostini, V., Panero, E., Gastaldi, L., and Knaflitz, M. (2018, January 11\u201313). Human Activity Recognition by Wearable Sensors: Comparison of different classifiers for real-time applications. Proceedings of the 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy.","DOI":"10.1109\/MeMeA.2018.8438750"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jarraya, A., Arour, K., Bouzeghoub, A., and Borgi, A. (2017, January 9\u201312). Feature selection based on Choquet integral for human activity recognition. Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy.","DOI":"10.1109\/FUZZ-IEEE.2017.8015432"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Doewes, A., Swasono, S.E., and Harjito, B. (2017, January 12\u201314). Feature selection on Human Activity Recognition dataset using Minimum Redundancy Maximum Relevance. Proceedings of the 2017 IEEE International Conference on Consumer Electronics\u2014Taiwan (ICCE-TW), Taipei, Taiwan.","DOI":"10.1109\/ICCE-China.2017.7991050"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, J. (2015, January 19\u201320). A Feature Subset Selection Algorithm Based on Feature Activity and Improved GA. Proceedings of the 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, China.","DOI":"10.1109\/CIS.2015.58"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ridok, A., Mahmudy, W.F., and Rifai, M. (2017, January 21\u201323). An improved artificial immune recognition system with fast correlation based filter (FCBF) for feature selection. Proceedings of the 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla, India.","DOI":"10.1109\/ICIIP.2017.8313761"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","article-title":"Transition-aware human activity recognition using smartphones","volume":"171","author":"Oneto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, Y., Cai, J., and Kot, A.C. (2016, January 20\u201325). Efficient object feature selection for action recognition. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472169"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.sigpro.2015.09.029","article-title":"Feature extraction from smartphone inertial signals for human activity segmentation","volume":"120","author":"Montero","year":"2016","journal-title":"Signal Process."},{"key":"ref_17","unstructured":"Gao, L., Bourke, A.K., and Nelson, J. (2012, January 23\u201324). A comparison of classifiers for activity recognition using multiple accelerometer-based sensors. Proceedings of the 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS), Limerick, Ireland."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xie, L., Tian, J., Ding, G., and Zhao, Q. (2018, January 26\u201319). Human activity recognition method based on inertial sensor and barometer. Proceedings of the 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Moltrasio, Italy.","DOI":"10.1109\/ISISS.2018.8358140"},{"key":"ref_19","first-page":"1406","article-title":"Activities of daily life (ADL) recognition using wrist-worn accelerometer","volume":"8","author":"Rajesh","year":"2016","journal-title":"Int. J. Eng. Technol."},{"key":"ref_20","first-page":"9762098","article-title":"Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition","volume":"2018","author":"Nhan","year":"2018","journal-title":"J. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a realtime human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mobark, M., Chuprat, S., and Mantoro, T. (2017, January 1\u20133). Improving the accuracy of complex activities recognition using accelerometer-embedded mobile phone classifiers. Proceedings of the 2017 Second International Conference on Informatics and Computing (ICIC), Jayapura, Indonesia.","DOI":"10.1109\/IAC.2017.8280606"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1016\/j.neucom.2011.03.028","article-title":"Analyzing human gait and posture by combining feature selection and kernel methods","volume":"74","author":"Albert","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kaytaran, T., and Bayindir, L. (2018, January 2\u20135). Activity recognition with wrist found in photon development board and accelerometer. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404630"},{"key":"ref_25","unstructured":"Ho, J. (2004). Interruptions: Using activity transitions to trigger proactive messages. [Master\u2019s Thesis, Massachusetts Institute of Technology]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/JSEN.2015.2487358","article-title":"A real-time human action recognition system using depth and inertial sensor fusion","volume":"16","author":"Chen","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/TNNLS.2014.2357794","article-title":"Ensemble manifold rank preserving for acceleration-based human activity recognition","volume":"27","author":"Tao","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, Z., and Jin, L. (2009, January 11\u201314). Activity recognition from acceleration data based on discrete consine transform and SVM. Proceedings of the IEEE Conference on Systems, Man and Cybernetics, San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5346042"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3198","DOI":"10.1109\/JSEN.2016.2519679","article-title":"A Triaxial Accelerometer-Based Human Activity Recognition via EEMD-Based Features and Game-Theory-Based Feature Selection","volume":"16","author":"Wang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pourpanah, F., Zhang, B., Ma, R., and Hao, Q. (2018, January 28\u201331). Non-Intrusive Human Motion Recognition Using Distributed Sparse Sensors and the Genetic Algorithm Based Neural Network. Proceedings of the 2018 IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589618"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.pmcj.2010.11.008","article-title":"Recognizing multi-user activities using wearable sensors in a smart home","volume":"7","author":"Wang","year":"2011","journal-title":"Pervasive Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/JSEN.2013.2288094","article-title":"2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors","volume":"14","author":"Zhou","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, M., Chen, Y., and Ji, G. (2010, January 11\u201314). Semi_Fisher Score: A semi-supervised method for feature selection. Proceedings of the 2010 International Conference on Machine Learning and Cybernetics, Qingdao, China.","DOI":"10.1109\/ICMLC.2010.5581007"},{"key":"ref_34","unstructured":"Liu, X., Wang, X.L., and Su, Q. (2015, January 22\u201324). Feature selection of medical data sets based on RS-RELIEFF. Proceedings of the 2015 12th International Conference on Service Systems and Service Management (ICSSSM), Guangzhou, China."},{"key":"ref_35","unstructured":"Haryanto, A.W., Mawardi, E.K. (2018, January 21\u201322). Influence of Word Normalization and Chi-Squared Feature Selection on Support Vector Machine (SVM) Text Classification. Proceedings of the 2018 International Seminar on Application for Technology of Information and Communication, Semarang, Indonesia."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Dai, H. (2018, January 9\u201312). Research on SVM improved algorithm for large data classification. Proceedings of the 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA), Shanghai, China.","DOI":"10.1109\/ICBDA.2018.8367673"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alam, S., Moonsoo, K., Jae-Young, P., and Kwon, G. (2016, January 5\u20138). Performance of classification based on PCA, linear SVM, and Multi-kernel SVM. Proceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria.","DOI":"10.1109\/ICUFN.2016.7536945"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9387","DOI":"10.1007\/s11042-014-2111-2","article-title":"Threshold selection and adjustment for online segmentation of one-stroke finger gestures using single tri-axial accelerometer","volume":"74","author":"Zhou","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_39","first-page":"33","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2013","journal-title":"Acm Comput. Surv."},{"key":"ref_40","unstructured":"Hoai, M., and Torre, F.D.L. (2012, January 18\u201320). Maximum margin temporal clustering. Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kavitha, S., Varuna, S., and Ramya, R. (2016, January 19). A comparative analysis on linear regression and support vector regression. Proceedings of the 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India.","DOI":"10.1109\/GET.2016.7916627"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, X., and Zhang, J. (2011, January 11\u201314). Active learning for human action recognition with Gaussian Processes. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6116363"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Oh, J., Kim, T., and Hong, H. (2013, January 24\u201326). Using Binary Decision Tree and Multiclass SVM for Human Gesture Recognition. Proceedings of the 2013 International Conference on Information Science and Applications (ICISA), Suwon, Korea.","DOI":"10.1109\/ICISA.2013.6579388"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yazdansepas, D., Niazi, A.H., Gay, J.L., Maier, F.W., Ramaswamy, L., Rasheed, K., and Buman, M.P. (2016, January 4\u20137). A Multi-featured Approach for Wearable Sensor-Based Human Activity Recognition. Proceedings of the 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, USA.","DOI":"10.1109\/ICHI.2016.81"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Capela, N., Lemaire, E., and Baddour, N. (2015). Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0124414"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/10\/290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:42Z","timestamp":1760188962000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/10\/290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,21]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["info10100290"],"URL":"https:\/\/doi.org\/10.3390\/info10100290","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,21]]}}}