{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:09:04Z","timestamp":1786982944460,"version":"build-2736575974"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"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":["J Healthc Inform Res"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s41666-025-00215-7","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T17:50:45Z","timestamp":1759254645000},"page":"209-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Impact Detection in Fall Events: Leveraging Spatio-temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeleton Data"],"prefix":"10.1007","volume":"10","author":[{"given":"Tresor Y.","family":"Koffi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youssef","family":"Mourchid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Hindawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohan","family":"Dupuis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"issue":"2","key":"215_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.12968\/hmed.2019.0377","volume":"81","author":"E Mitchell","year":"2020","unstructured":"Mitchell E, Walker R (2020) Global ageing: successes, challenges and opportunities. Br J Hosp Med 81(2):1\u20139","journal-title":"Br J Hosp Med"},{"key":"215_CR2","doi-asserted-by":"crossref","unstructured":"Bourke AK, O\u2019brien J, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194\u2013199","DOI":"10.1016\/j.gaitpost.2006.09.012"},{"key":"215_CR3","doi-asserted-by":"crossref","unstructured":"Ximenes MAM, Oliveira IKM, Cavalcante FML, Neto NMG, Caetano J\u00c1, Barros LM (2024) Impact of educational intervention in the perception of hospitalised patients about the risk of falling and associated factors. Authorea Preprints","DOI":"10.22541\/au.170664545.53454604\/v1"},{"key":"215_CR4","doi-asserted-by":"crossref","unstructured":"Noury N, Fleury A, Rumeau P, Bourke AK, Laighin G, Rialle V, Lundy J-E (2007) Fall detection-principles and methods. In: 2007 29th Annual international conference of the IEEE engineering in medicine and biology society, pp 1663\u20131666. IEEE","DOI":"10.1109\/IEMBS.2007.4352627"},{"key":"215_CR5","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neucom.2011.09.037","volume":"100","author":"M Mubashir","year":"2013","unstructured":"Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100:144\u2013152","journal-title":"Neurocomputing"},{"key":"215_CR6","doi-asserted-by":"crossref","unstructured":"Wang X, Talavera E, Karastoyanova D, Azzopardi G (2023) Fall detection with a non-intrusive and first-person vision approach. IEEE Sensors J","DOI":"10.1109\/JSEN.2023.3314828"},{"key":"215_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.110870","volume":"192","author":"Y Yang","year":"2022","unstructured":"Yang Y, Yang H, Liu Z, Yuan Y, Guan X (2022) Fall detection system based on infrared array sensor and multi-dimensional feature fusion. Measurement 192:110870","journal-title":"Measurement"},{"key":"215_CR8","doi-asserted-by":"crossref","unstructured":"Wang Y, Sarvari PA, Khadraoui D (2024) Fusion of machine learning and threshold-based approaches for fall detection in healthcare using inertial sensors","DOI":"10.5220\/0012250500003657"},{"key":"215_CR9","doi-asserted-by":"publisher","first-page":"77702","DOI":"10.1109\/ACCESS.2019.2922708","volume":"7","author":"L Ren","year":"2019","unstructured":"Ren L, Peng Y (2019) Research of fall detection and fall prevention technologies: a systematic review. IEEE Access 7:77702\u201377722","journal-title":"IEEE Access"},{"issue":"3","key":"215_CR10","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1109\/TCDS.2019.2948786","volume":"12","author":"KN Kottari","year":"2019","unstructured":"Kottari KN, Delibasis KK, Maglogiannis IG (2019) Real-time fall detection using uncalibrated fisheye cameras. IEEE Trans Cogn Dev Syst 12(3):588\u2013600","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"215_CR11","doi-asserted-by":"crossref","unstructured":"Espinosa R, Ponce H, Guti\u00e9rrez S, Mart\u00ednez-Villase\u00f1or L, Brieva J, Moya-Albor E (2020) Application of convolutional neural networks for fall detection using multiple cameras. Challenges and trends in multimodal fall detection for healthcare, pp 97\u2013120","DOI":"10.1007\/978-3-030-38748-8_5"},{"issue":"24","key":"215_CR12","doi-asserted-by":"publisher","first-page":"3896","DOI":"10.3390\/math12243896","volume":"12","author":"X Ma","year":"2024","unstructured":"Ma X, Zhang L, Zhan J, Chang S (2024) Application of dual-stage attention temporal convolutional networks in gas well production prediction. Mathematics 12(24):3896","journal-title":"Mathematics"},{"issue":"24","key":"215_CR13","doi-asserted-by":"publisher","first-page":"8051","DOI":"10.3390\/s24248051","volume":"24","author":"D Kibet","year":"2024","unstructured":"Kibet D, So MS, Kang H, Han Y, Shin J-H (2024) Sudden fall detection of human body using transformer model. Sensors 24(24):8051","journal-title":"Sensors"},{"key":"215_CR14","first-page":"195","volume":"8","author":"Y Mourchid","year":"2016","unstructured":"Mourchid Y, Hassouni M, Cherifi H (2016) Image segmentation based on community detection approach. Int J Comput Inf Syst Ind Manag Appl 8:195\u2013204","journal-title":"Int J Comput Inf Syst Ind Manag Appl"},{"key":"215_CR15","doi-asserted-by":"crossref","unstructured":"Lafhel M, Cherifi H, Renoust B, El\u00a0Hassouni M, Mourchid Y (2021) Movie script similarity using multilayer network portrait divergence. In: Complex networks & their applications IX: Volume 1, Proceedings of the ninth international conference on complex networks and their applications complex networks 2020, pp 284\u2013295. Springer","DOI":"10.1007\/978-3-030-65347-7_24"},{"key":"215_CR16","doi-asserted-by":"crossref","unstructured":"Cherifi C, Cherifi H, Karsai M, Musolesi M (2017) Complex networks and their applications vi. In: Proceedings of complex networks 2017 the sixth international conference on complex networks and their applications. Berlin, Springer. Springer","DOI":"10.1007\/978-3-319-72150-7"},{"key":"215_CR17","doi-asserted-by":"publisher","first-page":"107420","DOI":"10.1016\/j.compbiomed.2023.107420","volume":"165","author":"Y Mourchid","year":"2023","unstructured":"Mourchid Y, Slama R (2023) D-STGCNT: a dense spatio-temporal graph Conv-GRU network based on transformer for assessment of patient physical rehabilitation. Comput Biol Med 165:107420","journal-title":"Comput Biol Med"},{"issue":"5","key":"215_CR18","doi-asserted-by":"publisher","first-page":"744","DOI":"10.3390\/sym12050744","volume":"12","author":"W Chen","year":"2020","unstructured":"Chen W, Jiang Z, Guo H, Ni X (2020) Fall detection based on key points of human-skeleton using OpenPose. Symmetry 12(5):744","journal-title":"Symmetry"},{"key":"215_CR19","doi-asserted-by":"publisher","first-page":"166117","DOI":"10.1109\/ACCESS.2020.3021943","volume":"8","author":"MM Islam","year":"2020","unstructured":"Islam MM, Tayan O, Islam MR, Islam MS, Nooruddin S, Kabir MN, Islam MR (2020) Deep learning based systems developed for fall detection: a review. IEEE Access 8:166117\u2013166137","journal-title":"IEEE Access"},{"issue":"1","key":"215_CR20","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.medengphy.2006.12.001","volume":"30","author":"AK Bourke","year":"2008","unstructured":"Bourke AK, Lyons GM (2008) A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 30(1):84\u201390","journal-title":"Med Eng Phys"},{"key":"215_CR21","doi-asserted-by":"crossref","unstructured":"Mekruksavanich S, Jantawong P, Jitpattanakul A (2022) Pre-impact fall detection based on wearable inertial sensors using hybrid deep residual neural network. In: 2022 6th International Conference on Information Technology (InCIT), pp 450\u2013453. IEEE","DOI":"10.1109\/InCIT56086.2022.10067733"},{"key":"215_CR22","doi-asserted-by":"publisher","unstructured":"Li Q, Stankovic JA, Hanson MA, Barth AT, Lach J, Zhou G (2009) Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In: Proceedings of the 2009 sixth international workshop on wearable and implantable body sensor networks. https:\/\/doi.org\/10.1109\/BSN.2009.46. IEEE","DOI":"10.1109\/BSN.2009.46"},{"key":"215_CR23","doi-asserted-by":"publisher","first-page":"205520762210741","DOI":"10.1177\/20552076221074128","volume":"8","author":"ACY Lim","year":"2022","unstructured":"Lim ACY, Natarajan P, Fonseka RD, Maharaj M, Mobbs RJ (2022) The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: a scoping review of literature. Digit Health 8:20552076221074130. https:\/\/doi.org\/10.1177\/20552076221074128","journal-title":"Digit Health"},{"key":"215_CR24","unstructured":"Li H, Ma J, Ren X, Wang K (2023) Novel fall detection algorithm based on multi-threshold fall model"},{"issue":"4","key":"215_CR25","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s11554-012-0246-9","volume":"9","author":"G Mastorakis","year":"2014","unstructured":"Mastorakis G, Makris D (2014) Fall detection system using Kinect\u2019s infrared sensor. J Real-Time Image Proc 9(4):635\u2013646. https:\/\/doi.org\/10.1007\/s11554-012-0246-9","journal-title":"J Real-Time Image Proc"},{"issue":"4","key":"215_CR26","doi-asserted-by":"publisher","first-page":"5113","DOI":"10.1007\/s11042-021-11646-w","volume":"81","author":"A De","year":"2022","unstructured":"De A, Saha A, Kumar P (2022) Fall detection approach based on combined displacement of spatial features for intelligent indoor surveillance. Multimedia Tools Appl 81(4):5113\u20135136. https:\/\/doi.org\/10.1007\/s11042-021-11646-w","journal-title":"Multimedia Tools Appl"},{"issue":"13","key":"215_CR27","doi-asserted-by":"publisher","first-page":"6889","DOI":"10.1109\/JSEN.2020.2975522","volume":"20","author":"A Singh","year":"2020","unstructured":"Singh A, Rehman SU, Yongchareon S, Chong PHJ (2020) Sensor technologies for fall detection systems: a review. IEEE Sens J 20(13):6889\u20136919. https:\/\/doi.org\/10.1109\/JSEN.2020.2975522","journal-title":"IEEE Sens J"},{"key":"215_CR28","doi-asserted-by":"publisher","first-page":"100130","DOI":"10.1016\/j.iot.2019.100130","volume":"9","author":"S Nooruddin","year":"2020","unstructured":"Nooruddin S, Islam MM, Sharna FA (2020) An IoT based device-type invariant fall detection system. Internet Things 9:100130","journal-title":"Internet Things"},{"key":"215_CR29","doi-asserted-by":"crossref","unstructured":"Wasi MWI, Dziyauddin RA, Amir NIM, Ahmad R (2022) Machine learning algorithm for fall classification using wearable device. In: 2022 IEEE Symposium on Future Telecommunication Technologies (SOFTT), pp 62\u201366. IEEE","DOI":"10.1109\/SOFTT56880.2022.10010102"},{"key":"215_CR30","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.procs.2022.07.005","volume":"203","author":"F Kausar","year":"2022","unstructured":"Kausar F, Awadalla M, Mesbah M, AlBadi T (2022) Automated machine learning based elderly fall detection classification. Procedia Comput Sci 203:16\u201323","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"215_CR31","doi-asserted-by":"publisher","first-page":"20","DOI":"10.3390\/s18010020","volume":"18","author":"IPES Putra","year":"2017","unstructured":"Putra IPES, Brusey J, Gaura E, Vesilo R (2017) An event-triggered machine learning approach for accelerometer-based fall detection. Sensors 18(1):20","journal-title":"Sensors"},{"key":"215_CR32","doi-asserted-by":"crossref","unstructured":"Droghini D, Ferretti D, Principi E, Squartini S, Piazza F et al (2017) A combined one-class svm and template-matching approach for user-aided human fall detection by means of floor acoustic features. Computational intelligence and neuroscience 2017","DOI":"10.1155\/2017\/1512670"},{"issue":"1","key":"215_CR33","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/10739149.2019.1648293","volume":"48","author":"I Kim","year":"2020","unstructured":"Kim I, Lee K-S, Kim K, Kim K, Chae H-S, Kim H-C (2020) Implementation of a real-time fall detection system for elderly Korean farmers using an insole-integrated sensing device. Instrum Sci Technol 48(1):22\u201342","journal-title":"Instrum Sci Technol"},{"issue":"18","key":"215_CR34","doi-asserted-by":"publisher","first-page":"26081","DOI":"10.1007\/s11042-022-11914-3","volume":"81","author":"A De","year":"2022","unstructured":"De A, Saha A, Kumar P, Pal G (2022) Fall detection method based on spatio-temporal feature fusion using combined two-channel classification. Multimedia Tools Appl 81(18):26081\u201326100. https:\/\/doi.org\/10.1007\/s11042-022-11914-3","journal-title":"Multimedia Tools Appl"},{"key":"215_CR35","doi-asserted-by":"crossref","unstructured":"Mir AA, Khalid AS, Musa S, Fauzi MFA, Razak NN, Tang TB (2025) Machine learning in ambient assisted living for enhanced elderly healthcare: a systematic literature review. IEEE Access","DOI":"10.1109\/ACCESS.2025.3580961"},{"issue":"4","key":"215_CR36","doi-asserted-by":"publisher","first-page":"3369","DOI":"10.1007\/s10462-021-10093-5","volume":"55","author":"L Miranda","year":"2022","unstructured":"Miranda L, Viterbo J, Bernardini F (2022) A survey on the use of machine learning methods in context-aware middlewares for human activity recognition. Artif Intell Rev 55(4):3369\u20133400. https:\/\/doi.org\/10.1007\/s10462-021-10093-5","journal-title":"Artif Intell Rev"},{"key":"215_CR37","doi-asserted-by":"crossref","unstructured":"Li P, Jiang M, Lin H, Lv X, Huang J (2025) Multidimensional time series segmentation of human activity without prior knowledge. IEEE Internet Things J","DOI":"10.1109\/JIOT.2025.3562749"},{"key":"215_CR38","doi-asserted-by":"crossref","unstructured":"Koffi TY, Mourchid Y, Hindawi M, Dupuis Y (2023) Machine learning and feature ranking for impact fall detection event using multisensor data. In: 2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP), pp 1\u20136. IEEE","DOI":"10.1109\/MMSP59012.2023.10337682"},{"key":"215_CR39","doi-asserted-by":"crossref","unstructured":"Shen L, Zhang Q, Cao G, Xu H (2019) Fall detection system based on deep learning and image processing in cloud environment. In: Complex, intelligent, and software intensive systems: Proceedings of the 12th international conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2018), pp 590\u2013598. Springer","DOI":"10.1007\/978-3-319-93659-8_53"},{"key":"215_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103242","volume":"71","author":"G \u015eeng\u00fcl","year":"2022","unstructured":"\u015eeng\u00fcl G, Karakaya M, Misra S, Abayomi-Alli OO, Dama\u0161evi\u010dius R (2022) Deep learning based fall detection using smartwatches for healthcare applications. Biomed Signal Process Control 71:103242","journal-title":"Biomed Signal Process Control"},{"key":"215_CR41","doi-asserted-by":"crossref","unstructured":"Xu J, He Z, Zhang Y (2019) Cnn-lstm combined network for iot enabled fall detection applications. In: Journal of physics: conference series, vol 1267, p 012044. IOP Publishing","DOI":"10.1088\/1742-6596\/1267\/1\/012044"},{"issue":"17","key":"215_CR42","doi-asserted-by":"publisher","first-page":"2746","DOI":"10.3390\/diagnostics13172746","volume":"13","author":"S Balasubramaniam","year":"2023","unstructured":"Balasubramaniam S, Velmurugan Y, Jaganathan D, Dhanasekaran S (2023) A modified lenet cnn for breast cancer diagnosis in ultrasound images. Diagnostics 13(17):2746","journal-title":"Diagnostics"},{"issue":"1","key":"215_CR43","doi-asserted-by":"publisher","first-page":"21537","DOI":"10.1038\/s41598-024-71545-6","volume":"14","author":"WS Almukadi","year":"2024","unstructured":"Almukadi WS, Alrowais F, Saeed MK, Yahya AE, Mahmud A, Marzouk R (2024) Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety. Sci Rep 14(1):21537","journal-title":"Sci Rep"},{"issue":"8","key":"215_CR44","doi-asserted-by":"publisher","DOI":"10.1115\/1.4043449","volume":"141","author":"TH Kim","year":"2019","unstructured":"Kim TH, Choi A, Heo HM, Kim K, Lee K, Mun JH (2019) Machine learning-based pre-impact fall detection model to discriminate various types of fall. J Biomech Eng 141(8):081010","journal-title":"J Biomech Eng"},{"key":"215_CR45","doi-asserted-by":"publisher","first-page":"63","DOI":"10.3389\/fbioe.2020.00063","volume":"8","author":"X Yu","year":"2020","unstructured":"Yu X, Qiu H, Xiong S (2020) A novel hybrid deep neural network to predict pre-impact fall for older people based on wearable inertial sensors. Front Bioeng Biotechnol 8:63","journal-title":"Front Bioeng Biotechnol"},{"key":"215_CR46","doi-asserted-by":"crossref","unstructured":"Turetta C, Ali MT, Demrozi F, Pravadelli G (2025) A lightweight cnn for real-time pre-impact fall detection. In: 2025 Design, Automation & Test in Europe conference (DATE), pp 1\u20137. IEEE","DOI":"10.23919\/DATE64628.2025.10993022"},{"key":"215_CR47","doi-asserted-by":"crossref","unstructured":"Chi T-H, Liu K-C, Hsieh C-Y, Tsao Y, Chan C-T (2023) Prefallkd: pre-impact fall detection via cnn-vit knowledge distillation. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10094979"},{"key":"215_CR48","unstructured":"Qu Z, Huang T, Ji Y, Li Y (2024) Physics sensor based deep learning fall detection system. arXiv:2403.06994"},{"issue":"8","key":"215_CR49","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.3390\/math11081965","volume":"11","author":"Z Wang","year":"2023","unstructured":"Wang Z, Lv Y, Wu R, Zhang Y (2023) Review of grabcut in image processing. Mathematics 11(8):1965","journal-title":"Mathematics"},{"key":"215_CR50","unstructured":"Vyas P (2019) Pose estimation and action recognition in sports and fitness"},{"key":"215_CR51","unstructured":"Grishchenko I, Bazarevsky V, Zanfir A, Bazavan EG, Zanfir M, Yee R, Raveendran K, Zhdanovich M, Grundmann M, Sminchisescu C (2022) Blazepose ghum holistic: real-time 3d human landmarks and pose estimation. arXiv:2206.11678"},{"key":"215_CR52","unstructured":"Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, Zhang F, Chang C-L, Yong MG, Lee J et al (2019) Mediapipe: a framework for building perception pipelines. arXiv:1906.08172"},{"key":"215_CR53","unstructured":"Bazarevsky V, Grishchenko I, Raveendran K, Zhu T, Zhang F, Grundmann M (2020) Blazepose: on-device real-time body pose tracking. arXiv:2006.10204"},{"key":"215_CR54","unstructured":"Mart\u0131nez GH (2019) Openpose: whole-body pose estimation. PhD thesis, Carnegie Mellon University Pittsburgh, PA, USA"},{"issue":"6","key":"215_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2020.2996746","volume":"4","author":"C-L Lin","year":"2020","unstructured":"Lin C-L, Chiu W-C, Chen F-H, Ho Y-H, Chu T-C, Hsieh P-H (2020) Fall monitoring for the elderly using wearable inertial measurement sensors on eyeglasses. IEEE Sensors Lett 4(6):1\u20134","journal-title":"IEEE Sensors Lett"},{"issue":"21","key":"215_CR56","doi-asserted-by":"publisher","first-page":"11031","DOI":"10.3390\/app122111031","volume":"12","author":"SS Jeong","year":"2022","unstructured":"Jeong SS, Kim NH, Yu YS (2022) Fall detection system based on simple threshold method and long short-term memory: comparison with hidden Markov model and extraction of optimal parameters. Appl Sci 12(21):11031","journal-title":"Appl Sci"},{"issue":"2","key":"215_CR57","first-page":"80","volume":"10","author":"HA Abdo","year":"2023","unstructured":"Abdo HA, Amin K, Hamad AM (2023) Human fall detection using spatial temporal graph convolutional networks. IJCI Int J Comput Inform 10(2):80\u201398","journal-title":"IJCI Int J Comput Inform"},{"key":"215_CR58","doi-asserted-by":"publisher","first-page":"28224","DOI":"10.1109\/ACCESS.2021.3058219","volume":"9","author":"O Keskes","year":"2021","unstructured":"Keskes O, Noumeir R (2021) Vision-based fall detection using st-gcn. IEEE Access 9:28224\u201328236","journal-title":"IEEE Access"},{"issue":"9","key":"215_CR59","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.3390\/s19091988","volume":"19","author":"L Mart\u00ednez-Villase\u00f1or","year":"2019","unstructured":"Mart\u00ednez-Villase\u00f1or L, Ponce H, Brieva J, Moya-Albor E, N\u00fa\u00f1ez-Mart\u00ednez J, Pe\u00f1afort-Asturiano C (2019) Up-fall detection dataset: a multimodal approach. Sensors 19(9):1988","journal-title":"Sensors"},{"key":"215_CR60","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.procs.2017.06.110","volume":"110","author":"E Casilari","year":"2017","unstructured":"Casilari E, Santoyo-Ram\u00f3n JA, Cano-Garc\u00eda JM (2017) Umafall: a multisensor dataset for the research on automatic fall detection. Procedia Comput Sci 110:32\u201339","journal-title":"Procedia Comput Sci"},{"issue":"6","key":"215_CR61","first-page":"599","volume":"12","author":"\u017d Vujovi\u0107","year":"2021","unstructured":"Vujovi\u0107 \u017d et al (2021) Classification model evaluation metrics. Int J Adv Comput Sci Appl 12(6):599\u2013606","journal-title":"Int J Adv Comput Sci Appl"},{"key":"215_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genom 21:1\u201313","journal-title":"BMC Genom"},{"issue":"6","key":"215_CR63","doi-asserted-by":"publisher","first-page":"18091","DOI":"10.1007\/s11042-023-16214-y","volume":"83","author":"TV Ha","year":"2024","unstructured":"Ha TV, Nguyen HM, Thanh SH, Nguyen BT (2024) Fall detection using mixtures of convolutional neural networks. Multimedia Tools Appl 83(6):18091\u201318118","journal-title":"Multimedia Tools Appl"},{"key":"215_CR64","unstructured":"Castro F, Dentamaro V, Gattulli V, Impedovo D (2023) Fall detection with lstm and attention mechanism. In: WAMWB@ MobileHCI, pp 37\u201350"},{"key":"215_CR65","doi-asserted-by":"crossref","unstructured":"Eltahir MM, Yousif A, Alrowais F, Nour MK, Marzouk R, Dafaalla H, Hassan\u00a0Elnour AA, Aziz ASA, Hamza MA (2023) Deep transfer learning-enabled activity identification and fall detection for disabled people. Comput Mater Contin 75(2)","DOI":"10.32604\/cmc.2023.034037"},{"issue":"6","key":"215_CR66","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.14716\/ijtech.v13i6.5840","volume":"13","author":"XL Lau","year":"2022","unstructured":"Lau XL, Connie T, Goh MKO, Lau SH (2022) Fall detection and motion analysis using visual approaches. Int J Technol 13(6):1173\u20131182","journal-title":"Int J Technol"},{"key":"215_CR67","unstructured":"Bal\u0131n MF, Abid A, Zou J (2019) Concrete autoencoders: differentiable feature selection and reconstruction. In: International conference on machine learning, pp 444\u2013453. PMLR"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00215-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-025-00215-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00215-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T08:57:39Z","timestamp":1770195459000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-025-00215-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"references-count":67,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["215"],"URL":"https:\/\/doi.org\/10.1007\/s41666-025-00215-7","relation":{},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"value":"2509-4971","type":"print"},{"value":"2509-498X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,30]]},"assertion":[{"value":"27 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}