{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T09:46:58Z","timestamp":1785232018561,"version":"3.55.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s00521-022-07883-1","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T10:03:33Z","timestamp":1664877813000},"page":"1777-1802","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Deep-learning-based human activity recognition for Alzheimer\u2019s patients\u2019 daily life activities assistance"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3886-8150","authenticated-orcid":false,"given":"Ahmed","family":"Snoun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahani","family":"Bouchrika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olfa","family":"Jemai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"7883_CR1","doi-asserted-by":"crossref","unstructured":"Division UP (2019) World population ageing, 2019: highlights, p 37","DOI":"10.18356\/2dbe3948-en"},{"issue":"24","key":"7883_CR2","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1016\/j.jacc.2017.05.013","volume":"69","author":"V Fuster","year":"2017","unstructured":"Fuster V (2017) Changing demographics. J Am Coll Cardiol 69(24):3002\u20133005. https:\/\/doi.org\/10.1016\/j.jacc.2017.05.013","journal-title":"J Am Coll Cardiol"},{"issue":"5","key":"7883_CR3","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","volume":"40","author":"S Chernbumroong","year":"2013","unstructured":"Chernbumroong S, Cang S, Atkins A, Yu H (2013) Elderly activities recognition and classification for applications in assisted living. Expert Syst Appl 40(5):1662\u20131674. https:\/\/doi.org\/10.1016\/j.eswa.2012.09.004","journal-title":"Expert Syst Appl"},{"issue":"5","key":"7883_CR4","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.2337\/diacare.27.5.1047","volume":"27","author":"S Wild","year":"2004","unstructured":"Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes. Diabetes Care 27(5):1047\u20131053. https:\/\/doi.org\/10.2337\/diacare.27.5.1047","journal-title":"Diabetes Care"},{"issue":"4","key":"7883_CR5","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.jalz.2016.03.001","volume":"12","author":"A Association","year":"2016","unstructured":"Association A (2016) 2016 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement 12(4):459\u2013509. https:\/\/doi.org\/10.1016\/j.jalz.2016.03.001","journal-title":"Alzheimer\u2019s Dement"},{"key":"7883_CR6","unstructured":"Jackson-Webb FBF, Duckett RS Moodie: Australia\u2019s health 2016 report card: Australians living longer but with more chronic disease. https:\/\/www.aihw.gov.au\/getmedia\/9844cefb-7745-4dd8-9ee2-f4d1c3d6a727\/19787-AH16.pdf.aspx"},{"key":"7883_CR7","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1016\/j.jalz.2017.07.758","volume":"13","author":"T Dua","year":"2017","unstructured":"Dua T, Seeher K, Sivananthan S, Chowdhary N, Pot A, Saxena S (2017) World Health Organization\u2019s global action plan on the public health response to dementia 2017\u20132025. Alzheimer\u2019s Dement 13:1450\u20131451. https:\/\/doi.org\/10.1016\/j.jalz.2017.07.758","journal-title":"Alzheimer\u2019s Dement"},{"key":"7883_CR8","doi-asserted-by":"publisher","unstructured":"Kwak S, Han B, Han JH (2011) Scenario-based video event recognition by constraint flow, pp 3345\u20133352. https:\/\/doi.org\/10.1109\/CVPR.2011.5995435","DOI":"10.1109\/CVPR.2011.5995435"},{"key":"7883_CR9","doi-asserted-by":"publisher","unstructured":"Gaur U, Zhu Y, Song B, Roy-Chowdhury A (2011) A string of feature graphs model for recognition of complex activities in natural videos. In: 2011 international conference on computer vision, pp 2595\u20132602. https:\/\/doi.org\/10.1109\/ICCV.2011.6126548","DOI":"10.1109\/ICCV.2011.6126548"},{"issue":"7","key":"7883_CR10","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1109\/JPROC.2002.801449","volume":"90","author":"Z Duric","year":"2002","unstructured":"Duric Z, Gray WD, Heishman R, Fayin Li, Rosenfeld A, Schoelles MJ, Schunn C, Wechsler H (2002) Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction. Proc IEEE 90(7):1272\u20131289. https:\/\/doi.org\/10.1109\/JPROC.2002.801449","journal-title":"Proc IEEE"},{"key":"7883_CR11","doi-asserted-by":"publisher","unstructured":"Thangali A, Nash JP, Sclaroff S, Neidle C (2011) Exploiting phonological constraints for handshape inference in ASL video. In: CVPR 2011, pp 521\u2013528. https:\/\/doi.org\/10.1109\/CVPR.2011.5995718","DOI":"10.1109\/CVPR.2011.5995718"},{"key":"7883_CR12","unstructured":"Hankyu M, Rajeev S, Namsoon J (2012) Method and system for measuring shopper response to products based on behavior and facial expression. https:\/\/lens.org\/105-447-594-886-96X"},{"issue":"6","key":"7883_CR13","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1038\/scientificamerican0675-76","volume":"232","author":"G Johansson","year":"1975","unstructured":"Johansson G (1975) Visual motion perception. Sci Am 232(6):76\u201388","journal-title":"Sci Am"},{"key":"7883_CR14","doi-asserted-by":"publisher","unstructured":"Campbell LW, Bobick AF (1995) Recognition of human body motion using phase space constraints. In: Proceedings of IEEE international conference on computer vision, pp 624\u2013630. https:\/\/doi.org\/10.1109\/ICCV.1995.466880","DOI":"10.1109\/ICCV.1995.466880"},{"key":"7883_CR15","doi-asserted-by":"publisher","unstructured":"Sheikh Y, Sheikh M, Shah M (2005) Exploring the space of a human action. In: Tenth IEEE international conference on computer vision (ICCV\u201905), vol 1, pp 144\u2013149. https:\/\/doi.org\/10.1109\/ICCV.2005.90","DOI":"10.1109\/ICCV.2005.90"},{"key":"7883_CR16","doi-asserted-by":"publisher","unstructured":"Yilmaz A, Shah M (2005) Recognizing human actions in videos acquired by uncalibrated moving cameras. In: Tenth IEEE international conference on computer vision (ICCV\u201905), vol 1, pp 150\u2013157. https:\/\/doi.org\/10.1109\/ICCV.2005.201","DOI":"10.1109\/ICCV.2005.201"},{"issue":"1","key":"7883_CR17","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1023\/B:VISI.0000042934.15159.49","volume":"61","author":"PF Felzenszwalb","year":"2005","unstructured":"Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. Int J Comput Vis 61(1):55\u201379. https:\/\/doi.org\/10.1023\/B:VISI.0000042934.15159.49","journal-title":"Int J Comput Vis"},{"issue":"01","key":"7883_CR18","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S, Sheikh Y (2021) Openpose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43(01):172\u2013186. https:\/\/doi.org\/10.1109\/TPAMI.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7883_CR19","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-030-01264-9_17","volume-title":"Computer vision\u2014ECCV 2018","author":"G Papandreou","year":"2018","unstructured":"Papandreou G, Zhu T, Chen L-C, Gidaris S, Tompson J, Murphy K (2018) Personlab: person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision\u2014ECCV 2018. Springer, Cham, pp 282\u2013299"},{"key":"7883_CR20","doi-asserted-by":"publisher","unstructured":"Devanne M, Wannous H, Berretti S, Pala P, Daoudi M, Del Bimbo A (2013) Space-time pose representation for 3D human action recognition, pp 456\u2013464. https:\/\/doi.org\/10.1007\/978-3-642-41190-8_49","DOI":"10.1007\/978-3-642-41190-8_49"},{"key":"7883_CR21","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.4304\/jsw.8.9.2238-2245","volume":"8","author":"L Gan","year":"2013","unstructured":"Gan L, Chen F-T (2013) Human action recognition using APJ3D and random forests. J Softw 8:2238\u20132245","journal-title":"J Softw"},{"key":"7883_CR22","doi-asserted-by":"crossref","unstructured":"Wang J, Liu Z, Wu Y, Yuan J (2012) Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE conference on computer vision and pattern recognition, pp 1290\u20131297","DOI":"10.1109\/CVPR.2012.6247813"},{"key":"7883_CR23","doi-asserted-by":"crossref","unstructured":"Xia L, Chen C-C, Aggarwal JK (2012) View invariant human action recognition using histograms of 3D joints. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, pp 20\u201327","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"7883_CR24","doi-asserted-by":"publisher","unstructured":"Yang X, Tian Y (2014) Effective 3D action recognition using eigenjoints. J Vis Commun Image Represent 25(1):2\u201311. https:\/\/doi.org\/10.1016\/j.jvcir.2013.03.001 Visual Understanding and Applications with RGB-D Cameras","DOI":"10.1016\/j.jvcir.2013.03.001"},{"key":"7883_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2015\/676090","volume":"2015","author":"H Kim","year":"2015","unstructured":"Kim H, Kim I (2015) Human activity recognition as time-series analysis. Math Probl Eng 2015:1\u20139. https:\/\/doi.org\/10.1155\/2015\/676090","journal-title":"Math Probl Eng"},{"key":"7883_CR26","doi-asserted-by":"crossref","unstructured":"Taha A, Zayed HH, Khalifa ME, El-Horbaty E-SM (2015) Human activity recognition for surveillance applications. In: ICIT 2015, pp 577\u2013586","DOI":"10.15849\/icit.2015.0103"},{"key":"7883_CR27","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/THMS.2014.2377111","volume":"45","author":"S Gaglio","year":"2015","unstructured":"Gaglio S, Re GL, Morana M (2015) Human activity recognition process using 3-d posture data. IEEE Trans Hum Mach Syst 45:586\u2013597","journal-title":"IEEE Trans Hum Mach Syst"},{"key":"7883_CR28","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/978-3-030-20893-6_23","volume-title":"Computer vision\u2014ACCV 2018","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Lan Z, Newsam S, Hauptmann A (2019) Hidden two-stream convolutional networks for action recognition. In: Jawahar CV, Li H, Mori G, Schindler K (eds) Computer vision\u2014ACCV 2018. Springer, Cham, pp 363\u2013378"},{"issue":"1","key":"7883_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","volume":"35","author":"S Ji","year":"2013","unstructured":"Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221\u2013231. https:\/\/doi.org\/10.1109\/TPAMI.2012.59","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7883_CR30","doi-asserted-by":"publisher","unstructured":"Taylor GW, Fergus R, LeCun Y, Bregler C (2010) Convolutional Learning of spatio-temporal features. In: Proceedings of the 11th European conference on computer vision: Part VI. ECCV\u201910, pp 140\u2013153. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.5555\/1888212.1888225","DOI":"10.5555\/1888212.1888225"},{"key":"7883_CR31","doi-asserted-by":"publisher","unstructured":"Shamsipour G, Pirasteh S (2019) Artificial intelligence and convolutional neural network for recognition of human interaction by video from drone. https:\/\/doi.org\/10.20944\/preprints201908.0289.v1","DOI":"10.20944\/preprints201908.0289.v1"},{"issue":"12","key":"7883_CR32","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.1109\/TPAMI.2017.2769085","volume":"40","author":"H Bilen","year":"2018","unstructured":"Bilen H, Fernando B, Gavves E, Vedaldi A (2018) Action recognition with dynamic image networks. IEEE Trans Pattern Anal Mach Intell 40(12):2799\u20132813. https:\/\/doi.org\/10.1109\/TPAMI.2017.2769085","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7883_CR33","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. CoRR arXiv:1704.04861"},{"key":"7883_CR34","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7883_CR35","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) ICLR. arXiv:1409.1556"},{"key":"7883_CR36","unstructured":"Du Y, Wang W, Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1110\u20131118"},{"key":"7883_CR37","doi-asserted-by":"crossref","unstructured":"Zhu Y, Chen W, Guo G (2013) Fusing spatiotemporal features and joints for 3D action recognition. In: 2013 IEEE conference on computer vision and pattern recognition workshops, pp 486\u2013491","DOI":"10.1109\/CVPRW.2013.78"},{"key":"7883_CR38","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1109\/TPAMI.2017.2771306","volume":"40","author":"J Liu","year":"2018","unstructured":"Liu J, Shahroudy A, Xu D, Kot AC, Wang G (2018) Skeleton-based action recognition using spatio-temporal LSTM network with trust gates. IEEE Trans Pattern Anal Mach Intell 40:3007\u20133021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7883_CR39","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-20205-7_25","volume-title":"Image Anal","author":"FM Noori","year":"2019","unstructured":"Noori FM, Wallace B, Uddin MZ, Torresen J (2019) A robust human activity recognition approach using openpose, motion features, and deep recurrent neural network. In: Felsberg M, Forss\u00e9n P-E, Sintorn I-M, Unger J (eds) Image Anal. Springer, Cham, pp 299\u2013310"},{"key":"7883_CR40","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Lu, Polosukhin I (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in neural information processing systems, vol 30, pp. 1\u201311"},{"key":"7883_CR41","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M., Heigold G, Gelly S, Uszkoreit J, Houlsby N (2020) An image is worth 16x16 words: transformers for image recognition at scale. CoRR arXiv:2010.11929"},{"key":"7883_CR42","doi-asserted-by":"crossref","unstructured":"Lin C-H, Yumer E, Wang O, Shechtman E, Lucey S (2018) St-gan: spatial transformer generative adversarial networks for image compositing. In: 2018 IEEE\/CVF conference on computer vision and pattern recognition, pp 9455\u20139464","DOI":"10.1109\/CVPR.2018.00985"},{"key":"7883_CR43","doi-asserted-by":"publisher","unstructured":"Berg A, O\u2019Connor M, Cruz MT (2021) Keyword transformer: a self-attention model for keyword spotting. In: Proc. Interspeech 2021, pp 4249\u20134253. https:\/\/doi.org\/10.21437\/Interspeech.2021-1286","DOI":"10.21437\/Interspeech.2021-1286"},{"key":"7883_CR44","doi-asserted-by":"crossref","unstructured":"Jean-Baptiste E, Mihailidis A (2017) Benefits of automatic human action recognition in an assistive system for people with dementia. In: 2017 IEEE Canada international humanitarian technology conference (IHTC), pp 61\u201365","DOI":"10.1109\/IHTC.2017.8058201"},{"issue":"4","key":"7883_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2579700","volume":"5","author":"C Peters","year":"2014","unstructured":"Peters C, Hermann T, Wachsmuth S, Hoey J (2014) Automatic task assistance for people with cognitive disabilities in brushing teeth\u2014a user study with the tebra system. ACM Trans Access Comput 5(4):1\u201334. https:\/\/doi.org\/10.1145\/2579700","journal-title":"ACM Trans Access Comput"},{"issue":"2","key":"7883_CR46","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jides.2016.10.003","volume":"3","author":"EMD Jean-Baptiste","year":"2016","unstructured":"Jean-Baptiste EMD, Rotshtein P, Russell M (2016) Cogwatch: automatic prompting system for stroke survivors during activities of daily living. J Innov Digit Ecosyst 3(2):48\u201356. https:\/\/doi.org\/10.1016\/j.jides.2016.10.003","journal-title":"J Innov Digit Ecosyst"},{"key":"7883_CR47","unstructured":"Chen H, Soh Y (2018) A cooking assistance system for patients with Alzheimers disease using reinforcement learning. Int J Inf Technol 23(2), pp. 1\u201311"},{"key":"7883_CR48","doi-asserted-by":"crossref","unstructured":"Hou R, Chen C, Shah M (2017) An end-to-end 3D convolutional neural network for action detection and segmentation in videos. CoRR arXiv:1712.01111","DOI":"10.1109\/ICCV.2017.620"},{"key":"7883_CR49","unstructured":"Hou R, Chen C, Sukthankar R, Shah M (2019) An efficient 3D CNN for action\/object segmentation in video. CoRR arXiv:1907.08895"},{"key":"7883_CR50","doi-asserted-by":"publisher","unstructured":"Fernando B, Gavves E, Jos\u00e9 Oramas M, Ghodrati A, Tuytelaars T (2015) Modeling video evolution for action recognition. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 5378\u20135387. https:\/\/doi.org\/10.1109\/CVPR.2015.7299176","DOI":"10.1109\/CVPR.2015.7299176"},{"key":"7883_CR51","doi-asserted-by":"publisher","unstructured":"Smola AJ, Sch\u00f6lkopf B (2003) A tutorial on support vector regression. Technical report, statistics and computing. https:\/\/doi.org\/10.1023\/B:STCO.0000035301.49549.88","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"7883_CR52","unstructured":"Hendrycks D, Gimpel K (2016) Bridging nonlinearities and stochastic regularizers with Gaussian error linear units. CoRR arXiv:1606.08415"},{"key":"7883_CR53","unstructured":"Karakostas A, Briassouli A, Avgerinakis K, Kompatsiaris I, Tsolaki M (2016) The dem@care experiments and datasets: a technical report"},{"key":"7883_CR54","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.cviu.2015.10.013","volume":"144","author":"K Avgerinakis","year":"2016","unstructured":"Avgerinakis K, Briassouli A, Kompatsiaris Y (2016) Activity detection using sequential statistical boundary detection (SSBD). Comput Vis Image Underst 144:46\u201361","journal-title":"Comput Vis Image Underst"},{"key":"7883_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.image.2017.01.005","volume":"53","author":"S Poularakis","year":"2017","unstructured":"Poularakis S, Avgerinakis K, Briassouli A, Kompatsiaris Y (2017) Efficient motion estimation methods for fast recognition of activities of daily living. Signal Process Image Commun 53:1\u201312","journal-title":"Signal Process Image Commun"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07883-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07883-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07883-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T11:55:27Z","timestamp":1701172527000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07883-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7883"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07883-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,4]]},"assertion":[{"value":"9 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 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 have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}