{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:05:52Z","timestamp":1778252752458,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,9]],"date-time":"2017-12-09T00:00:00Z","timestamp":1512777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.<\/jats:p>","DOI":"10.3390\/s17122864","type":"journal-article","created":{"date-parts":[[2017,12,11]],"date-time":"2017-12-11T12:26:37Z","timestamp":1512995197000},"page":"2864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":230,"title":["Home Camera-Based Fall Detection System for the Elderly"],"prefix":"10.3390","volume":"17","author":[{"given":"Koldo","family":"De Miguel","sequence":"first","affiliation":[{"name":"Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9873-232X","authenticated-orcid":false,"given":"Alberto","family":"Brunete","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9997-0266","authenticated-orcid":false,"given":"Miguel","family":"Hernando","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1705-1800","authenticated-orcid":false,"given":"Ernesto","family":"Gambao","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2007). WHO Global Report on Falls Prevention in Older Age, World Health Organization."},{"key":"ref_2","unstructured":"Rodrigues, R., Rodrigues, M., and Lamura, G. (2012). Facts and Figures on Healthy Ageing and Long-Term Care, European Centre for Social Welfare Policy and Research."},{"key":"ref_3","unstructured":"Alzheimer\u2019s Disease International (ADI) (2013). World Alzheimer Report 2013, Alzheimer\u2019s Disease International (ADI)."},{"key":"ref_4","unstructured":"World Health Organization and Alzheimer\u2019s Disease International (2012). Dementia: A Public Health Priority, World Health Organization."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1108\/JET-12-2015-0039","article-title":"Can smart homes extend people with Alzheimer\u2019s disease stay at home?","volume":"11","author":"Brunete","year":"2017","journal-title":"J. Enabling Technol."},{"key":"ref_6","first-page":"763","article-title":"Internet use and depression among retired older adults in the United States: A longitudinal analysis","volume":"69","author":"Cotten","year":"2014","journal-title":"J. Gerontol. Ser. B Psychol. Sci. Soc. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","article-title":"A survey on fall detection: Principles and approaches","volume":"100","author":"Mubashir","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bagal\u00e0, F., Becker, C., Cappello, A., Chiari, L., Aminian, K., Hausdorff, J.M., Zijlstra, W., and Klenk, J. (2012). Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037062"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.-C., Chiang, C.-Y., Lin, P.-Y., Chou, Y.-C., Kuo, I.-T., Huang, C.-N., and Chan, C.-T. (2008, January 16\u201318). Development of a Fall Detecting System for the Elderly Residents. Proceedings of the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China.","DOI":"10.1109\/ICBBE.2008.669"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/BF02351026","article-title":"Evaluation of a fall detector based on accelerometers: A pilot study","volume":"43","author":"Lindemann","year":"2005","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mathie, M.J., Coster, A.C.F., Lovell, N.H., and Celler, B.G. (2014). Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol. Meas., 25.","DOI":"10.1088\/0967-3334\/25\/2\/R01"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TNSRE.2010.2070807","article-title":"Barometric pressure and triaxial accelerometry-based falls event detection","volume":"18","author":"Bianchi","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TITB.2009.2035050","article-title":"A body sensor network with electromyogram and inertial sensors: Multimodal interpretation of muscular activities","volume":"14","author":"Ghasemzadeh","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.pmcj.2012.08.003","article-title":"A smartphone-based fall detection system","volume":"8","author":"Abbate","year":"2012","journal-title":"Perv. Mob. Comput. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aihua, M., Ma, X., He, Y., and Luo, J. (2017). Highly Portable, Sensor-Based System for Human Fall Monitoring. Sensors, 17.","DOI":"10.3390\/s17092096"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Albert, M.V., Kording, K., Herrmann, M., and Jayaraman, A. (2012). Fall Classification by Machine Learning Using Mobile Phones. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0036556"},{"key":"ref_17","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_18","unstructured":"(2017, November 30). FATE Project. Available online: https:\/\/fate.webs.upc.edu\/project."},{"key":"ref_19","unstructured":"(2017, November 30). Tunstall Products. Available online: https:\/\/uk.tunstall.com\/services\/our-products\/."},{"key":"ref_20","unstructured":"Zhuang, X., Huang, J., Potamianos, G., and Hasegawa-Johnson, M. (2009, January 19\u201324). Acoustic fall detection using gaussian mixture models and gmm supervectors. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (2009), Taipei, Taiwan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.sigpro.2014.08.021","article-title":"An unsupervised acoustic fall detection system using source separation for sound interference suppression","volume":"110","author":"Khan","year":"2015","journal-title":"Signal Process."},{"key":"ref_22","unstructured":"Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., and Felder, R. (2006, January 24\u201328). A smart and passive floor-vibration based fall detector for elderly. Proceedings of the 2nd Information and Communication Technologies, ICTTA \u201906, Damascus, Syria."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1109\/TITB.2010.2051956","article-title":"Detection of falls among the elderly by a floor sensor using the electric near field","volume":"14","author":"Rimminen","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_24","unstructured":"Lin, J.C., and Nikita, K.S. (2011). Future care floor: A sensitive floor for movement monitoring and fall detection in home environments. Wireless Mobile Communication and Healthcare, Springer. Volume 55 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.autcon.2016.03.004","article-title":"Fall Detection and Intervention based on Wireless Sensor Network Technologies","volume":"71","author":"Cheng","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16920","DOI":"10.3390\/s121216920","article-title":"Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/TITB.2009.2033673","article-title":"A Wearable Airbag to Prevent Fall Injuries","volume":"13","author":"Tamura","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_28","unstructured":"(2017, November 30). The Top 10 Fall Detectors. Available online: http:\/\/www.toptenreviews.com."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asoc.2015.11.031","article-title":"Fuzzy inference-based fall detection using kinect and body-worn accelerometer","volume":"40","author":"Kwolek","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hsu, Y.W., Perng, J.W., and Liu, H.L. (2015, January 11\u201313). Development of a vision based pedestrian fall detection system with back propagation neural network. Proceedings of the 2015 IEEE\/SICE International Symposium on System Integration (SII), Nagoya, Japan.","DOI":"10.1109\/SII.2015.7405018"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Diraco, G., Leone, A., and Siciliano, P. (2010, January 8\u201312). An active vision system for fall detection and posture recognition in elderly healthcare. Proceedings of the 2010 Design, Automation and Test in Europe Conference and Exhibition (DATE 2010), Dresden, Germany.","DOI":"10.1109\/DATE.2010.5457055"},{"key":"ref_32","unstructured":"Kepski, M., and Kwolek, B. (2014, January 5\u20138). Fall detection using ceiling-mounted 3D depth camera. Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cviu.2015.12.002","article-title":"Human fall detection in videos via boosting and fusing statistical features of appearance, shape and motion dynamics on Riemannian manifolds with applications to assisted living","volume":"148","author":"Yun","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nguyen, H.T.K., Fahama, H., Belleudy, C., and Pham, T.V. (2014, January 21\u201323). Low Power Architecture Exploration for Standalone Fall Detection System Based on Computer Vision. Proceedings of the 2014 European Modelling Symposium, Pisa, Italy.","DOI":"10.1109\/EMS.2014.100"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zivkovic, Z. (2004, January 26). Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1333992"},{"key":"ref_36","unstructured":"Rougier, C., Meunier, J., St-Arnaud, A., and Rousseau, J. (September, January 30). Monocular 3D head tracking to detect falls of elderly people. Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS\u201906, New York, NY, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.imavis.2012.11.003","article-title":"3D head tracking for fall detection using a single calibrated camera","volume":"31","author":"Rougier","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1109\/TPAMI.2004.96","article-title":"Contour-based object tracking with occlusion handling in video acquired using mobile cameras","volume":"26","author":"Yilmaz","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Yakhu, S., and Suvonvorn, N. (2011, January 14\u201316). Object Based Video Surveillance Retrieval Using Color and Spatial Information of Human Appearance. Proceedings of the International Conference on Computer and Electrical Engineering 4th (ICCEE 2011), Singapore."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TPAMI.2007.1174","article-title":"Multicamera people tracking with a probabilistic occupancy map","volume":"30","author":"Fleuret","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Toreyin, B.U., Dedeoglu, Y., and Cetin, A.E. (2006, January 17\u201319). HMM based falling person detection using both audio and video. Proceedings of the 2006 IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey."},{"key":"ref_42","unstructured":"Yao, J., and Odobez, J.M. (2008, January 25\u201329). Multi-Camera 3D person tracking with particle filter in a surveillance environment. Proceedings of the 16th European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland."},{"key":"ref_43","unstructured":"Miaou, S.G., Sung, P.-H., and Huang, C.-Y. (2006, January 2\u20134). A customized human fall detection system using omni-camera images and personal information. Proceedings of the 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, Arlington, VA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vishwakarma, V., Mandal, C., and Sural, S. (2007). Automatic detection of human fall in video. Pattern Recognition and Machine Intelligence, Springer. Volume 4815 of Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-77046-6_76"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/1475-925X-12-66","article-title":"Challenges, issues and trends in fall detection systems","volume":"12","author":"Igual","year":"2013","journal-title":"BioMed. Eng. Online"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7174","DOI":"10.1016\/j.eswa.2010.04.014","article-title":"A fall detection system using k-nearest neighbor classier","volume":"37","author":"Liu","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s11760-014-0645-4","article-title":"Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera","volume":"8","author":"Feng","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.asoc.2014.01.024","article-title":"The implementation of an intelligent and video-based fall detection system using a neural network","volume":"18","author":"Alhimale","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_49","unstructured":"(2017, November 30). Carecams Website. Available online: https:\/\/www.carecams.co.uk\/peace-of-mind-cameras."},{"key":"ref_50","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, (IJCAI\u201981), Vancouver, BC, Canada."},{"key":"ref_51","unstructured":"Lucas, B.D. (1984). Generalized Image Matching by the Method of Differences. [Ph.D. Thesis, Robotics Institute, Carnegie Mellon University]."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2009.09.019","article-title":"Recognition of human activities using SVM multi-class classifier","volume":"31","author":"Qian","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_53","unstructured":"Elkins, M.R., and Blosser, J. (2017, November 30). The Mutt E-Mail Client. Available online: http:\/\/www.mutt.org\/."},{"key":"ref_54","unstructured":"Vysheng (2017, November 30). Vysheng\/tg-Github. Available online: https:\/\/github.com\/vysheng\/tg."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1258\/jtt.2009.090107","article-title":"An intelligent videomonitoring system for fall detection at home: perceptions of elderly people","volume":"15","author":"Londei","year":"2009","journal-title":"J. Telemed. Telecare"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2864\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:53:20Z","timestamp":1760208800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/12\/2864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,9]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["s17122864"],"URL":"https:\/\/doi.org\/10.3390\/s17122864","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,9]]}}}