{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T17:51:21Z","timestamp":1772473881051,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Projects of Sichuan","award":["2021YFSY0059"],"award-info":[{"award-number":["2021YFSY0059"]}]},{"name":"Science and Technology Projects of Sichuan","award":["2021YFQ0055"],"award-info":[{"award-number":["2021YFQ0055"]}]},{"name":"Science and Technology Projects of Chengdu","award":["2022-YF05-00379-SN."],"award-info":[{"award-number":["2022-YF05-00379-SN."]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09334-x","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T14:01:56Z","timestamp":1703599316000},"page":"5093-5108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Fall detection on embedded platform using infrared array sensor for healthcare applications"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9561-3878","authenticated-orcid":false,"given":"Yan","family":"Jiang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4818-3091","authenticated-orcid":false,"given":"Tianyi","family":"Gong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3681-8042","authenticated-orcid":false,"given":"Lingfeng","family":"He","sequence":"additional","affiliation":[]},{"given":"Shicheng","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xiaoping","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jianyang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"9334_CR1","doi-asserted-by":"crossref","unstructured":"Kamel MH, Abdulmajeed AA, Sally EI (2013) Risk factors of falls among elderly living in Urban Suez-Egypt. Pan Afr Med J 14(1)","DOI":"10.11604\/pamj.2013.14.26.1609"},{"key":"9334_CR2","doi-asserted-by":"crossref","unstructured":"Xing X-Y et al. (2020) Mortality and disease burden of injuries from 2008 to 2017 in Anhui Province, China. BioMed Res Int 2020","DOI":"10.1155\/2020\/7303897"},{"key":"9334_CR3","doi-asserted-by":"publisher","unstructured":"Ojetola O, Gaura EI, Brusey J (2011) Fall detection with wearable sensors-safe (Smart fall detection). In: Seventh international conference on intelligent environments, pp 318\u2013321. https:\/\/doi.org\/10.1109\/IE.2011.38","DOI":"10.1109\/IE.2011.38"},{"issue":"16","key":"9334_CR4","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.1016\/j.jbiomech.2008.08.009","volume":"41","author":"MN Nyan","year":"2008","unstructured":"Nyan MN, Tay FE, Murugasu E (2008) A wearable system for pre-impact fall detection. J Biomech 41(16):3475\u20133481","journal-title":"J Biomech"},{"issue":"1","key":"9334_CR5","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s11277-020-07366-3","volume":"114","author":"KY Tai","year":"2020","unstructured":"Tai KY et al (2020) Smart fall prediction for elderly care using iPhone and apple watch. Wirel Pers Commun 114(1):347\u2013365","journal-title":"Wirel Pers Commun"},{"key":"9334_CR6","doi-asserted-by":"publisher","unstructured":"Torti E et al. (2018) Embedded real-time fall detection with deep learning on wearable devices. In: 2018 21st Euromicro conference on digital system design (DSD), pp 405\u2013412. https:\/\/doi.org\/10.1109\/DSD.2018.00075","DOI":"10.1109\/DSD.2018.00075"},{"key":"9334_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3181701","author":"Z Qian","year":"2022","unstructured":"Qian Z et al (2022) Development of a real time wearable fall detection system in the context of internet of things. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2022.3181701","journal-title":"IEEE Internet Things J"},{"key":"9334_CR8","doi-asserted-by":"publisher","first-page":"121034","DOI":"10.1016\/j.eswa.2023.121034","volume":"234","author":"JK Kim","year":"2023","unstructured":"Kim JK, Kangbok L, Sang GH (2023) Detection of important features and comparison of datasets for fall detection based on wrist-wearable devices. Expert Syst Appl 234:121034","journal-title":"Expert Syst Appl"},{"issue":"12","key":"9334_CR9","doi-asserted-by":"publisher","first-page":"5082","DOI":"10.1109\/JSEN.2018.2829815","volume":"18","author":"T de Quadros","year":"2018","unstructured":"de Quadros T, Lazzaretti AE, Schneider FK (2018) A movement decomposition and machine learning-based fall detection system using wrist wearable device. IEEE Sens J 18(12):5082\u20135089. https:\/\/doi.org\/10.1109\/JSEN.2018.2829815","journal-title":"IEEE Sens J"},{"key":"9334_CR10","doi-asserted-by":"publisher","unstructured":"Mokhtari G, Zhang Q, Fazlollahi A (2017) Non-wearable UWB sensor to detect falls in smart home environment. In: IEEE international conference on pervasive computing and communications workshops (PerCom Workshops), pp 274\u2013278. https:\/\/doi.org\/10.1109\/PERCOMW.2017.7917571","DOI":"10.1109\/PERCOMW.2017.7917571"},{"issue":"4","key":"9334_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3161183","volume":"1","author":"S Palipana","year":"2018","unstructured":"Palipana S et al (2018) FallDeFi: ubiquitous fall detection using commodity Wi-Fi devices. Proc ACM Interact, Mob, Wearable Ubiquitous Technol 1(4):1\u201325","journal-title":"Proc ACM Interact, Mob, Wearable Ubiquitous Technol"},{"key":"9334_CR12","doi-asserted-by":"publisher","unstructured":"Youngkong P, Panpanyatep W (2021) A novel double pressure sensors-based monitoring and alarming system for fall detection. In: 2021 second international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP), pp.1\u20135. https:\/\/doi.org\/10.1109\/ICA-SYMP50206.2021.9358439","DOI":"10.1109\/ICA-SYMP50206.2021.9358439"},{"key":"9334_CR13","doi-asserted-by":"publisher","first-page":"118681","DOI":"10.1016\/j.eswa.2022.118681","volume":"212","author":"M Amsaprabhaa","year":"2023","unstructured":"Amsaprabhaa M (2023) Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection. Expert Syst Appl 212:118681","journal-title":"Expert Syst Appl"},{"key":"9334_CR14","doi-asserted-by":"crossref","unstructured":"Chen Z, Yiye W, Wankou Y (2022) Video based fall detection using human poses. In: CCF conference on big data. Singapore, Springer Singapore","DOI":"10.1007\/978-981-16-9709-8_19"},{"key":"9334_CR15","doi-asserted-by":"publisher","unstructured":"Chua J-L, Chang YC, Lim WK (2013) Visual based fall detection through human shape variation and head detection. IMPACT-2013, pp 61\u201365. https:\/\/doi.org\/10.1109\/MSPCT.2013.6782088","DOI":"10.1109\/MSPCT.2013.6782088"},{"issue":"2","key":"9334_CR16","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1007\/s00521-021-06495-5","volume":"34","author":"S Saurav","year":"2022","unstructured":"Saurav S, Saini R, Singh S (2022) A dual-stream fused neural network for fall detection in multi-camera and $$360^{\\circ }$$ videos. Neural Comput Appl 34(2):1455\u20131482","journal-title":"Neural Comput Appl"},{"issue":"23","key":"9334_CR17","doi-asserted-by":"publisher","first-page":"17245","DOI":"10.1007\/s00521-020-05208-8","volume":"32","author":"SV Georgakopoulos","year":"2020","unstructured":"Georgakopoulos SV et al (2020) Change detection and convolution neural networks for fall recognition. Neural Comput Appl 32(23):17245\u201317258","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9334_CR18","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1109\/JBHI.2014.2312180","volume":"19","author":"EE Stone","year":"2014","unstructured":"Stone EE, Skubic M (2014) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Health Inform 19(1):290\u2013301","journal-title":"IEEE J Biomed Health Inform"},{"key":"9334_CR19","doi-asserted-by":"publisher","unstructured":"Mashiyama S, Hong J, Ohtsuki T (2014) A fall detection system using low resolution infrared array sensor. In: 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), pp 2109\u20132113. https:\/\/doi.org\/10.1109\/PIMRC.2014.7136520","DOI":"10.1109\/PIMRC.2014.7136520"},{"key":"9334_CR20","doi-asserted-by":"crossref","unstructured":"Hayashida A, Vasily M, Koji H (2017) New approach for indoor fall detection by infrared thermal array sensor. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE","DOI":"10.1109\/MWSCAS.2017.8053196"},{"key":"9334_CR21","unstructured":"Tzeng H-W, Mei-Yung C, Jai-Yu C (2010) Design of fall detection system with floor pressure and infrared image. In: 2010 international conference on system science and engineering. IEEE"},{"issue":"16","key":"9334_CR22","first-page":"9558","volume":"20","author":"Z Liu","year":"2020","unstructured":"Liu Z et al (2020) Fall detection and personnel tracking system using infrared array sensors. IEEE Sens J 20(16):9558\u20139566","journal-title":"IEEE Sens J"},{"key":"9334_CR23","doi-asserted-by":"publisher","unstructured":"Spasov G, Tsvetkov V, Petrova G (2019) Using IR array MLX90640 to build an IoT solution for ALL and security smart systems. In: IEEE XXVIII international scientific conference electronics (ET), pp 1\u20134. https:\/\/doi.org\/10.1109\/ET.2019.8878637","DOI":"10.1109\/ET.2019.8878637"},{"issue":"14","key":"9334_CR24","doi-asserted-by":"publisher","first-page":"7268","DOI":"10.3390\/app12147268","volume":"12","author":"H Tang","year":"2022","unstructured":"Tang H et al (2022) An improved algorithm for low-light image enhancement based on retinexnet. Appl Sci 12(14):7268","journal-title":"Appl Sci"},{"key":"9334_CR25","doi-asserted-by":"publisher","unstructured":"Wei C , Wang W , Yang W, et al. (2018) Deep retinex decomposition for low-light enhancement. https:\/\/doi.org\/10.48550\/arXiv.1808.04560[P]","DOI":"10.48550\/arXiv.1808.04560[P]"},{"issue":"6","key":"9334_CR26","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","volume":"237","author":"EH Land","year":"1977","unstructured":"Land EH (1977) The retinex theory of color vision. Sci Am 237(6):108\u2013129","journal-title":"Sci Am"},{"key":"9334_CR27","doi-asserted-by":"crossref","unstructured":"Han K et al. (2020) Ghostnet: more features from cheap operations. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"9334_CR28","doi-asserted-by":"crossref","unstructured":"Woo S, Jongchan P, Joon-Young L, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"9334_CR29","unstructured":"Liu Y, Guolei S, Yu Q, Le Z, Ajad C, Luc VG (2021) Transformer in convolutional neural networks. arXiv preprint arXiv:2106.03180"},{"key":"9334_CR30","unstructured":"Thuan D (2021) Evolution of Yolo algorithm and Yolov5: the state-of-the-art object detention algorithm"},{"key":"9334_CR31","doi-asserted-by":"crossref","unstructured":"Zhu X, Shuchang L, Xu W, Qi Z (2021) TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2778\u20132788","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"9334_CR32","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, No(07), pp 12993\u201313000","DOI":"10.1609\/aaai.v34i07.6999"},{"issue":"4","key":"9334_CR33","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1049\/ipr2.12392","volume":"16","author":"B Ma","year":"2022","unstructured":"Ma B, Yongkang F, Wang C, Li J, Wang Yuli (2022) A high-performance insulators location scheme based on YOLOv4 deep learning network with GDIoU loss function. IET Image Proc 16(4):1124\u20131134","journal-title":"IET Image Proc"},{"key":"9334_CR34","doi-asserted-by":"crossref","unstructured":"Henderson P, Ferrari V(2017) End-to-end training of object class detectors for mean average precision. Asian conference on computer vision. Springer, Cham","DOI":"10.1007\/978-3-319-54193-8_13"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09334-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09334-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09334-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T10:15:37Z","timestamp":1708164937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09334-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9334"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09334-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"11 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","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"}}]}}