{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:51:17Z","timestamp":1762509077353,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T00:00:00Z","timestamp":1706227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Science and Technology (CONACYT, Consejo Nacional de Humanidades, Ciencia y Tecnolog\u00eda) of the Mexican Federal Government"},{"name":"Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient\u2019s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.<\/jats:p>","DOI":"10.3390\/bdcc8020013","type":"journal-article","created":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T08:56:01Z","timestamp":1706259361000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2874-5533","authenticated-orcid":false,"given":"Claudia Angelica","family":"Rivera-Romero","sequence":"first","affiliation":[{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda El\u00e9ctrica Plantel Jalpa, Universidad Aut\u00f3noma de Zacatecas, Libramiento Jalpa Km 156+380, Fraccionamiento Solidaridad, Jalpa 99601, Zacatecas, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8097-9551","authenticated-orcid":false,"given":"Jorge Ulises","family":"Munoz-Minjares","sequence":"additional","affiliation":[{"name":"Unidad Acad\u00e9mica de Ingenier\u00eda El\u00e9ctrica Plantel Jalpa, Universidad Aut\u00f3noma de Zacatecas, Libramiento Jalpa Km 156+380, Fraccionamiento Solidaridad, Jalpa 99601, Zacatecas, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-7644","authenticated-orcid":false,"given":"Carlos","family":"Lastre-Dominguez","sequence":"additional","affiliation":[{"name":"Departamento de Ingenier\u00eda Electr\u00f3nica, Tecnol\u00f3gico Nacional de M\u00e9xico, Instituto Tecnol\u00f3gico de Oaxaca, Av. Ing. V\u00edctor Bravo Ahuja No. 125 Esquina Calzada Tecnol\u00f3gico, Oaxaca de Ju\u00e1rez 68030, Oaxaca, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0801-029X","authenticated-orcid":false,"given":"Misael","family":"Lopez-Ramirez","sequence":"additional","affiliation":[{"name":"Multidisciplinary Studies Department, Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato, Yuriria 38954, Guanajuato, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/RBME.2019.2927200","article-title":"Pressure injury prevention: A survey","volume":"13","author":"Mansfield","year":"2019","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1378\/chest.08-0934","article-title":"Sleep-related problems in common medical conditions","volume":"135","author":"Parish","year":"2009","journal-title":"Chest"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.procs.2022.12.192","article-title":"A systematic literature review of machine learning application in COVID-19 medical image classification","volume":"216","author":"Cenggoro","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1109\/JSEN.2023.3234335","article-title":"Recent Advances in Thermal Imaging and its Applications using Machine Learning: A Review","volume":"23","author":"Wilson","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_5","first-page":"1","article-title":"Survey on SVM and their application in image classification","volume":"13","author":"Chandra","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_6","unstructured":"Grandini, M., Bagli, E., and Visani, G. (2020). Metrics for Multi-Class Classification: An Overview. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1109\/TIM.2015.2426331","article-title":"In-bed mobility monitoring using pressure sensors","volume":"64","author":"Bennett","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1109\/JBHI.2013.2252911","article-title":"Estimation of body postures on bed using unconstrained ECG measurements","volume":"17","author":"Lee","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TITB.2012.2220374","article-title":"Position recognition to support bedsores prevention","volume":"17","author":"Barsocchi","year":"2012","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hittawe, M.M., Sidib\u00e9, D., and M\u00e9riaudeau, F. (2015, January 18\u201322). Bag of words representation and SVM classifier for timber knots detection on color images. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153187"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Beya, O., Hittawe, M., Sidib\u00e9, D., and M\u00e9riaudeau, F. (2015, January 23\u201327). Automatic Detection and Tracking of Animal Sperm Cells in Microscopy Images. Proceedings of the 2015 11th International Conference on Signal Image Technology and Internet Based Systems (SITIS), Bangkok, Thailand.","DOI":"10.1109\/SITIS.2015.111"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Rivera-romero, C.A., Palacios-hern\u00e1ndez, E.R., Vite-ch\u00e1vez, O., and Reyes-portillo, I.A. (2024). Early-Stage Identification of Powdery Mildew Levels for Cucurbit Plants in Open-Field Conditions Based on Texture Descriptors. Inventions, 9.","DOI":"10.3390\/inventions9010008"},{"key":"ref_14","first-page":"044515","article-title":"Visible and near-infrared spectroscopy for detection of powdery mildew in Cucurbita pepo L. leaves","volume":"14","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cruz-Santos, W., Beltr\u00e1n-Herrera, A., V\u00e1zquez-Santacruz, E., and Gamboa-Z\u00fa\u00f1iga, M. (2014, January 6\u201311). Posture classification of lying down human bodies based on pressure sensors array. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889886"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dweekat, O.Y., Lam, S.S., and McGrath, L. (2023). Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20010796"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s12028-022-01605-0","article-title":"Current practices for intracranial pressure and cerebral oxygenation monitoring in severe traumatic brain injury: A Latin American survey","volume":"38","author":"Godoy","year":"2023","journal-title":"Neurocrit. Care"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1038\/s41598-022-26812-9","article-title":"Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera","volume":"13","author":"Liu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TPAMI.2022.3158902","article-title":"Bodypressure-inferring body pose and contact pressure from a depth image","volume":"45","author":"Clever","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Stern, L., and Roshan Fekr, A. (2023). In-Bed Posture Classification Using Deep Neural Network. Sensors, 23.","DOI":"10.3390\/s23052430"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Silva, A., Metr\u00f4lho, J., Ribeiro, F., Fidalgo, F., Santos, O., and Dionisio, R. (2022). A review of intelligent sensor-based systems for pressure ulcer prevention. Computers, 11.","DOI":"10.3390\/computers11010006"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"527065","DOI":"10.1155\/2012\/527065","article-title":"Transition temperatures of thermotropic liquid crystals from the local binary gray level cooccurrence matrix","volume":"2012","author":"Sastry","year":"2012","journal-title":"Adv. Condens. Matter Phys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hajari, N., Lastre-Dominguez, C., Ho, C., Ibarra-Manzano, O., and Cheng, I. (2021). Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring. Sensors, 21.","DOI":"10.3390\/s21134356"},{"key":"ref_24","unstructured":"Grimm, R., Sukkau, J., Hornegger, J., and Greiner, G. (2011). Bildverarbeitung f\u00fcr die Medizin 2011: Algorithmen-Systeme-Anwendungen Proceedings des Workshops vom 20.-22. M\u00e4rz 2011 in L\u00fcbeck, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"559","DOI":"10.2478\/amns.2022.2.0041","article-title":"Pressure Image Recognition of Lying Positions Based on Multi-Feature Value Regularized Extreme Learning Algorithm","volume":"8","author":"Zhu","year":"2022","journal-title":"Appl. Math. Nonlinear Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2019.2892970","article-title":"In-bed pose estimation: Deep learning with shallow dataset","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.physa.2018.10.060","article-title":"SVM and KNN ensemble learning for traffic incident detection","volume":"517","author":"Xiao","year":"2019","journal-title":"Phys. A Stat. Mech. Its Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.neucom.2018.11.101","article-title":"Cost-Sensitive KNN Classification","volume":"391","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/01621459.1967.10482916","article-title":"On the Kolmogorov-Smirnov test for normality with mean and variance unknown","volume":"62","author":"Lilliefors","year":"1967","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1017\/S0021859600003750","article-title":"Studies in crop variation. I. An examination of the yield of dressed grain from Broadbalk","volume":"11","author":"Fisher","year":"1921","journal-title":"J. Agric. Sci."},{"key":"ref_32","unstructured":"Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. (1996). Applied Linear Statistical Models, Irwin."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ins.2016.01.033","article-title":"An improved method to construct basic probability assignment based on the confusion matrix for classification problem","volume":"340\u2013341","author":"Deng","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.beproc.2018.01.004","article-title":"Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle","volume":"148","author":"Salla","year":"2018","journal-title":"Behav. Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.compag.2018.08.048","article-title":"A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network","volume":"154","author":"Ma","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.compag.2018.08.027","article-title":"Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y","volume":"153","author":"Griffel","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1109\/TKDE.2017.2682249","article-title":"Confusion-Matrix-Based Kernel Logistic Regression for Imbalanced Data Classification","volume":"29","author":"Ohsaki","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vieira, S.M., Kaymak, U., and Sousa, J.M.C. (2010, January 18\u201323). Cohen\u2019s kappa coefficient as a performance measure for feature selection. Proceedings of the International Conference on Fuzzy Systems, Barcelona, Spain.","DOI":"10.1109\/FUZZY.2010.5584447"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.1016\/j.acra.2019.12.020","article-title":"Receiver Operating Characteristic (ROC) Analysis of Image Search-and-Localize Tasks","volume":"27","author":"Jiang","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pouyan, M.B., Birjandtalab, J., Heydarzadeh, M., Nourani, M., and Ostadabbas, S. (2017, January 16\u201319). A pressure map dataset for posture and subject analytics. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897206"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_43","unstructured":"Richard, A., and Johnson, D.W.W. (2007). Applied Multivariate Statistical Analysis, Pearson, Prentice Hall. [6th ed.]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1214\/ss\/1076102418","article-title":"John W. Tukey and Data Analysis","volume":"18","author":"Hoaglin","year":"2003","journal-title":"Stat. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patrec.2012.09.010","article-title":"A subspace approach to error correcting output codes","volume":"34","author":"Bagheri","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.1016\/j.patcog.2013.03.014","article-title":"A genetic-based subspace analysis method for improving Error-Correcting Output Coding","volume":"46","author":"Bagheri","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_47","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, NV, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rasheed, J. (2022). Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging. Symmetry, 14.","DOI":"10.3390\/sym14071398"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Attallah, O., and Ragab, D.A. (2023). Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104273"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3501105","DOI":"10.1109\/LGRS.2023.3235714","article-title":"A Method of Rainfall Detection from X-band Marine Radar Image Based on the Principal Component Feature Extracted","volume":"20","author":"Wei","year":"2023","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/2\/13\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:49:50Z","timestamp":1760104190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/2\/13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,26]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["bdcc8020013"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8020013","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2024,1,26]]}}}