{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:58:19Z","timestamp":1778345899974,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,26]],"date-time":"2021-09-26T00:00:00Z","timestamp":1632614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0104001"],"award-info":[{"award-number":["2020AAA0104001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008990","name":"Science and Technology Department of Zhejiang Province","doi-asserted-by":"publisher","award":["2021C03129"],"award-info":[{"award-number":["2021C03129"]}],"id":[{"id":"10.13039\/501100008990","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Lab.","award":["2019KD0AD011005"],"award-info":[{"award-number":["2019KD0AD011005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/s21196434","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"6434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method"],"prefix":"10.3390","volume":"21","author":[{"given":"Changjun","family":"Fan","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/MPRV.2010.7","article-title":"Human activity recognition and pattern discovery","volume":"9","author":"Kim","year":"2009","journal-title":"IEEE Pervasive Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.jnca.2007.11.002","article-title":"Human activity recognition in pervasive health-care: Supporting efficient remote collaboration","volume":"31","author":"Osmani","year":"2008","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MPRV.2008.24","article-title":"Activity-aware computing for healthcare","volume":"7","author":"Tentori","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_4","first-page":"2933","article-title":"Activity recognition in sensor data streams for active and assisted living environments","volume":"28","author":"Mosa","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1109\/TPAMI.2012.263","article-title":"Characterizing humans on Riemannian manifolds","volume":"35","author":"Tosato","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","first-page":"e1245","article-title":"Recent trends in machine learning for human activity recognition\u2014A survey","volume":"8","author":"Ramamurthy","year":"2018","journal-title":"Interdiscipl. Rev. Data Mining Knowl. Discov."},{"key":"ref_7","first-page":"11","article-title":"Joint amplitude and frequency analysis of tremor activity","volume":"39","author":"Foerster","year":"1999","journal-title":"Electromyogr. Clin. Neuro-Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, H., Cang, S., and Wang, Y. (2016., January 15\u201317). A review of sensor selection, sensor devices and sensor deployment for wearable sensor-based human activity recognition systems. Proceedings of the 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Chengdu, China.","DOI":"10.1109\/SKIMA.2016.7916228"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, J., and Havinga, P.J.M. (2016). Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MCOM.2012.6194391","article-title":"KNOWME: A case study in wireless body area sensor network design","volume":"50","author":"Mitra","year":"2012","journal-title":"IEEE Commun. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.eswa.2019.04.057","article-title":"A survey on wearable sensor modality centered human activity recognition in health care","volume":"137","author":"Wang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_12","unstructured":"Jordao, A., Nazare, A.C., Sena, J., and Schwartz, W.R. (2018). Human activity recognition based on wearable sensor data: A standardization of the state-of-the-art. arXiv."},{"key":"ref_13","first-page":"84","article-title":"Survey on Human Activity Recognition based on Acceleration Data","volume":"10","author":"Slim","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the Pervasive Computing: Second International Conference (PERVASIVE 2004), Linz\/Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., and Friedman, R. (2007, January 11\u201313). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor. Proceedings of the International Symposium on Wearable Computers, Boston, MA, USA.","DOI":"10.1109\/ISWC.2007.4373774"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ermes, M., Parkka, J., and Cluitmans, L. (2008, January 20\u201325). Advancing from offline to online activity recognition with wearable sensors. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada.","DOI":"10.1109\/IEMBS.2008.4650199"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TITB.2005.856863","article-title":"Activity Classification Using Realistic Data from Wearable Sensors","volume":"10","author":"Parkka","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Berchtold, M., Budde, M., Gordon, D., Schmidtke, H., and Beigl, M. (2010, January 10\u201313). Actiserv: Activity recognition service for mobile phones. Proceedings of the International Symposium on Wearable Computers, Seoul, Korea.","DOI":"10.1109\/ISWC.2010.5665868"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1689239.1689243","article-title":"Using mobile phones to determine transportation modes","volume":"6","author":"Reddy","year":"2010","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.future.2017.11.029","article-title":"A robust human activity recognition system using smartphone sensors and deep learning","volume":"81","author":"Hassan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.eswa.2016.04.032","article-title":"Human activity recognition with smartphone sensors using deep learning neural networks","volume":"59","author":"Ronao","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4566","DOI":"10.1109\/JSEN.2016.2545708","article-title":"A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone","volume":"16","author":"Wang","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1016\/j.eswa.2014.04.037","article-title":"Unsupervised learning for human activity recognition using smartphone sensors","volume":"41","author":"Kwon","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronao, C.A., and Cho, S.-B. (2014, January 19\u201321). Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models. Proceedings of the 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975918"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3070","DOI":"10.1109\/TII.2017.2712746","article-title":"Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM","volume":"13","author":"Chen","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Paul, P., and George, T. (2015, January 20). An effective approach for human activity recognition on smartphone. Proceedings of the 2015 IEEE International Conference on Engineering and Technology (ICETECH), Coimbatore, India.","DOI":"10.1109\/ICETECH.2015.7275024"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jpdc.2017.05.007","article-title":"GCHAR: An efficient Group-based Context\u2014Aware human activity recognition on smartphone","volume":"118","author":"Cao","year":"2018","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., Hnoohom, N., and Jitpattanakul, A. (2018, January 25\u201328). Smartwatch-based sitting detection with human activity recognition for office workers syndrome. Proceedings of the 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), Chiang Rai, Thailand.","DOI":"10.1109\/ECTI-NCON.2018.8378302"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2618045","DOI":"10.1155\/2018\/2618045","article-title":"Recognition of daily human activity using an artificial neural network and smartwatch","volume":"2018","author":"Kwon","year":"2018","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"568","DOI":"10.22362\/ijcert\/2016\/v3\/i10\/48906","article-title":"Human activity recognition using smartphone and smartwatch","volume":"3","author":"Ali","year":"2016","journal-title":"Int. J. Comput. Eng. Res. Trends"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Baldominos, A., Cervantes, A., Saez, Y., and Isasi, P. (2019). A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices. Sensors, 19.","DOI":"10.3390\/s19030521"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"124","DOI":"10.26599\/BDMA.2020.9020022","article-title":"Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications","volume":"4","author":"Salman","year":"2021","journal-title":"Big Data Min. Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"120835","DOI":"10.1109\/ACCESS.2020.3006163","article-title":"A new approach for smoking event detection using a variational autoencoder and neural decision forest","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jovi\u0107, A., Brki\u0107, K., and Bogunovi\u0107, N. (2015, January 25\u201329). A review of feature selection methods with applications. Proceedings of the 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Suto, J., Oniga, S., and Sitar, P.P. (2016, January 10\u201314). Comparison of wrapper and filter feature selection algorithms on human activity recognition. Proceedings of the 2016 6th International Conference on Computers Communications and Control (ICCCC), Oradea, Romania.","DOI":"10.1109\/ICCCC.2016.7496749"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2011, January 7\u20138). A feature selection-based framework for human activity recognition using wearable multimodal sensors. Proceedings of the 6th International Conference on Body Area Networks: Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Beijing, China.","DOI":"10.4108\/icst.bodynets.2011.247018"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, G., Wu, X., Liu, L., An, N., and Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18.","DOI":"10.3390\/s18113629"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/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_42","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1016\/j.isatra.2014.06.008","article-title":"Human activity recognition based on feature selection in smart home using back-propagation algorithm","volume":"53","author":"Fang","year":"2014","journal-title":"ISA Trans."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Capela, N.A., 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"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Fish, B., Khan, A., Chehade, N.H., Chien, C., and Pottie, G. (2012, January 25\u201330). Feature selection based on mutual information for human activity recognition. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288232"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Karagiannaki, K., Panousopoulou, A., and Tsakalides, P. (2017, January 5\u20139). An online feature selection architecture for human activity recognition. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952611"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1109\/JBHI.2017.2705036","article-title":"Physical activity recognition using posterior-adapted class-based fusion of multiaccelerometer data","volume":"22","author":"Chowdhury","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zainudin, M.N.S., Sulaiman, N., Mustapha, N., and Perumal, T. (2018, January 21\u201322). Activity Recognition Using One-Versus-All Strategy with Relief-F and Self-Adaptive Algorithm. Proceedings of the 2018 IEEE Conference on Open Systems (ICOS), Langkawi, Malaysia.","DOI":"10.1109\/ICOS.2018.8632818"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/TBME.2014.2307069","article-title":"Feature selection and activity recognition system using a single triaxial accelerometer","volume":"61","author":"Gupta","year":"2014","journal-title":"Biomed. Eng. IEEE Trans."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Altun, K., and Barshan, B. (2010, January 22). Human activity recognition using inertial\/magnetic sensor units. Proceedings of the 1st International Workshop on Human Behavior Understanding, Istanbul, Turkey.","DOI":"10.1007\/978-3-642-14715-9_5"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Leightley, D., Darby, J., Li, B., McPhee, J.S., and Yap, M.H. (2013, January 13\u201316). Human Activity Recognition for Physical Rehabilitation. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.","DOI":"10.1109\/SMC.2013.51"},{"key":"ref_51","unstructured":"Kose, M., Incel, O.D., and Ersoy, C. (2012, January 16). Online human activity recognition on smart phones. Proceedings of the 2nd International Workshop on Mobile Sensing: From Smartphones and Wearables to Big Data, Beijing, China."},{"key":"ref_52","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X.L., and Krishnaswamy, S. (2015, January 25\u201331). Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_53","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv."},{"key":"ref_54","first-page":"1","article-title":"Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities","volume":"54","author":"Chen","year":"2021","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chen, K., Yao, L., Zhang, D., Guo, B., and Yu, Z. (2019, January 10\u201316). Multi-agent Attentional Activity Recognition. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2019\/186"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, K., Yao, L., Wang, X., Zhang, D., Gu, T., Yu, Z., and Yang, Z. (2018, January 8\u201313). Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489767"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bhat, G., Deb, R., Chaurasia, V.V., Shill, H., and Ogras, U.Y. (2018, January 5\u20138). Online human activity recognition using low-power wearable devices. Proceedings of the Proceedings of the International Conference on Computer-Aided Design, San Diego, CA, USA.","DOI":"10.1145\/3240765.3240833"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1016\/j.eswa.2011.09.073","article-title":"A new hybrid ant colony optimization algorithm for feature selection","volume":"39","author":"Kabir","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Rostami, M., and Moradi, P. (2014, January 27\u201329). A clustering based genetic algorithm for feature selection. Proceedings of the 2014 6th Conference on Information and Knowledge Technology (IKT), Shahrood, Iran.","DOI":"10.1109\/IKT.2014.7030343"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.asoc.2013.09.018","article-title":"Browne. Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms","volume":"18","author":"Xue","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sadeg, S., Hamdad, L., Benatchba, K., and Habbas, Z. BSO-FS: Bee swarm optimization for feature selection in classification. Advances in Computational Intelligence, Proceedings of the International Work-Conference on Artificial Neural Networks, Palma de Mallorca, Spain, 10\u201312 June 2015, Springer.","DOI":"10.1007\/978-3-319-19258-1_33"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sadeg, S., Hamdad, L., Remache, A.R., Karech, M.N., Benatchba, K., and Habbas, A. (2019). Qbso-fs: A reinforcement learning based bee swarm optimization metaheuristic for feature selection. Advances in Computational Intelligence, Proceedings of the International Work-Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-20518-8_65"},{"key":"ref_63","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"133190","DOI":"10.1109\/ACCESS.2019.2940729","article-title":"Smartphone and smartwatch-based biometrics using activities of daily living","volume":"7","author":"Weiss","year":"2019","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"205707","DOI":"10.1155\/2015\/205707","article-title":"Gesture recognition from data streams of human motion sensor using accelerated PSO swarm search feature selection algorithm","volume":"2015","author":"Fong","year":"2015","journal-title":"J. Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"706287","DOI":"10.1155\/2014\/706287","article-title":"User-Independent Activity Recognition via Three-Stage GA-Based Feature Selection","volume":"10","author":"Saputri","year":"2014","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, J., Tian, L., Chen, L., Wang, H., Cao, T., and Yu, L. (2019, January 19\u201321). Optimal Feature Selection for Activity Recognition based on Ant Colony Algorithm. Proceedings of the Conference on Industrial Electronics and Applications, Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8834380"},{"key":"ref_68","first-page":"25","article-title":"A Cyclic Attribution Technique Feature Selection Method for Human Activity Recognition","volume":"10","author":"Myo","year":"2019","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ahmed, N., Rafiq, J.I., and Islam, M.R. (2020). Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors, 20.","DOI":"10.3390\/s20010317"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:05:31Z","timestamp":1760166331000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/19\/6434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,26]]},"references-count":69,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21196434"],"URL":"https:\/\/doi.org\/10.3390\/s21196434","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,26]]}}}