{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:43:39Z","timestamp":1775274219704,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GRRC program of Gyeonggi province","award":["GRRC-Gachon2017(B02)"],"award-info":[{"award-number":["GRRC-Gachon2017(B02)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, we propose a method to find an optimal combination of hyperparameters to improve the accuracy of respiration pattern recognition in a 1D (Dimensional) convolutional neural network (CNN). The proposed method is designed to integrate with a 1D CNN using the harmony search algorithm. In an experiment, we used the depth of the convolutional layer of the 1D CNN, the number and size of kernels in each layer, and the number of neurons in the dense layer as hyperparameters for optimization. The experimental results demonstrate that the proposed method provided a recognition rate for five respiration patterns of approximately 96.7% on average, which is an approximately 2.8% improvement over an existing method. In addition, the number of iterations required to derive the optimal combination of hyperparameters was 2,000,000 in the previous study. In contrast, the proposed method required only 3652 iterations.<\/jats:p>","DOI":"10.3390\/s20133697","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T02:44:25Z","timestamp":1593657865000},"page":"3697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System"],"prefix":"10.3390","volume":"20","author":[{"given":"Seong-Hoon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-5562","authenticated-orcid":false,"given":"Zong Woo","family":"Geem","sequence":"additional","affiliation":[{"name":"Department of Energy IT, Gachon University, Seoongnam 13120, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5905-9424","authenticated-orcid":false,"given":"Gi-Tae","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam 13120, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"265","DOI":"10.2528\/PIER09120302","article-title":"Analysis of vital signs monitoring using an IR-UWB radar","volume":"100","author":"Lazaro","year":"2010","journal-title":"Prog. Electromagn. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cho, H., and Yoon, S.M. (2018). Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening. Sensors, 18.","DOI":"10.3390\/s18041055"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kravchik, M., and Shabtai, A. (2018, January 15\u201319). Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks. Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy\u2014CPS-SPC \u201918, Toronto, ON, Canada.","DOI":"10.1145\/3264888.3264896"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kim, T., Lee, J., and Nam, J. (2018, January 15\u201320). Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8462046"},{"key":"ref_5","unstructured":"Lee, S.-M., Yoon, S.M., and Cho, H. (2017, January 13\u201316). Human activity recognition from accelerometer data using Convolutional Neural Network. Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5656","DOI":"10.1109\/TII.2019.2909730","article-title":"Deep Learning Based on 1-D Ensemble Networks Using ECG for Real-Time User Recognition","volume":"15","author":"Kim","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tran, V.P., Al-Jumaily, A., and Islam, S. (2019). Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3010003"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Javaid, A.Q., Noble, C.M., Rosenberg, R., and Weitnauer, M.A. (2015, January 9\u201311). Towards Sleep Apnea Screening with an Under-the-Mattress IR-UWB Radar Using Machine Learning. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.79"},{"key":"ref_9","unstructured":"Huang, X., Sun, L., Tian, T., Huang, Z., and Clancy, E. (2015, January 18\u201321). Real-time non-contact infant respiratory monitoring using UWB radar. Proceedings of the 2015 IEEE 16th International Conference on Communication Technology (ICCT), Hangzhou, China."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fedele, G., Pittella, E., Pisa, S., Cavagnaro, M., Canali, R., and Biagi, M. (2015, January 4\u20137). Sleep-Apnea Detection with UWB Active Sensors. Proceedings of the IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), Montreal, QC, Canada.","DOI":"10.1109\/ICUWB.2015.7324512"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11805","DOI":"10.1109\/ACCESS.2017.2707460","article-title":"HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yildirim, O., Baloglu, U.B., and Acharya, U.R. (2018). A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput. Appl., 1\u201312.","DOI":"10.1007\/s00521-018-3889-z"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","article-title":"Arrhythmia detection using deep convolutional neural network with long duration ECG signals","volume":"102","author":"Yildirim","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"432","DOI":"10.15837\/ijccc.2020.2.3868","article-title":"Weighted Random Search for CNN Hyperparameter Optimization","volume":"15","author":"Andonie","year":"2020","journal-title":"Int. J. Comput. Commun. Control."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s10772-019-09652-3","article-title":"PSO-based optimized CNN for Hindi ASR","volume":"22","author":"Passricha","year":"2019","journal-title":"Int. J. Speech Technol."},{"key":"ref_16","first-page":"26","article-title":"Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Senst, T., and Sikora, T. (2017, January 17\u201320). Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297018"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yadav, N., Yadav, A., Bansal, J.C., Deep, K., and Kim, J.H. (2018). Harmony Search and Nature Inspired Optimization Algorithms, Springer Nature Singapore pte Ltd.. Advances in Intelligent Systems and Computing.","DOI":"10.1007\/978-981-13-0761-4"},{"key":"ref_19","first-page":"105","article-title":"A Harmony Search Algorithm Comparison with Genetic Algorithms","volume":"574","author":"Peraza","year":"2014","journal-title":"Adv. Intell. Inf. Database Syst."},{"key":"ref_20","first-page":"587","article-title":"Comparison of Genetic Algorithm and Harmony Search for Generator Maintenance Scheduling","volume":"31","author":"Khan","year":"2012","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.swevo.2019.03.012","article-title":"Review of harmony search with respect to algorithm structure","volume":"48","author":"Zhang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.swevo.2016.01.005","article-title":"Metaheuristics in structural optimization and discussions on harmony search algorithm","volume":"28","author":"Saka","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"608","DOI":"10.3938\/NPSM.67.608","article-title":"Music-Inspired Harmony Search Algorithm and Its Experience-Based Derivative","volume":"67","author":"Geem","year":"2017","journal-title":"New Phys. Sae Mulli"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/03052150802449227","article-title":"Particle-swarm harmony search for water network design","volume":"41","author":"Geem","year":"2009","journal-title":"Eng. Optim."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, S.-H., Geem, Z.W., and Han, G.-T. (2019). A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor. Sensors, 19.","DOI":"10.3390\/s19153340"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/JETCAS.2018.2837778","article-title":"Guest Editorial Wireless Sensing Circuits and Systems for Healthcare and Biomedical Applications","volume":"8","author":"Li","year":"2018","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/JSEN.2018.2791400","article-title":"Wireless Wearable Magnetometer-Based Sensor for Sleep Quality Monitoring","volume":"18","author":"Milici","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TMTT.2013.2256924","article-title":"A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring","volume":"61","author":"Li","year":"2013","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1109\/JSEN.2013.2285915","article-title":"Design, Realization, and Test of a UWB Radar Sensor for Breath Activity Monitoring","volume":"14","author":"Bernardi","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32507","DOI":"10.1109\/ACCESS.2018.2846605","article-title":"Breathing Rhythm Analysis in Body Centric Networks","volume":"6","author":"Fan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","first-page":"494","article-title":"Respiratory Rate: The Forgotten Vital Sign\u2014Make It Count!","volume":"44","author":"Loughlin","year":"2018","journal-title":"Jt. Comm. J. Qual. Patient Saf."},{"key":"ref_32","unstructured":"Brownlee, J. (2018). Deep Learning for Time Series Forecasting, Machine Learning Mastery Pty. Ltd."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dubey, S.R., Chakraborty, S., Roy, S.K., Mukherjee, S., Singh, S.K., and Chaudhuri, B.B. (2020). diffGrad: An Optimization Method for Convolutional Neural Networks. IEEE Trans. Neural Netw. Learn. Syst., 1\u201312.","DOI":"10.1109\/TNNLS.2019.2955777"},{"key":"ref_34","unstructured":"Nal\u00e7akan, Y., and Ensari, T. (2018, January 11\u201315). Decision of Neural Networks Hyperparameters with a Population-Based Algorithm. Proceedings of the Intelligent Tutoring Systems, Montreal, QC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Geem, Z.W. (2009). Music-Inspired Harmony Search Algorithm: Theory and Applications, Springer.","DOI":"10.1007\/978-3-642-00185-7"},{"key":"ref_36","first-page":"17","article-title":"A Survey of Harmony Search Algorithm","volume":"70","author":"Metwally","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compstruc.2014.02.001","article-title":"An enhanced harmony search algorithm for optimum design of side sway steel frames","volume":"136","author":"Maheri","year":"2014","journal-title":"Comput. Struct."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Askarzadeh, A., Rashedi, E., Giannoccaro, I., and Patnaik, S. (2017). Harmony Search Algorithm. Advances in Computational Intelligence and Robotics, IGI Global.","DOI":"10.4018\/978-1-5225-2322-2.ch001"},{"key":"ref_39","first-page":"683","article-title":"Fine-Tuning Convolutional Neural Networks Using Harmony Search","volume":"Volume 9423","author":"Rosa","year":"2015","journal-title":"Natural Language Processing and Information Systems"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3697\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:46:07Z","timestamp":1760175967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/13\/3697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":39,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20133697"],"URL":"https:\/\/doi.org\/10.3390\/s20133697","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,1]]}}}