{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:16:31Z","timestamp":1774991791166,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Currently, telemedicine has gained more strength and its use allows establishing areas that acceptably guarantee patient care, either at the level of control or event monitors. One of the systems that adapt to the objectives of telemedicine are fall detection systems, for which artificial vision or artificial intelligence algorithms are used. This work proposes the design and development of a fall detection model with the use of artificial intelligence, the model can classify various positions of people and identify when there is a fall. A Kinect 2.0 camera is used for monitoring, this device can sense an area and guarantees the quality of the images. The measurement of position values allows to generate the skeletonization of the person and the classification of the different types of movements and the activation of alarms allow us to consider this model as an ideal and reliable assistant for the integrity of the elderly. This approach analyzes images in real time and the results showed that our proposed position-based approach detects human falls reaching 80% accuracy with a simple architecture compared to other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/computation10110195","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:11:21Z","timestamp":1667445081000},"page":"195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Model for the Detection of Falls with the Use of Artificial Intelligence as an Assistant for the Care of the Elderly"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-7710","authenticated-orcid":false,"given":"William","family":"Villegas-Ch.","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda en Tecnolog\u00edas de la Informaci\u00f3n, FICA, Universidad de Las Am\u00e9ricas, Quito 170125, Ecuador"},{"name":"Facultad de Tecnolog\u00edas de Informaci\u00f3n, Universidad Latina de Costa Rica, San Jos\u00e9 70201, Costa Rica"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Santiago","family":"Barahona-Espinosa","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda en Tecnolog\u00edas de la Informaci\u00f3n, FICA, Universidad de Las Am\u00e9ricas, Quito 170125, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Walter","family":"Gaibor-Naranjo","sequence":"additional","affiliation":[{"name":"Carrera de Ciencias de la Computaci\u00f3n, Universidad Polit\u00e9cnica Salesiana, Quito 170105, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aracely","family":"Mera-Navarrete","sequence":"additional","affiliation":[{"name":"Departamento de Sistemas, Universidad Internacional del Ecuador, Quito 170411, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1109\/JSYST.2017.2780260","article-title":"A Skeleton-Free Fall Detection System from Depth Images Using Random Decision Forest","volume":"12","author":"Abobakr","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/978-3-642-25507-6_7","article-title":"A Comparative Analysis on Edge Detection of Colloid Cyst: A Medical Imaging Approach","volume":"395","author":"Behera","year":"2012","journal-title":"Stud. Comput. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6733","DOI":"10.1109\/JSEN.2016.2585667","article-title":"A Wearable Fall Detector for Elderly People Based on AHRS and Barometric Sensor","volume":"16","author":"Pierleoni","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Saleh, M., Georgi, N., Abbas, M., and le Bouquin Jeann\u00e8s, R. (2019, January 2\u20136). A Highly Reliable Wrist-Worn Acceleration-Based Fall Detector. Proceedings of the European Signal Processing Conference, A Coru\u00f1a, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902563"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17081","DOI":"10.1038\/srep17081","article-title":"Wearable Fall Detector Using Integrated Sensors and Energy Devices","volume":"5","author":"Jung","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_6","unstructured":"Instituto Nacional de Estad\u00edstica y Censos. (2022, August 08). La Poblaci\u00f3n Adulta Mayor Se Triplicar\u00eda En Los Pr\u00f3ximos 40 A\u00f1os, Available online: https:\/\/inec.cr\/noticias\/la-poblacion-adulta-mayor-se-triplicaria-los-proximos-40-anos."},{"key":"ref_7","unstructured":"Carmona Vald\u00e9s, S.E. (2009). El Bienestar Personal en el Envejecimiento, Ciencias Sociales de la Universidad Iberoamericana."},{"key":"ref_8","unstructured":"Meeradevi, T., Vikash Kumar, V., Subhiksa, S., and Rajhan, V. (2020). Wearable Fall Detector for Elderly People, Kongu Engineering College."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1002\/mds.27830","article-title":"Home-Based Monitoring of Falls Using Wearable Sensors in Parkinson\u2019s Disease","volume":"35","author":"Smits","year":"2020","journal-title":"Mov. Disord."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1007\/s11517-017-1632-z","article-title":"Combining Novelty Detectors to Improve Accelerometer-Based Fall Detection","volume":"55","author":"Medrano","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5465","DOI":"10.1109\/JSEN.2020.2970994","article-title":"Automated Development of Custom Fall Detectors: Position, Model and Rate Impact in Performance","volume":"20","author":"Silva","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1109\/JSEN.2016.2628099","article-title":"From Fall Detection to Fall Prevention: A Generic Classification of Fall-Related Systems","volume":"17","author":"Chaccour","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_13","first-page":"8","article-title":"Clinical Utility of a Personalized and Long-Term Monitoring Device for Parkinson\u2019s Disease in a Real Clinical Practice Setting: An Expert Opinion Survey on STAT-ONTM","volume":"1","author":"Cubo","year":"2020","journal-title":"Neurologia"},{"key":"ref_14","unstructured":"Odunmbaku, A., Rahmani, A.M., Liljeberg, P., and Tenhunen, H. (2015, January 27\u201329). Elderly Monitoring System with Sleep and Fall Detector. Proceedings of the Internet of Things. IoT Infrastructures Second International Summit, IoT 360\u00b0 2015, Rome, Italy."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"de Ram\u00f3n-Fern\u00e1ndez, A., Ruiz-Fern\u00e1ndez, D., Marcos-Jorquera, D., Gilart-Iglesias, V., and Vives-Boix, V. (2017). Monitoring-Based Model for Personalizing the Clinical Process of Crohn\u2019s Disease. Sensors, 17.","DOI":"10.3390\/s17071570"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Goeuriot, L., Pasi, G., Viviani, M., Villegas-Ch, W., Molina, S., de Jan\u00f3n, V., Montalvo, E., and Mera-Navarrete, A. (2022). Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R. Informatics, 9.","DOI":"10.3390\/informatics9030063"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Villegas-Ch, W., Garc\u00eda-Ortiz, J., and S\u00e1nchez-Viteri, S. (2021). Identification of the Factors That Influence University Learning with Low-Code\/No-Code Artificial Intelligence Techniques. Electronics, 10.","DOI":"10.3390\/electronics10101192"},{"key":"ref_18","unstructured":"Shen, L., Zhang, Q., Cao, G., and Xu, H. (2018, January 4\u201366). Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment. Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2018), Matsue, Japan."},{"key":"ref_19","first-page":"8887","article-title":"Assessment and Comparison of Functionalities of Telemedical Applications","volume":"107","author":"Rybka","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"40389","DOI":"10.1109\/ACCESS.2020.2969453","article-title":"Cluster-Analysis-Based User-Adaptive Fall Detection Using Fusion of Heart Rate Sensor and Accelerometer in a Wearable Device","volume":"8","author":"Nho","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","unstructured":"Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., and Felder, R. (2006). A Smart and Passive Floor-Vibration Based Fall Detector for Elderly, IEEE."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TNSRE.2007.916282","article-title":"Portable Preimpact Fall Detector with Inertial Sensors","volume":"16","author":"Wu","year":"2008","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sheikh, S.Y., and Jilani, M.T. (2021). A Ubiquitous Wheelchair Fall Detection System Using Low-Cost Embedded Inertial Sensors and Unsupervised One-Class SVM. J. Ambient. Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-021-03279-6"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"227","DOI":"10.14257\/ijmue.2014.9.4.24","article-title":"Development of Pallet Recognition System Using Kinect Camera","volume":"9","author":"Oh","year":"2014","journal-title":"Int. J. Multimed. Ubiquitous Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.procs.2018.07.121","article-title":"Kinect Camera Based Gait Data Recording and Analysis for Assistive Robotics-An Alternative to Goniometer Based Measurement Technique","volume":"133","author":"Roy","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1167\/18.10.156","article-title":"Face Recognition in Humans and Machines","volume":"18","author":"Abudarham","year":"2018","journal-title":"J. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jfoodeng.2015.10.009","article-title":"A Computer Vision System for Coffee Beans Classification Based on Computational Intelligence Techniques","volume":"171","author":"Leme","year":"2016","journal-title":"J. Food Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1590\/1678-457X.11615","article-title":"Computer Vision System Approach in Colour Measurements of Foods: Part I. Development of Methodology","volume":"36","author":"Tarlak","year":"2016","journal-title":"Food Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.lwt.2016.06.054","article-title":"The Use of Computer Vision System to Detect Pork Defects","volume":"73","author":"Chmiel","year":"2016","journal-title":"LWT"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.procs.2020.12.023","article-title":"Personality Classification of Facebook Users According to Big Five Personality Using SVM (Support Vector Machine) Method","volume":"179","author":"Utami","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/15715124.2019.1628030","article-title":"Prediction of Water Quality Index (WQI) Using Support Vector Machine (SVM) and Least Square-Support Vector Machine (LS-SVM)","volume":"19","author":"Leong","year":"2021","journal-title":"Int. J. River Basin Manag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aziz, O., Klenk, J., Schwickert, L., Chiari, L., Becker, C., Park, E.J., Mori, G., and Robinovitch, S.N. (2017). Validation of Accuracy of SVM-Based Fall Detection System Using Real-World Fall and Non-Fall Datasets. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180318"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9882","DOI":"10.1109\/JSEN.2018.2872835","article-title":"Impact of Sampling Rate on Wearable-Based Fall Detection Systems Based on Machine Learning Models","volume":"18","author":"Liu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1111\/coin.12428","article-title":"Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow","volume":"37","author":"Chhetri","year":"2021","journal-title":"Comput. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"166117","DOI":"10.1109\/ACCESS.2020.3021943","article-title":"Deep Learning Based Systems Developed for Fall Detection: A Review","volume":"8","author":"Islam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100185","DOI":"10.1016\/j.iot.2020.100185","article-title":"Highly-Efficient Fog-Based Deep Learning AAL Fall Detection System","volume":"11","author":"Usach","year":"2020","journal-title":"Internet Things"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Castro-Luna, G., and Jim\u00e9nez-Rodr\u00edguez, D. (2020). Relative and Absolute Reliability of a Motor Assessment System Using Kinect\u00ae Camera. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17165807"},{"key":"ref_38","first-page":"149","article-title":"An\u00e1lisis Del Desempe\u00f1o Del Sitio Web Del Instituto Ecuatoriano de Seguridad Social (IESS) Para Evaluar Su Accesibilidad y Usabilidad En Los Adultos Mayores de La Asociaci\u00f3n de Jubilados de La \u201cHermandad de Ferroviarios\u201d de La Ciudad de Quito","volume":"11","year":"2020","journal-title":"Propues. De. ComHumanit. Rev. Cient\u00edfica De Comun."},{"key":"ref_39","first-page":"359","article-title":"Pendeteksi Golongan Darah Manusia Berbasis Tensorflow Menggunakan ESP32-CAM","volume":"9","author":"Ghifari","year":"2021","journal-title":"ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"362","DOI":"10.11591\/eei.v9i1.1616","article-title":"Development of Vocabulary Learning Application by Using Machine Learning Technique","volume":"9","author":"Brahin","year":"2020","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_41","unstructured":"Huang, J. (2017). Accelerated Training and Inference with the Tensorflow Object Detection API, Google AI Blog."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hsieh, C.H., Lin, D.C., Wang, C.J., Chen, Z.T., and Liaw, J.J. (2019, January 7\u201310). Real-Time Car Detection and Driving Safety Alarm System with Google Tensorflow Object Detection API. Proceedings of the International Conference on Machine Learning and Cybernetics, Kobe, Japan.","DOI":"10.1109\/ICMLC48188.2019.8949265"},{"key":"ref_43","first-page":"421","article-title":"Pembuatan Aplikasi Deteksi Objek Menggunakan TensorFlow Object Detection API Dengan Memanfaatkan SSD MobileNet V2 Sebagai Model Pra-Terlatih","volume":"19","author":"Aningtiyas","year":"2020","journal-title":"J. Ilm. Komputasi"},{"key":"ref_44","first-page":"171","article-title":"Implementasi Framework Tensorflow Object Detection API Dalam Mengklasifikasi Jenis Kendaraan Bermotor","volume":"15","author":"Manajang","year":"2020","journal-title":"J. Tek. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Villegas-Ch, W., Garc\u00eda-Ortiz, J., Mullo-Ca, K., S\u00e1nchez-Viteri, S., and Roman-Ca\u00f1izares, M. (2021). Implementation of a Virtual Assistant for the Academic Management of a University with the Use of Artificial Intelligence. Future Internet, 13.","DOI":"10.3390\/fi13040097"},{"key":"ref_46","first-page":"9","article-title":"Human Related-Health Actions Detection Using Android Camera Based on TensorFlow Object Detection API","volume":"9","author":"Taqi","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/1475-925X-13-88","article-title":"Arm Movement Speed Assessment via a Kinect Camera: A Preliminary Study in Healthy Subjects","volume":"13","author":"Elgendi","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_48","first-page":"1751","article-title":"A Wrist-Type Fall Detector with Statistical Classifier for the Elderly Care","volume":"5","author":"Park","year":"2011","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"de Miguel, K., Brunete, A., Hernando, M., and Gambao, E. (2017). Home Camera-Based Fall Detection System for the Elderly. Sensors, 17.","DOI":"10.3390\/s17122864"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1108\/14608790200400005","article-title":"Fall Detectors: Do They Work or Reduce the Fear of Falling?","volume":"7","author":"Brownsell","year":"2004","journal-title":"Hous. Care Support"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/11\/195\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:15Z","timestamp":1760144955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/11\/195"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,2]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["computation10110195"],"URL":"https:\/\/doi.org\/10.3390\/computation10110195","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,2]]}}}