{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T07:16:57Z","timestamp":1765178217628,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973185"],"award-info":[{"award-number":["61973185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF097"],"award-info":[{"award-number":["ZR2020MF097"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Colleges and Universities Twenty Terms Foundation of Jinan City","award":["2021GXRC100"],"award-info":[{"award-number":["2021GXRC100"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evolving Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12530-024-09601-9","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:02:20Z","timestamp":1719878540000},"page":"1957-1970","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fall detection algorithm based on pyramid network and feature fusion"],"prefix":"10.1007","volume":"15","author":[{"given":"Jiangjiao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengqi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4028-0938","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"9601_CR1","doi-asserted-by":"publisher","first-page":"108258","DOI":"10.1016\/j.measurement.2020.108258","volume":"167","author":"A Alarifi","year":"2021","unstructured":"Alarifi A, Alwadain A (2021) Killer heuristic optimized convolution neural network-based fall detection with wearable iot sensor devices. Measurement 167:108258\u2013108267. https:\/\/doi.org\/10.1016\/j.measurement.2020.108258","journal-title":"Measurement"},{"key":"9601_CR2","doi-asserted-by":"publisher","first-page":"118681","DOI":"10.1016\/j.eswa.2022.118681","volume":"212","author":"M Amsaprabhaa","year":"2022","unstructured":"Amsaprabhaa M (2022) Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection. Expert Syst Appl 212:118681\u2013118695. https:\/\/doi.org\/10.1016\/j.eswa.2022.118681","journal-title":"Expert Syst Appl"},{"key":"9601_CR3","doi-asserted-by":"publisher","first-page":"103407","DOI":"10.1016\/j.jvcir.2021.103407","volume":"82","author":"DR Beddiar","year":"2022","unstructured":"Beddiar DR, Oussalah M, Nini B (2022) Fall detection using body geometry and human pose estimation in video sequences. J Vis Commun Image Represent 82:103407\u2013103419. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103407","journal-title":"J Vis Commun Image Represent"},{"issue":"10","key":"9601_CR4","doi-asserted-by":"publisher","first-page":"3252","DOI":"10.1007\/s10489-020-01716-1","volume":"50","author":"A Belhadi","year":"2020","unstructured":"Belhadi A, Djenouri Y, Djenouri D et al (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50(10):3252\u20133265. https:\/\/doi.org\/10.1007\/s10489-020-01716-1","journal-title":"Appl Intell"},{"key":"9601_CR5","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE et al (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1302\u20131310","DOI":"10.1109\/CVPR.2017.143"},{"key":"9601_CR6","doi-asserted-by":"crossref","unstructured":"Charfi I, Miteran J, Dubois J et al (2012) Definition and performance evaluation of a robust SVM based fall detection solution. In: 2012 eighth international conference on signal image technology and internet based Systems, pp 218\u2013224","DOI":"10.1109\/SITIS.2012.155"},{"issue":"18","key":"9601_CR7","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 et al (2022) Fall detection method based on spatio-temporal feature fusion using combined two-channel classification. Multimed Tools Appl 81(18):26081\u201326100. https:\/\/doi.org\/10.1007\/s11042-022-11914-3","journal-title":"Multimed Tools Appl"},{"key":"9601_CR8","doi-asserted-by":"crossref","unstructured":"Dentamaro V, Impedovo D, Pirlo G (2021) Fall detection by human pose estimation and kinematic theory. In: 2020 25th international conference on pattern recognition (ICPR), pp 2328\u20132335","DOI":"10.1109\/ICPR48806.2021.9413331"},{"key":"9601_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-022-02416-2","author":"K Fei","year":"2022","unstructured":"Fei K, Wang C, Zhang J et al (2022) Flow-pose net: an effective two-stream network for fall detection. Vis Comput. https:\/\/doi.org\/10.1007\/s00371-022-02416-2","journal-title":"Vis Comput"},{"key":"9601_CR10","doi-asserted-by":"publisher","first-page":"114226","DOI":"10.1016\/j.eswa.2020.114226","volume":"168","author":"YM Galv\u00e3o","year":"2021","unstructured":"Galv\u00e3o YM, Ferreira J, Albuquerque VA et al (2021) A multimodal approach using deep learning for fall detection. Expert Syst Appl 168:114226\u2013114234. https:\/\/doi.org\/10.1016\/j.eswa.2020.114226","journal-title":"Expert Syst Appl"},{"key":"9601_CR11","doi-asserted-by":"publisher","first-page":"103375","DOI":"10.1016\/j.jvcir.2021.103375","volume":"82","author":"B Hadjadji","year":"2022","unstructured":"Hadjadji B, Saumard M, Aron M (2022) Multi-oriented run length based static and dynamic features fused with choquet fuzzy integral for human fall detection in videos. J Vis Commun Image Represent 82:103375\u2013103388. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103375","journal-title":"J Vis Commun Image Represent"},{"issue":"15","key":"9601_CR12","doi-asserted-by":"publisher","first-page":"16969","DOI":"10.1109\/JSEN.2021.3079835","volume":"21","author":"K Hanifi","year":"2021","unstructured":"Hanifi K, Karsligil ME (2021) Elderly fall detection with vital signs monitoring using cw doppler radar. IEEE Sens J 21(15):16969\u201316978. https:\/\/doi.org\/10.1109\/JSEN.2021.3079835","journal-title":"IEEE Sens J"},{"key":"9601_CR13","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 et al (2020) Deep learning based systems developed for fall detection: a review. IEEE Access 8:166117\u2013166137. https:\/\/doi.org\/10.1109\/ACCESS.2020.3021943","journal-title":"IEEE Access"},{"key":"9601_CR14","doi-asserted-by":"publisher","first-page":"104514","DOI":"10.1016\/j.micpro.2022.104514","volume":"91","author":"O Kerdjidj","year":"2022","unstructured":"Kerdjidj O, Boutellaa E, Amira A et al (2022) A hardware framework for fall detection using inertial sensors and compressed sensing. Microprocess Microsyst 91:104514\u2013104521. https:\/\/doi.org\/10.1016\/j.micpro.2022.104514","journal-title":"Microprocess Microsyst"},{"issue":"3","key":"9601_CR15","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.cmpb.2014.09.005","volume":"117","author":"B Kwolek","year":"2014","unstructured":"Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Progr Biomed 117(3):489\u2013501. https:\/\/doi.org\/10.1016\/j.cmpb.2014.09.005","journal-title":"Comput Methods Progr Biomed"},{"key":"9601_CR16","doi-asserted-by":"crossref","unstructured":"Liu Z, Ning J, Cao Y et al (2022) Video swin transformer. In: 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR),  pp 3192\u20133201","DOI":"10.1109\/CVPR52688.2022.00320"},{"key":"9601_CR17","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1016\/j.procs.2021.12.305","volume":"198","author":"N Mamchur","year":"2022","unstructured":"Mamchur N, Shakhovska N, Gregusml M (2022) Person fall detection system based on video stream analysis. Procedia Comput Sci 198:676\u2013681. https:\/\/doi.org\/10.1016\/j.procs.2021.12.305","journal-title":"Procedia Comput Sci"},{"key":"9601_CR18","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.neucom.2021.04.138","volume":"491","author":"M Nasir","year":"2022","unstructured":"Nasir M, Muhammad K, Ullah A et al (2022) Enabling automation and edge intelligence over resource constraint iot devices for smart home. Neurocomputing 491:494\u2013506. https:\/\/doi.org\/10.1016\/j.neucom.2021.04.138","journal-title":"Neurocomputing"},{"key":"9601_CR19","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. https:\/\/doi.org\/10.1109\/ACCESS.2019.2922708","journal-title":"IEEE Access"},{"key":"9601_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2021.103242","volume":"71","author":"G \u015eeng\u00fcl","year":"2022","unstructured":"\u015eeng\u00fcl G, Karakaya M, Misra S et al (2022) Deep learning based fall detection using smartwatches for healthcare applications. Biomed Signal Process Control 71:1\u201313. https:\/\/doi.org\/10.1016\/j.bspc.2021.103242","journal-title":"Biomed Signal Process Control"},{"key":"9601_CR21","doi-asserted-by":"publisher","first-page":"104431","DOI":"10.1016\/j.imavis.2022.104431","volume":"122","author":"PK Soni","year":"2022","unstructured":"Soni PK, Choudhary A (2022) Grassmann manifold based framework for automated fall detection from a camera. Image Vis Comput 122:104431\u2013104439. https:\/\/doi.org\/10.1016\/j.imavis.2022.104431","journal-title":"Image Vis Comput"},{"key":"9601_CR22","unstructured":"Vaswani A, Shazeer N, Parmar N et\u00a0al (2017) Attention is all you need. CoRR. https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"9601_CR23","doi-asserted-by":"publisher","first-page":"108876","DOI":"10.1109\/ACCESS.2023.3321192","volume":"11","author":"SQ Wahla","year":"2023","unstructured":"Wahla SQ, Ghani MU (2023) Visual fall detection from activities of daily living for assistive living. IEEE Access 11:108876\u2013108890. https:\/\/doi.org\/10.1109\/ACCESS.2023.3321192","journal-title":"IEEE Access"},{"key":"9601_CR24","doi-asserted-by":"publisher","first-page":"103443","DOI":"10.1109\/ACCESS.2020.2999503","volume":"8","author":"BH Wang","year":"2020","unstructured":"Wang BH, Yu J, Wang K et al (2020) Fall detection based on dual-channel feature integration. IEEE Access 8:103443\u2013103453. https:\/\/doi.org\/10.1109\/ACCESS.2020.2999503","journal-title":"IEEE Access"},{"issue":"10","key":"9601_CR25","doi-asserted-by":"publisher","first-page":"9824","DOI":"10.1109\/JSEN.2022.3165188","volume":"22","author":"B Wang","year":"2022","unstructured":"Wang B, Zheng Z, Guo YX (2022a) Millimeter-wave frequency modulated continuous wave radar-based soft fall detection using pattern contour-confined doppler-time maps. IEEE Sens J 22(10):9824\u20139831. https:\/\/doi.org\/10.1109\/JSEN.2022.3165188","journal-title":"IEEE Sens J"},{"key":"9601_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06795-w","author":"P Wang","year":"2022","unstructured":"Wang P, Li Q, Yin P et al (2022b) A convolution neural network approach for fall detection based on adaptive channel selection of uwb radar signals. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06795-w","journal-title":"Neural Comput Appl"},{"issue":"6","key":"9601_CR27","doi-asserted-by":"publisher","first-page":"4862","DOI":"10.1109\/TSG.2022.3204796","volume":"13","author":"X Wei","year":"2022","unstructured":"Wei X, Li Y, Li Y et al (2022) Detection of false data injection attacks in smart grid: a secure federated deep learning approach. IEEE Trans Smart Grid 13(6):4862\u20134872. https:\/\/doi.org\/10.1109\/TSG.2022.3204796","journal-title":"IEEE Trans Smart Grid"},{"key":"9601_CR28","doi-asserted-by":"publisher","first-page":"103355","DOI":"10.1016\/j.bspc.2021.103355","volume":"72","author":"X Wu","year":"2022","unstructured":"Wu X, Zheng Y, Chu CH et al (2022) Applying deep learning technology for automatic fall detection using mobile sensors. Biomed Signal Process Control 72:103355\u2013103363. https:\/\/doi.org\/10.1016\/j.bspc.2021.103355","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"9601_CR29","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1109\/TCSVT.2023.3303258","volume":"34","author":"L Wu","year":"2024","unstructured":"Wu L, Huang C, Fei L et al (2024) Video-based fall detection using human pose and constrained generative adversarial network. IEEE Trans Circuits Syst Video Technol 34(4):2179\u20132194. https:\/\/doi.org\/10.1109\/TCSVT.2023.3303258","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"9601_CR30","doi-asserted-by":"crossref","unstructured":"Xiao Z, Zhang H, Tong H et al (2022) An efficient temporal network with dual self-distillation for electroencephalography signal classification. In: 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 1759\u20131762","DOI":"10.1109\/BIBM55620.2022.9995049"},{"key":"9601_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3201203","volume":"71","author":"H Xing","year":"2021","unstructured":"Xing H, Xiao Z, Qu R et al (2021) An efficient federated distillation learning system for multitask time series classification. IEEE Trans Instrum Meas 71:1\u201312. https:\/\/doi.org\/10.1109\/TIM.2022.3201203","journal-title":"IEEE Trans Instrum Meas"},{"issue":"11","key":"9601_CR32","doi-asserted-by":"publisher","first-page":"8583","DOI":"10.1002\/int.22957","volume":"37","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Zhan D et al (2022) Selfmatch: robust semisupervised time-series classification with self-distillation. Int J Intell Syst 37(11):8583\u20138610. https:\/\/doi.org\/10.1002\/int.22957","journal-title":"Int J Intell Syst"},{"issue":"10","key":"9601_CR33","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1007\/s10489-020-01751-y","volume":"50","author":"X Xiong","year":"2020","unstructured":"Xiong X, Min W, Zheng WS et al (2020) S3d-cnn: skeleton-based 3d consecutive-low-pooling neural network for fall detection. Appl Intell 50(10):3521\u20133534. https:\/\/doi.org\/10.1007\/s10489-020-01751-y","journal-title":"Appl Intell"},{"key":"9601_CR34","doi-asserted-by":"publisher","first-page":"107948","DOI":"10.1016\/j.knosys.2021.107948","volume":"239","author":"SK Yadav","year":"2022","unstructured":"Yadav SK, Luthra A, Tiwari K et al (2022) Arfdnet: an efficient activity recognition & fall detection system using latent feature pooling. Knowl Based Syst 239:107948\u2013107958. https:\/\/doi.org\/10.1016\/j.knosys.2021.107948","journal-title":"Knowl Based Syst"},{"key":"9601_CR35","doi-asserted-by":"publisher","first-page":"110870","DOI":"10.1016\/j.measurement.2022.110870","volume":"192","author":"Y Yang","year":"2022","unstructured":"Yang Y, Yang H, Liu Z et al (2022) Fall detection system based on infrared array sensor and multi-dimensional feature fusion. Measurement 192:110870\u2013110879. https:\/\/doi.org\/10.1016\/j.measurement.2022.110870","journal-title":"Measurement"},{"key":"9601_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3175894","author":"Y Yao","year":"2022","unstructured":"Yao Y, Liu C, Zhang H et al (2022) Fall detection system using millimeter wave radar based on neural network and information fusion. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2022.3175894","journal-title":"IEEE Internet Things J"},{"issue":"6","key":"9601_CR37","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s11554-023-01377-6","volume":"20","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Gan J, Zhao Z et al (2023) A real-time fall detection model based on blazepose and improved st-gcn. J Real Time Image Process 20(6):121. https:\/\/doi.org\/10.1007\/s11554-023-01377-6","journal-title":"J Real Time Image Process"},{"issue":"3","key":"9601_CR38","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1007\/s10489-021-02575-0","volume":"52","author":"W Zou","year":"2022","unstructured":"Zou W, Zhang D, Lee DJ (2022) A new multi-feature fusion based convolutional neural network for facial expression recognition. Appl Intell 52(3):2918\u20132929. https:\/\/doi.org\/10.1007\/s10489-021-02575-0","journal-title":"Appl Intell"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09601-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-024-09601-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-024-09601-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T20:04:34Z","timestamp":1724443474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-024-09601-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,1]]},"references-count":38,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["9601"],"URL":"https:\/\/doi.org\/10.1007\/s12530-024-09601-9","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"type":"print","value":"1868-6478"},{"type":"electronic","value":"1868-6486"}],"subject":[],"published":{"date-parts":[[2024,7,1]]},"assertion":[{"value":"9 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2024","order":3,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}