{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:43:25Z","timestamp":1760150605087,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["12161025","YQ22106","S202210595237","S202310595170"],"award-info":[{"award-number":["12161025","YQ22106","S202210595237","S202310595170"]}]},{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments","award":["12161025","YQ22106","S202210595237","S202310595170"],"award-info":[{"award-number":["12161025","YQ22106","S202210595237","S202310595170"]}]},{"name":"Innovation and Entrepreneurship Training Program for College Students of Guangxi","award":["12161025","YQ22106","S202210595237","S202310595170"],"award-info":[{"award-number":["12161025","YQ22106","S202210595237","S202310595170"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model\u2019s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.<\/jats:p>","DOI":"10.3390\/s23239613","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T12:15:14Z","timestamp":1701692114000},"page":"9613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7727-8640","authenticated-orcid":false,"given":"Qiancheng","family":"Tan","sequence":"first","affiliation":[{"name":"College of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-4257","authenticated-orcid":false,"given":"Yonghui","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Center for Applied Mathematics of Guangxi (GUET), Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0639-6749","authenticated-orcid":false,"given":"Rui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing, Fuzhou University, Fuzhou 350108, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3664-2979","authenticated-orcid":false,"given":"Sixuan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1257-3367","authenticated-orcid":false,"given":"Jing","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frobt.2015.00028","article-title":"A review of human activity recognition methods","volume":"2","author":"Vrigkas","year":"2015","journal-title":"Front. Robot. AI"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"30509","DOI":"10.1007\/s11042-020-09004-3","article-title":"Vision-based human activity recognition: A survey","volume":"79","author":"Beddiar","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hannan, A., Shafiq, M.Z., Hussain, F., and Pires, I.M. (2021). A portable smart fitness suite for real-time exercise monitoring and posture correction. Sensors, 21.","DOI":"10.3390\/s21196692"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.autcon.2017.02.001","article-title":"Monitoring workers\u2019 attention and vigilance in construction activities through a wireless and wearable electroencephalography system","volume":"82","author":"Wang","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22301","DOI":"10.1109\/TITS.2021.3135251","article-title":"Action recognition framework in traffic scene for autonomous driving system","volume":"23","author":"Xu","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1109\/JIOT.2019.2949715","article-title":"A novel IoT-perceptive human activity recognition (HAR) approach using multihead convolutional attention","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105986","DOI":"10.1016\/j.asoc.2019.105986","article-title":"Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition","volume":"87","author":"Khan","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). LSTM networks using smartphone data for sensor-based human activity recognition in smart homes. Sensors, 21.","DOI":"10.3390\/s21051636"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107338","DOI":"10.1016\/j.knosys.2021.107338","article-title":"A federated learning system with enhanced feature extraction for human activity recognition","volume":"229","author":"Xiao","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","article-title":"Bi-LSTM network for multimodal continuous human activity recognition and fall detection","volume":"20","author":"Li","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1080\/0951192X.2023.2177736","article-title":"HAR-time: Human action recognition with time factor analysis on worker operating time","volume":"36","author":"Yang","year":"2023","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, X., Meiqing, W., and Joaqu\u00edn, O. (2018). Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0. Sensors, 18.","DOI":"10.3390\/s18072146"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lima, W.S., Souto, E., El-Khatib, K., Jalali, R., and Gama, J. (2019). Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview. Sensors, 19.","DOI":"10.3390\/s19143213"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MIC.2023.3307395","article-title":"EQuaTE: Efficient Quantum Train Engine for Run-Time Dynamic Analysis and Visual Feedback in Autonomous Driving","volume":"27","author":"Park","year":"2023","journal-title":"IEEE Internet Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gao, G., Li, Z., Huan, Z., Chen, Y., Liang, J., Zhou, B., and Dong, C. (2021). Human behavior recognition model based on feature and classifier selection. Sensors, 21.","DOI":"10.3390\/s21237791"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1109\/ACCESS.2017.2676168","article-title":"Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","article-title":"Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey","volume":"8","author":"Demrozi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xia, C., and Sugiura, Y. (2021). Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design. Sensors, 21.","DOI":"10.3390\/s21206893"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/S0747-5632(99)00037-0","article-title":"Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring","volume":"15","author":"Foerster","year":"1999","journal-title":"Comput. Hum. Behav."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/10.554760","article-title":"A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity","volume":"44","author":"Bouten","year":"1997","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_21","unstructured":"Bao, L., and Intille, S.S. (2004). International Conference on Pervasive Computing, Springer."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.pmcj.2011.06.004","article-title":"Centinela: A human activity recognition system based on acceleration and vital sign data","volume":"8","author":"Lara","year":"2012","journal-title":"Pervasive Mob. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"31261","DOI":"10.1007\/s11042-018-6117-z","article-title":"A novel chaotic map based compressive classification scheme for human activity recognition using a tri-axial accelerometer","volume":"77","author":"Jansi","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/JBHI.2017.2722870","article-title":"Assessment of homomorphic analysis for human activity recognition from acceleration signals","volume":"22","author":"Vanrell","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/S0893-6080(01)00142-3","article-title":"Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm","volume":"15","author":"Billings","year":"2002","journal-title":"Neural Netw."},{"key":"ref_26","unstructured":"Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.R. (1999, January 25). Fisher discriminant analysis with kernels. Proceedings of the 1999 IEEE Signal Processing Society Workshop, Madison, WI, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"110081","DOI":"10.1016\/j.petrol.2021.110081","article-title":"Lithofacies identification in carbonate reservoirs by multiple kernel Fisher discriminant analysis using conventional well logs: A case study in A oilfield, Zagros Basin, Iraq","volume":"210","author":"Dong","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TCSVT.2003.818352","article-title":"Improving kernel Fisher discriminant analysis for face recognition","volume":"14","author":"Liu","year":"2004","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","unstructured":"Reyes-Ortiz, J., Anguita, D., Ghio, A., Oneto, L., and Parra, X. (2023, August 27). Human Activity Recognition Using Smartphones. UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/dataset\/240\/human+activity+recognition+using+smartphones."},{"key":"ref_30","unstructured":"Kwapisz, J.R., Weiss, G.M., and Moore, S.A. (2010, January 25). Activity Recognition using Cell Phone Accelerometers. Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington, DC, USA."},{"key":"ref_31","unstructured":"Barshan, B., and Altun, K. (2023, August 27). Daily and Sports Activities; UCI Machine Learning Repository: 2013. Available online: http:\/\/archive.ics.uci.edu\/dataset\/256\/daily+and+sports+activities."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tahir, S.B.U.D., Jalal, A., and Kim, K. (2020). Wearable Inertial Sensors for Daily Activity Analysis Based on Adam Optimization and the Maximum Entropy Markov Model. Entropy, 22.","DOI":"10.3390\/e22050579"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9244","DOI":"10.1038\/s41598-022-13498-2","article-title":"Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm","volume":"12","author":"Zhang","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_34","first-page":"1","article-title":"Feature selection: A data perspective","volume":"50","author":"Li","year":"2017","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining ACM, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear component analysis as a kernel eigenvalue problem","volume":"10","author":"Smola","year":"1998","journal-title":"Neural Comput."},{"key":"ref_37","unstructured":"Sch\u00f6lkopf, B., and Smola, A. (2002). Massachusetts Institute of Technology, MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/TIM.2015.2504078","article-title":"Using distributed wearable sensors to measure and evaluate human lower limb motions","volume":"65","author":"Qiu","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"70556","DOI":"10.1109\/ACCESS.2021.3078513","article-title":"Monitoring real-time personal locomotion behaviors over smart indoor-outdoor environments via body-worn sensors","volume":"9","author":"Gochoo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100190","DOI":"10.1016\/j.array.2022.100190","article-title":"Stochastic recognition of human daily activities via hybrid descriptors and random forest using wearable sensors","volume":"15","author":"Halim","year":"2022","journal-title":"Array"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"23964","DOI":"10.1109\/ACCESS.2022.3154775","article-title":"MS-DLD: Multi-sensors based daily locomotion detection via kinematic-static energy and body-specific HMMs","volume":"10","author":"Ghadi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106529","DOI":"10.1016\/j.engappai.2023.106529","article-title":"A new CNN-LSTM architecture for activity recognition employing wearable motion sensor data: Enabling diverse feature extraction","volume":"124","author":"Barshan","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"18708","DOI":"10.1109\/JSEN.2023.3292380","article-title":"MarNASNets: Toward CNN Model Architectures Specific to Sensor-Based Human Activity Recognition","volume":"23","author":"Kobayashi","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7188","DOI":"10.1109\/JSEN.2023.3242603","article-title":"A Novel Deep Multifeature Extraction Framework Based on Attention Mechanism Using Wearable Sensor Data for Human Activity Recognition","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s00607-021-00928-8","article-title":"Multi-input CNN-GRU based human activity recognition using wearable sensors","volume":"103","author":"Dua","year":"2021","journal-title":"Computing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Imran, H.A., Ikram, A.A., Wazir, S., and Hamza, K. (2023, January 17\u201318). EdgeHARNet: An Edge-Friendly Shallow Convolutional Neural Network for Recognizing Human Activities Using Embedded Inertial Sensors of Smart-Wearables. Proceedings of the 2023 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, Pakistan.","DOI":"10.1109\/C-CODE58145.2023.10139860"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"94173","DOI":"10.1109\/ACCESS.2023.3310269","article-title":"Attention-Based Residual BiLSTM Networks for Human Activity Recognition","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Thakur, D., Roy, S., Biswas, S., Ho, E.S.L., Chattopadhyay, S., and Shetty, S. (2023, January 4\u20136). A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network. Proceedings of the 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), Bellevue, WA, USA.","DOI":"10.1109\/IRI58017.2023.00032"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, S., Li, Y., and Yun, F. (2016, January 24). Multi-view time series classification: A discriminative bilinear projection approach. Proceedings of the 25th ACM International Conference on Information and Knowledge Management ACM, New York, NY, USA.","DOI":"10.1145\/2983323.2983780"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of prediction error by using K-fold cross-validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:47Z","timestamp":1760132267000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":51,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239613"],"URL":"https:\/\/doi.org\/10.3390\/s23239613","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,4]]}}}