{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:40:20Z","timestamp":1760402420675,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,4]],"date-time":"2021-04-04T00:00:00Z","timestamp":1617494400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17K00384"],"award-info":[{"award-number":["17K00384"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.<\/jats:p>","DOI":"10.3390\/a14040117","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T11:48:29Z","timestamp":1617623309000},"page":"117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5485-2928","authenticated-orcid":false,"given":"Hirokazu","family":"Madokoro","sequence":"first","affiliation":[{"name":"Faculty of Software and Information Science, Iwate Prefectural University, Takizawa City 020-0693, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Nix","sequence":"additional","affiliation":[{"name":"Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo City 015-0055, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuhito","family":"Sato","sequence":"additional","affiliation":[{"name":"Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo City 015-0055, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"177","DOI":"10.18632\/aging.100139","article-title":"Why Human Lifespan is Rapidly Increasing: Solving \u201cLongevity Riddle\u201d with \u201cRevealed-Slow-Aging\u201d Hypothesis","volume":"2","author":"Blagosklonny","year":"2010","journal-title":"Aging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/S0140-6736(09)61460-4","article-title":"Aging Populations: The Challenges Ahead","volume":"374","author":"Christensen","year":"2009","journal-title":"Lancet"},{"key":"ref_3","first-page":"123","article-title":"Analysis of Incidents Occurring in Long-Term Care Insurance Facilities","volume":"30","author":"Mitadera","year":"2013","journal-title":"Bull. Soc. Med."},{"key":"ref_4","first-page":"1","article-title":"Detection of Abnormal Action Using Image Sequence for Monitoring System of Aged People","volume":"122","author":"Seki","year":"2002","journal-title":"Trans. Inst. Electr. Eng. Jpn. Part D"},{"key":"ref_5","first-page":"1129","article-title":"Proposal of Detection Method of the State of Fall in Closed Space with RFID Tag System","volume":"51","author":"Kaji","year":"2010","journal-title":"J. Inf. Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Madokoro, H., Nakasho, K., Shimoi, N., Woo, H., and Sato, K. (2020). Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns. Sensors, 20.","DOI":"10.3390\/s20051415"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4979","DOI":"10.1364\/AO.26.004979","article-title":"Counterpropagation Networks","volume":"26","author":"Nielsen","year":"1987","journal-title":"Appl. Opt."},{"key":"ref_8","unstructured":"Hiramatsu, D., Madokoro, H., Sato, K., Nakasho, K., and Shimoi, N. (2018, January 17\u201320). Automatic Calibration of Bed-Leaving Sensor Signals Based on Genetic Evolutionary Learning. Proceedings of the 18th International Conference on Control, Automation and Systems, GangWon, Korea."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","article-title":"A Survey on Evolutionary Computation Approaches to Feature Selection","volume":"20","author":"Xue","year":"2016","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Song, X., Yang, Z., and Zhao, J. (2008, January 20\u201320). Preliminary Research of a Medical Sensor Design by Evolution. Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering, Leicestershire, UK.","DOI":"10.1109\/FITME.2008.147"},{"key":"ref_11","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, University of Michigan Press."},{"key":"ref_12","unstructured":"Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press."},{"key":"ref_13","unstructured":"Katagiri, H., Hirasama, K., and Hu, J. (2000, January 8\u201311). Genetic network programming\u2014application to intelligent agents. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics,  Nashville, TN, USA."},{"key":"ref_14","unstructured":"Katagiri, H., Hirasama, K., Hu, J., and Murata, J. (2001, January 7\u201311). Network Structure Oriented Evolutionary Model\u2014Genetic Network Programming\u2014and Its Comparison with Genetic. Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA, USA."},{"key":"ref_15","unstructured":"Hirasawa, K., Okubo, M., Katagiri, H., Hu, J., and Murata, J. (2001, January 27\u201331). Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP). Proceedings of the Congress on Evolutionary Computation, Seoul, Korea."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"485","DOI":"10.9746\/sicetr1965.38.485","article-title":"Network Structure Oriented Evolutionary Model: Genetic Network Programming\u2014Its Comparison with Genetic Programming","volume":"38","author":"Katagiri","year":"2002","journal-title":"Trans. Soc. Instrum. Control Eng."},{"key":"ref_17","unstructured":"Goldman, C.V., and Rosenschein, J.S. (August, January 31). Emergent Coordination Through the Use of Cooperative State-changing Rules. Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1162\/evco.2007.15.3.369","article-title":"A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning","volume":"15","author":"Mabu","year":"2007","journal-title":"Evol. Comput."},{"key":"ref_19","first-page":"237","article-title":"Reinforcement learning: A survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Mabu, S., Hirasawa, K., and Hu, J. (2007, January 25\u201328). Genetic network programming with SARSA learning and its application to creating stock trading rules. Proceedings of the IEEE Congress on Evolutionary Computation, Singapore.","DOI":"10.1109\/CEC.2007.4424475"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1541\/ieejeiss.126.1009","article-title":"Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior","volume":"126","author":"Mabu","year":"2006","journal-title":"IEEJ Trans. EIS"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Mabu, S., Zhou, H., Shimada, K., and Hirasawa, K. (2010, January 18\u201323). Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction. Proceedings of the IEEE Congress on Evolutionary Computation, Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586456"},{"key":"ref_23","first-page":"89","article-title":"Towards the Maintenance of Population Diversity: A Hybrid Probabilistic Model Building Genetic Network Programming","volume":"1","author":"Li","year":"2010","journal-title":"Trans. Jpn. Soc. Evol. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.eswa.2015.10.006","article-title":"Combination of genetic network programming and knapsack problem to support record clustering on distributed databases","volume":"46","author":"Wedashwara","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Singh, R.P. (2011, January 27\u201329). Solving 0-1 Knapsack problem using Genetic Algorithms. Proceedings of the IEEE 3rd International Conference on Communication Software and Networks, Xi\u2019an, China.","DOI":"10.1109\/ICCSN.2011.6013975"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/TSMCC.2010.2050685","article-title":"An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming","volume":"41","author":"Mabu","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy Sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Inf. Control"},{"key":"ref_28","first-page":"13","article-title":"The CRISP-DM Model: The New Blueprint for Data Mining","volume":"5","author":"Shearer","year":"2000","journal-title":"J. Data Warehous."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/tee.23109","article-title":"Semi-Supervised Learning for Class Association Rule Mining Using Genetic Network Programming","volume":"15","author":"Mabu","year":"2020","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1177\/0583102404043275","article-title":"A Review of Power Harvesting from Vibration using Piezoelectric Materials","volume":"36","author":"Henry","year":"2004","journal-title":"Shock Vib. Dig."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_33","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep Learning for Visual Understanding: A Review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","article-title":"Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies","volume":"83","author":"Chong","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_36","unstructured":"Xia, X., Xu, C., and Nan, B. (2017, January 2\u20134). Inception-v3 for Flower Classification. Proceedings of the 2nd International Conference on Image, Vision and Computing, Chengdu, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning With Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the IEEE Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2Net: A New Multi-Scale Backbone Architecture","volume":"43","author":"Gao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.autcon.2018.12.006","article-title":"Deep Learning-Based Automatic Volumetric Damage Quantification Using Depth Camera","volume":"99","author":"Beckman","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Carbonaro, N., Laurino, M., Arcarisi, L., Menicucci, D., Gemignani, A., and Tognetti, A. (2021). Textile-Based Pressure Sensing Matrix for In-Bed Monitoring of Subject Sleeping Posture and Breathing Activity. Appl. Sci., 11.","DOI":"10.3390\/app11062552"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gaddam, A., Mukhopadhyay, S.C., and Gupta, G.S. (2010, January 23\u201325). Intelligent Bed Sensor System: Design, Experimentation and Results. Proceedings of the IEEE Sensors Applications Symposium, Limerick, Ireland.","DOI":"10.1109\/SAS.2010.5439390"},{"key":"ref_44","first-page":"25","article-title":"Analyzing Rising Patterns of Patients to Prevent Bed-related Falls (Second Report)","volume":"12","author":"Motegi","year":"2011","journal-title":"Trans. Jpn. Soc. Health Care Manag."},{"key":"ref_45","unstructured":"Liu, J., and Tang, Y.Y. (1998, January 4\u20139). Evolutionary Image Processing. Proceedings of the IEEE World Congress on Computational Intelligence, Anchorage, AK, USA."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:50Z","timestamp":1760362430000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/4\/117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,4]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["a14040117"],"URL":"https:\/\/doi.org\/10.3390\/a14040117","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,4,4]]}}}