{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:30:00Z","timestamp":1776357000518,"version":"3.51.2"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["11801542"],"award-info":[{"award-number":["11801542"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["U1913210"],"award-info":[{"award-number":["U1913210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"shenzhen science and technology projects","award":["JCYJ20170818163445670"],"award-info":[{"award-number":["JCYJ20170818163445670"]}]},{"name":"shenzhen science and technology projects","award":["JCYJ20180703145002040"],"award-info":[{"award-number":["JCYJ20180703145002040"]}]},{"name":"strategic priority research program of chinese academy of sciences","award":["XDB 38040200"],"award-info":[{"award-number":["XDB 38040200"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.<\/jats:p>","DOI":"10.1007\/s00521-021-06795-w","type":"journal-article","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T07:16:29Z","timestamp":1641021389000},"page":"15967-15980","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals"],"prefix":"10.1007","volume":"35","author":[{"given":"Ping","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qimeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raffaele","family":"Gravina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ye","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"6795_CR1","unstructured":"United Nations (2019) World Population Prospects 2019, [Online]. Available: https:\/\/www.un.org\/development\/desa\/pd\/node\/1114"},{"key":"6795_CR2","unstructured":"World Health Organization (2018) WHO Global Report on Falls Prevention in Older Age"},{"key":"6795_CR3","doi-asserted-by":"crossref","unstructured":"Mubashir M, Shao L, Seed L (2013) A survey on fall detection: Principles and approaches. Neurocomputing 100:144\u2013152, special issue: Behaviours in video. [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231212003153","DOI":"10.1016\/j.neucom.2011.09.037"},{"key":"6795_CR4","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.inffus.2020.06.004","volume":"63","author":"Q Li","year":"2020","unstructured":"Li Q, Gravina R, Li Y, Alsamhi SH, Sun F, Fortino G (2020) Multi-user activity recognition: challenges and opportunities. Inf Fusion 63:121\u2013135","journal-title":"Inf Fusion"},{"key":"6795_CR5","doi-asserted-by":"crossref","unstructured":"Wang X, Ellul J, Azzopardi G (2020) Elderly fall detection systems: a literature survey. Front Robot AI, vol. 7(71)","DOI":"10.3389\/frobt.2020.00071"},{"key":"6795_CR6","doi-asserted-by":"crossref","unstructured":"Gibson RM, Amira A, Ramzan N, de-la Higuera PC, Pervez Z (2016) Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl Soft Comput, 39:94\u2013103, [Online]. Available: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494615007061","DOI":"10.1016\/j.asoc.2015.10.062"},{"issue":"3","key":"6795_CR7","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TSMCA.2010.2093883","volume":"41","author":"C Zhu","year":"2011","unstructured":"Zhu C, Sheng W (2011) Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living. IEEE Trans Syst Man Cybern Part A Syst Humans 41(3):569\u2013573","journal-title":"IEEE Trans Syst Man Cybern Part A Syst Humans"},{"issue":"3","key":"6795_CR8","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/JSEN.2014.2370945","volume":"15","author":"SC Mukhopadhyay","year":"2015","unstructured":"Mukhopadhyay SC (2015) Wearable sensors for human activity monitoring: A review. IEEE Sens J 15(3):1321\u20131330","journal-title":"IEEE Sens J"},{"issue":"12","key":"6795_CR9","doi-asserted-by":"publisher","first-page":"3585","DOI":"10.1109\/JSEN.2017.2697077","volume":"17","author":"E Cippitelli","year":"2017","unstructured":"Cippitelli E, Fioranelli F, Gambi E, Spinsante S (2017) Radar and RGB-depth sensors for fall detection: a review. IEEE Sens J 17(12):3585\u20133604","journal-title":"IEEE Sens J"},{"issue":"8","key":"6795_CR10","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/TPAMI.2016.2537340","volume":"38","author":"D Wu","year":"2016","unstructured":"Wu D, Pigou L, Kindermans P-J, Le ND-H, Shao L, Dambre J, Odobez J-M (2016) Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Trans Pattern Anal Mach Intell 38(8):1583\u20131597","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"6795_CR11","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/JSEN.2016.2628099","volume":"17","author":"K Chaccour","year":"2017","unstructured":"Chaccour K, Darazi R, El Hassani AH, Andr\u00e8s E (2017) From fall detection to fall prevention: a generic classification of fall-related systems. IEEE Sens J 17(3):812\u2013822","journal-title":"IEEE Sens J"},{"issue":"5","key":"6795_CR12","doi-asserted-by":"publisher","first-page":"2086","DOI":"10.1109\/TMTT.2013.2247054","volume":"61","author":"Z Li","year":"2013","unstructured":"Li Z, Li W, Lv H, Zhang Y, Jing X, Wang J (2013) A novel method for respiration-like clutter cancellation in life detection by dual-frequency IR-UWB radar. IEEE Trans Microw Theory Tech 61(5):2086\u20132092","journal-title":"IEEE Trans Microw Theory Tech"},{"key":"6795_CR13","doi-asserted-by":"crossref","unstructured":"Xin L, Qiao D, Ye L, Dai H (2013) A novel through-wall respiration detection algorithm using uwb radar. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE","DOI":"10.1109\/EMBC.2013.6609675"},{"issue":"1","key":"6795_CR14","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1109\/JBHI.2018.2808281","volume":"23","author":"N Lu","year":"2019","unstructured":"Lu N, Wu Y, Feng L, Song J (2019) Deep learning for fall detection: three-dimensional CNN combined with LSTM on video kinematic data. IEEE J Biomed Health Inf 23(1):314\u2013323","journal-title":"IEEE J Biomed Health Inf"},{"issue":"5","key":"6795_CR15","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TCSVT.2011.2129370","volume":"21","author":"C Rougier","year":"2011","unstructured":"Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611\u2013622","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"6795_CR16","doi-asserted-by":"crossref","unstructured":"Wang X, Jia K (2020) Human fall detection algorithm based on yolov3. In: 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), pp 50\u201354","DOI":"10.1109\/ICIVC50857.2020.9177447"},{"key":"6795_CR17","doi-asserted-by":"crossref","unstructured":"Enayati M, Banerjee T, Popescu M, Skubic M, Rantz M (2014) A novel web-based depth video rewind approach toward fall preventive interventions in hospitals. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 4511\u20134514","DOI":"10.1109\/EMBC.2014.6944626"},{"key":"6795_CR18","doi-asserted-by":"crossref","unstructured":"Harris A, True H, Hu Z, Cho J, Fell N, Sartipi M (2016) Fall recognition using wearable technologies and machine learning algorithms. In: IEEE International Conference on Big Data (Big Data) 2016:3974\u20133976","DOI":"10.1109\/BigData.2016.7841080"},{"key":"6795_CR19","doi-asserted-by":"crossref","unstructured":"DesaiK, Mane P, Dsilva M, Zare A, Shingala P, Ambawade D (2020) A novel machine learning based wearable belt for fall detection. In: 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), pp 502\u2013505","DOI":"10.1109\/GUCON48875.2020.9231114"},{"key":"6795_CR20","doi-asserted-by":"crossref","unstructured":"Ball\u0131 S, Sagba\u015f E. A, Korukoglu S (2018) Design of smartwatch-assisted fall detection system via smartphone. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), pp 1\u20134","DOI":"10.1109\/SIU.2018.8404413"},{"key":"6795_CR21","doi-asserted-by":"crossref","unstructured":"Zhao S, Li W, Niu W, Gravina R, Fortino G (2018) Recognition of human fall events based on single tri-axial gyroscope. In: 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), pp 1\u20136","DOI":"10.1109\/ICNSC.2018.8361365"},{"key":"6795_CR22","doi-asserted-by":"crossref","unstructured":"de Araujo IL, Dourado L, Fernandes L, Andrade RMDC, Aguilar PAC (2018) An algorithm for fall detection using data from smartwatch. In: 2018 13th Annual Conference on System of Systems Engineering (SoSE), pp 124\u2013131","DOI":"10.1109\/SYSOSE.2018.8428786"},{"key":"6795_CR23","doi-asserted-by":"crossref","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","DOI":"10.1109\/ICA-SYMP50206.2021.9358439"},{"key":"6795_CR24","doi-asserted-by":"crossref","unstructured":"Ogawa Y, Naito K (2020) Fall detection scheme based on temperature distribution with ir array sensor. In: IEEE International Conference on Consumer Electronics (ICCE) 2020:1\u20135","DOI":"10.1109\/ICCE46568.2020.9043000"},{"key":"6795_CR25","doi-asserted-by":"crossref","unstructured":"Miawarni H, Sardjono TA, Setijadi E, Arraziqi WD, Gumelar AB, Purnomo MH (2020) Fall detection system for elderly based on 2d lidar: a preliminary study of fall incident and activities of daily living (ADL) detection. In: 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), pp 1\u20135","DOI":"10.1109\/CENIM51130.2020.9298000"},{"issue":"3","key":"6795_CR26","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","volume":"20","author":"H Li","year":"2020","unstructured":"Li H, Shrestha A, Heidari H, Le Kernec J, Fioranelli F (2020) Bi-LSTM network for multimodal continuous human activity recognition and fall detection. IEEE Sens J 20(3):1191\u20131201","journal-title":"IEEE Sens J"},{"key":"6795_CR27","doi-asserted-by":"crossref","unstructured":"Li H, Le Kernec J, Mehul A, Gurbuz SZ, Fioranelli F (2020) Distributed radar information fusion for gait recognition and fall detection. In: 2020 IEEE Radar Conference (RadarConf20), pp 1\u20136","DOI":"10.1109\/RadarConf2043947.2020.9266319"},{"key":"6795_CR28","doi-asserted-by":"crossref","unstructured":"Sadreazami H, Mitra D, Bolic M, Rajan S (2020) Compressed domain contactless fall incident detection using uwb radar signals. In: 2020 18th IEEE International New Circuits and Systems Conference (NEWCAS), pp 90\u201393","DOI":"10.1109\/NEWCAS49341.2020.9159760"},{"key":"6795_CR29","doi-asserted-by":"crossref","unstructured":"Khawaja W, Koohifar F, Guvenc I (2017) Uwb radar based beyond wall sensing and tracking for ambient assisted living. In: 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC), pp 142\u2013147","DOI":"10.1109\/CCNC.2017.7983096"},{"issue":"22","key":"6795_CR30","doi-asserted-by":"publisher","first-page":"13364","DOI":"10.1109\/JSEN.2020.3006918","volume":"20","author":"B Wang","year":"2020","unstructured":"Wang B, Guo L, Zhang H, Guo Y-X (2020) A millimetre-wave radar-based fall detection method using line kernel convolutional neural network. IEEE Sens J 20(22):13364\u201313370","journal-title":"IEEE Sens J"},{"issue":"4","key":"6795_CR31","doi-asserted-by":"publisher","first-page":"2005","DOI":"10.1109\/TGRS.2009.2036840","volume":"48","author":"A Nezirovic","year":"2010","unstructured":"Nezirovic A, Yarovoy AG, Ligthart LP (2010) Signal processing for improved detection of trapped victims using UWB radar. IEEE Trans Geosci Remote Sens 48(4):2005\u20132014","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06795-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06795-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06795-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T20:47:29Z","timestamp":1689194849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06795-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":31,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["6795"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06795-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]},"assertion":[{"value":"12 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2022","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 declared that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}