{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T14:27:43Z","timestamp":1771511263310,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11036-023-02215-6","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T11:01:47Z","timestamp":1691146907000},"page":"413-423","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fall Detection in the Elderly using Different Machine Learning Algorithms with Optimal Window Size"],"prefix":"10.1007","volume":"29","author":[{"given":"Firdous","family":"Kausar","sequence":"first","affiliation":[]},{"given":"Mostefa","family":"Mesbah","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3616-2621","authenticated-orcid":false,"given":"Waseem","family":"Iqbal","sequence":"additional","affiliation":[]},{"given":"Awais","family":"Ahmad","sequence":"additional","affiliation":[]},{"given":"Ikram","family":"Sayyed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"2215_CR1","unstructured":"Centers for Disease Control and Prevention (n.d.) Deaths from older adult falls. https:\/\/www.cdc.gov\/falls\/data\/fall-deaths.html. Accessed 20 July 2022"},{"key":"2215_CR2","unstructured":"Centers for Disease Control and Prevention (n.d.) Keep on your feet - preventing older adult falls. https:\/\/www.cdc.gov\/injury\/features\/older-adult-falls\/index.html. Accessed 20 July 2022"},{"issue":"5","key":"2215_CR3","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1097\/EDE.0b013e3181e89905","volume":"21","author":"S Deandrea","year":"2010","unstructured":"Deandrea S et al (2010) Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology 21(5):658\u2013668","journal-title":"Epidemiology"},{"key":"2215_CR4","doi-asserted-by":"publisher","unstructured":"Waheed M et al (2021) NT-FDS\u2014a noise tolerant fall detection system using deep learning on wearable devices. Sensors 21(6). https:\/\/doi.org\/10.3390\/s21062006","DOI":"10.3390\/s21062006"},{"issue":"15","key":"2215_CR5","doi-asserted-by":"publisher","first-page":"12192","DOI":"10.1016\/j.eswa.2012.04.058","volume":"39","author":"A Costa","year":"2012","unstructured":"Costa A et al (2012) Sensor-driven agenda for intelligent home care of the elderly. Expert Syst Appl 39(15):12192\u201312204","journal-title":"Expert Syst Appl"},{"key":"2215_CR6","first-page":"5005","volume":"83","author":"SR Santhosh","year":"2021","unstructured":"Santhosh SR (2021) Healthcare monitoring system for elderly or disabled persons using IoT. Test Eng Manage 83:5005\u20135008","journal-title":"Test Eng Manage"},{"key":"2215_CR7","doi-asserted-by":"crossref","unstructured":"Foroughi H et al (2008) Intelligent video surveillance for monitoring fall detection of elderly in home environments. 11th International Conference on Computer and Information Technology, pp 219\u2013224","DOI":"10.1109\/ICCITECHN.2008.4803020"},{"key":"2215_CR8","doi-asserted-by":"publisher","first-page":"178627","DOI":"10.1109\/ACCESS.2020.3027535","volume":"8","author":"F Muheidat","year":"2020","unstructured":"Muheidat F, Tawalbeh AL (2020) In-home floor based sensor system-smart carpet- to facilitate healthy aging in place (AIP). IEEE Access 8:178627\u2013178638","journal-title":"IEEE Access"},{"issue":"2","key":"2215_CR9","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/JSEN.2016.2625099","volume":"17","author":"M Daher","year":"2017","unstructured":"Daher M et al (2017) Elder tracking and fall detection system using smart tiles. IEEE Sens J 17(2):469\u2013479","journal-title":"IEEE Sens J"},{"key":"2215_CR10","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s40860-018-0065-2","volume":"4","author":"G Mokhtari","year":"2018","unstructured":"Mokhtari G et al (2018) Fall detection in smart home environments using UWB sensors and unsupervised change detection. J Reliable Intell Environ 4:131\u2013139","journal-title":"J Reliable Intell Environ"},{"issue":"8","key":"2215_CR11","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1109\/JSEN.2019.2891128","volume":"19","author":"M Saleh","year":"2019","unstructured":"Saleh M, Jeann\u00e8s RLB (2019) Elderly fall detection using wearable sensors: a low cost highly accurate algorithm. IEEE Sens J 19(8):3156\u20133164","journal-title":"IEEE Sens J"},{"issue":"1","key":"2215_CR12","first-page":"23","volume":"28","author":"T Xu","year":"2021","unstructured":"Xu T et al (2021) A two-step fall detection algorithm combining threshold-based method and convolutional neural network. Metrol Meas Syst 28(1):23\u201340","journal-title":"Metrol Meas Syst"},{"issue":"22","key":"2215_CR13","doi-asserted-by":"publisher","first-page":"6479","DOI":"10.3390\/s20226479","volume":"20","author":"L Plamerini","year":"2020","unstructured":"Plamerini L et al (2020) Accelerometer-based fall detection using machine learning: training and testing on real-world falls. Sensors 20(22):6479. https:\/\/doi.org\/10.3390\/s20226479","journal-title":"Sensors"},{"issue":"9","key":"2215_CR14","doi-asserted-by":"publisher","first-page":"12301","DOI":"10.3390\/s120912301","volume":"12","author":"SH Liu","year":"2012","unstructured":"Liu SH, Cheng WC (2012) Fall detection with the support vector machine during scripted and continuous unscripted activities. Sensors 12(9):12301\u201312316","journal-title":"Sensors"},{"issue":"4","key":"2215_CR15","doi-asserted-by":"publisher","first-page":"649","DOI":"10.3390\/sym12040649","volume":"12","author":"E Casilari","year":"2020","unstructured":"Casilari E et al (2020) A study of the use of gyroscope measurements in wearable fall detection systems. Symmetry 12(4):649. https:\/\/doi.org\/10.3390\/sym12040649","journal-title":"Symmetry"},{"key":"2215_CR16","doi-asserted-by":"publisher","unstructured":"Radmanesh E et al (2020) A wearable IoT-based fall detection system using triaxial accelerometer and barometric pressure sensor. In: Lecture Notes in Computer Science, vol. 12293, Springer. https:\/\/doi.org\/10.1007\/978-3-030-58008-7_13","DOI":"10.1007\/978-3-030-58008-7_13"},{"key":"2215_CR17","doi-asserted-by":"publisher","unstructured":"Tolkiehn M et al (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 369\u2013372. https:\/\/doi.org\/10.1109\/IEMBS.2011.6090120","DOI":"10.1109\/IEMBS.2011.6090120"},{"key":"2215_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25751-8_69","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision","author":"FM Grisales-Franco","year":"2015","unstructured":"Grisales-Franco FM et al (2015) Fall detection algorithm based on thresholds and residual events. In: Pardo A, Kittler J (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, vol 9423. Springer. https:\/\/doi.org\/10.1007\/978-3-319-25751-8_69"},{"key":"2215_CR19","doi-asserted-by":"crossref","unstructured":"Fudickar S et al (2014) Threshold-based fall detection on smart phones. In: Proceedings of the International Conference on Health Informatics (HEALTHINF-2014), pp 303\u2013309","DOI":"10.5220\/0004795803030309"},{"issue":"13","key":"2215_CR20","doi-asserted-by":"publisher","first-page":"5110","DOI":"10.1109\/JSEN.2019.2903482","volume":"19","author":"J He","year":"2019","unstructured":"He J et al (2019) A low power fall sensing technology based on FD-CNN. IEEE Sens J 19(13):5110\u20135118","journal-title":"IEEE Sens J"},{"issue":"1","key":"2215_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","volume":"55","author":"O Aziz","year":"2016","unstructured":"Aziz O et al (2016) A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Compu 55(1):45\u201355","journal-title":"Med Biol Eng Compu"},{"key":"2215_CR22","doi-asserted-by":"crossref","unstructured":"Giuffrida D et al (2019) Fall detection with supervised machine learning using wearable sensors. In: IEEE 17th International Conference on Industrial Informatics","DOI":"10.1109\/INDIN41052.2019.8972246"},{"key":"2215_CR23","doi-asserted-by":"crossref","unstructured":"Vallabh P et al (2016) Fall detection using machine learning algorithms. In: 24th International Conference on Software, Telecommunications and Computer Networks","DOI":"10.1109\/SOFTCOM.2016.7772142"},{"key":"2215_CR24","doi-asserted-by":"publisher","unstructured":"Boulellaa E et al (2019) Covariance matrix based fall detection from multiple wearable sensors. J Biomed Inform 94. https:\/\/doi.org\/10.1016\/j.jbi.2019.103189","DOI":"10.1016\/j.jbi.2019.103189"},{"key":"2215_CR25","doi-asserted-by":"publisher","first-page":"012035","DOI":"10.1088\/1755-1315\/258\/1\/012035","volume":"258","author":"IWW Wisesa","year":"2019","unstructured":"Wisesa IWW, Mahardika G (2019) Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks. IOP Conf Ser: Earth Environ Sci 258:012035","journal-title":"IOP Conf Ser: Earth Environ Sci"},{"key":"2215_CR26","doi-asserted-by":"publisher","first-page":"39413","DOI":"10.1109\/ACCESS.2021.3056441","volume":"9","author":"J Al Nahian","year":"2021","unstructured":"Al Nahian J et al (2021) Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9:39413\u201339431","journal-title":"IEEE Access"},{"key":"2215_CR27","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.procs.2018.10.189","volume":"141","author":"TB Rodrigues","year":"2018","unstructured":"Rodrigues TB et al (2018) Fall detection system by machine learning framework for public health. Procedia Comput Sci 141:358\u2013365","journal-title":"Procedia Comput Sci"},{"key":"2215_CR28","doi-asserted-by":"crossref","unstructured":"Soni M et al (2020) An Approach To Enhance Fall Detection Using Machine Learning Classifier. In the 12th International Conference on Computational Intelligence and Communication Networks","DOI":"10.1109\/CICN49253.2020.9242634"},{"key":"2215_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CCECE.2010.5575129","volume":"2010","author":"X Yang","year":"2010","unstructured":"Yang X et al (2010) A wearable real-time fall detector based on Naive Bayes classifier. CCECE 2010:1\u20134. https:\/\/doi.org\/10.1109\/CCECE.2010.5575129","journal-title":"CCECE"},{"issue":"8","key":"2215_CR30","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3390\/s16081161","volume":"16","author":"AT \u00d6zdemir","year":"2016","unstructured":"\u00d6zdemir AT (2016) An analysis on sensor locations of the human body for wearable fall detection devices: principles and practice. Sensors 16(8):1161. https:\/\/doi.org\/10.3390\/s16081161","journal-title":"Sensors"},{"key":"2215_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-81935-4","volume-title":"An introduction to machine learning","author":"M Kubat","year":"2021","unstructured":"Kubat M (2021) An introduction to machine learning, 3rd edn. Springer Nature Switzerland","edition":"3"},{"key":"2215_CR32","volume-title":"Data mining: practical machine learning tools and techniques","author":"I Witten","year":"2017","unstructured":"Witten I et al (2017) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Cambridge"},{"issue":"7","key":"2215_CR33","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.3390\/s17071513","volume":"17","author":"E Casilari","year":"2017","unstructured":"Casilari E et al (2017) Analysis of public datasets for wearable fall detection systems. Sensors 17(7):1513. https:\/\/doi.org\/10.3390\/s17071513","journal-title":"Sensors"},{"issue":"1","key":"2215_CR34","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3390\/s17010198","volume":"17","author":"A Sucerquia","year":"2017","unstructured":"Sucerquia A et al (2017) SisFall: A fall and movement dataset. Sensors 17(1):198. https:\/\/doi.org\/10.3390\/s17010198","journal-title":"Sensors"},{"key":"2215_CR35","unstructured":"Johansson V (n.d.) A Sensor Orientation and Signal Preprocessing Study of A Person Fall Detection Algorithms, BSc thesis, Faculty of Natural"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-023-02215-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-023-02215-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-023-02215-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T09:08:09Z","timestamp":1732871289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-023-02215-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2215"],"URL":"https:\/\/doi.org\/10.1007\/s11036-023-02215-6","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"29 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}