{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T15:12:56Z","timestamp":1768662776160,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Pers Commun"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s11277-022-09819-3","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T15:18:59Z","timestamp":1655911139000},"page":"1733-1750","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Fall Detection Using LSTM and Transfer Learning"],"prefix":"10.1007","volume":"126","author":[{"given":"Ayesha","family":"Butt","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3537-8949","authenticated-orcid":false,"given":"Sanam","family":"Narejo","sequence":"additional","affiliation":[]},{"given":"Muhammad Rizwan","family":"Anjum","sequence":"additional","affiliation":[]},{"given":"Muhammad Usman","family":"Yonus","sequence":"additional","affiliation":[]},{"given":"Mashal","family":"Memon","sequence":"additional","affiliation":[]},{"given":"Arbab Ali","family":"Samejo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"9819_CR1","doi-asserted-by":"publisher","first-page":"108383","DOI":"10.1016\/j.measurement.2020.108383","volume":"167","author":"T Ba","year":"2021","unstructured":"Ba, T., Li, S., & Wei, Y. (2021). A data-driven machine learning integrated wearable medical sensor framework for elderly care service. Measurement, 167, 108383.","journal-title":"Measurement"},{"key":"9819_CR2","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/978-981-15-8752-8_49","volume-title":"Advances in Electronics, Communication and Computing","author":"P Anudeep","year":"2021","unstructured":"Anudeep, P., Mourya, P., & Anandhi, T. (2021). Parkinson\u2019s disease detection using machine learning techniques. Advances in Electronics, Communication and Computing (pp. 483\u2013493). Springer."},{"key":"9819_CR3","doi-asserted-by":"publisher","first-page":"102572","DOI":"10.1016\/j.scs.2020.102572","volume":"65","author":"AR Javed","year":"2021","unstructured":"Javed, A. R., Fahad, L. G., Farhan, A. A., Abbas, S., Srivastava, G., Parizi, R. M., & Khan, M. S. (2021). Automated cognitive health assessment in smart homes using machine learning. Sustainable Cities and Society, 65, 102572.","journal-title":"Sustainable Cities and Society"},{"key":"9819_CR4","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Stankoski, S., Kiprijanovska, I., Nikolovska, A., Mladenovska, N., Trajanoska, M., Velichkovska, B., Gjoreski, M., Lu\u0161trek, M. & Gams, M. (2020). Wearable sensors data-fusion and machine-learning method for fall detection and activity recognition. In\u00a0Challenges and Trends in Multimodal Fall Detection for Healthcare\u00a0(pp. 81\u201396). Springer.","DOI":"10.1007\/978-3-030-38748-8_4"},{"key":"9819_CR5","doi-asserted-by":"crossref","unstructured":"Zurbuchen, N., Bruegger, P., & Wilde, A. (2020). A comparison of machine learning algorithms for fall detection using wearable sensors. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) (pp. 427\u2013431). IEEE.","DOI":"10.1109\/ICAIIC48513.2020.9065205"},{"issue":"12","key":"9819_CR6","doi-asserted-by":"publisher","first-page":"4528","DOI":"10.1109\/JSEN.2019.2898891","volume":"19","author":"F Hussain","year":"2019","unstructured":"Hussain, F., Hussain, F., Ehatisham-ul-Haq, M., & Azam, M. A. (2019). Activity-aware fall detection and recognition based on wearable sensors. IEEE Sensors Journal, 19(12), 4528\u20134536.","journal-title":"IEEE Sensors Journal"},{"issue":"4","key":"9819_CR7","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.3390\/s18041155","volume":"18","author":"JA Santoyo-Ram\u00f3n","year":"2018","unstructured":"Santoyo-Ram\u00f3n, J. A., Casilari, E., & Cano-Garc\u00eda, J. M. (2018). Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning. Sensors, 18(4), 1155.","journal-title":"Sensors"},{"issue":"8","key":"9819_CR8","doi-asserted-by":"publisher","first-page":"3156","DOI":"10.1109\/JSEN.2019.2891128","volume":"19","author":"M Saleh","year":"2019","unstructured":"Saleh, M., & Jeann\u00e8s, R. L. B. (2019). Elderly fall detection using wearable sensors: A low cost highly accurate algorithm. IEEE Sensors Journal, 19(8), 3156\u20133164.","journal-title":"IEEE Sensors Journal"},{"issue":"12","key":"9819_CR9","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.3390\/s17122864","volume":"17","author":"K De Miguel","year":"2017","unstructured":"De Miguel, K., Brunete, A., Hernando, M., & Gambao, E. (2017). Home camera-based fall detection system for the elderly. Sensors, 17(12), 2864.","journal-title":"Sensors"},{"key":"9819_CR10","unstructured":"Asif, U., Mashford, B., Von Cavallar, S., Yohanandan, S., Roy, S., Tang, J., & Harrer, S. (2020). Privacy preserving human fall detection using video data. In\u00a0Machine Learning for Health Workshop\u00a0(pp. 39\u201351). PMLR."},{"key":"9819_CR11","doi-asserted-by":"crossref","unstructured":"Taufeeque, M., Koita, S., Spicher, N., & Deserno, T. M. (2021). Multi-camera, multi-person, and real-time fall detection using long short term memory. In\u00a0Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications\u00a0(Vol. 11601, p. 1160109). International Society for Optics and Photonics.","DOI":"10.1117\/12.2580700"},{"issue":"1","key":"9819_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"F Shu","year":"2021","unstructured":"Shu, F., & Shu, J. (2021). An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box. Scientific reports, 11(1), 1\u201317.","journal-title":"Scientific reports"},{"key":"9819_CR13","unstructured":"World Health Organization, World Health Organization. Ageing, & Life Course Unit. (2008).\u00a0WHO global report on falls prevention in older age. World Health Organization."},{"issue":"1","key":"9819_CR14","first-page":"22","volume":"41","author":"DE Bloom","year":"2011","unstructured":"Bloom, D. E., Boersch-Supan, A., McGee, P., & Seike, A. (2011). Population aging: Facts, challenges, and responses. Benefits and compensation International, 41(1), 22.","journal-title":"Benefits and compensation International"},{"issue":"18","key":"9819_CR15","doi-asserted-by":"publisher","first-page":"509","DOI":"10.15585\/mmwr.mm6718a1","volume":"67","author":"E Burns","year":"2018","unstructured":"Burns, E., & Kakara, R. (2018). Deaths from falls among persons aged\u2265 65 years\u2014United States, 2007\u20132016. Morbidity and Mortality Weekly Report, 67(18), 509.","journal-title":"Morbidity and Mortality Weekly Report"},{"key":"9819_CR16","unstructured":"Xu, J. (2017). Age-adjusted death rates from unintentional falls among adults aged>= 65 Years, by Sex-National Vital Statistics System, United States, 2000\u20132015."},{"issue":"3","key":"9819_CR17","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.apmr.2007.11.005","volume":"89","author":"K Ziegler-Graham","year":"2008","unstructured":"Ziegler-Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008). Estimating the prevalence of limb loss in the United States: 2005 to 2050. Archives of Physical Medicine and Rehabilitation, 89(3), 422\u2013429. https:\/\/doi.org\/10.1016\/j.apmr.2007.11.005","journal-title":"Archives of Physical Medicine and Rehabilitation"},{"issue":"4","key":"9819_CR18","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1111\/j.1532-5415.2007.01639.x","volume":"56","author":"N Deshpande","year":"2008","unstructured":"Deshpande, N., Metter, E. J., Lauretani, F., Bandinelli, S., Guralnik, J., & Ferrucci, L. (2008). Activity restriction induced by fear of falling and objective and subjective measures of physical function: A prospective cohort study. Journal of the American Geriatrics Society, 56(4), 615\u2013620.","journal-title":"Journal of the American Geriatrics Society"},{"key":"9819_CR19","doi-asserted-by":"publisher","first-page":"805","DOI":"10.2147\/IJGM.S32651","volume":"5","author":"Y Dionyssiotis","year":"2012","unstructured":"Dionyssiotis, Y. (2012). Analyzing the problem of falls among older people. International Journal of General Medicine, 5, 805.","journal-title":"International journal of general medicine"},{"issue":"3","key":"9819_CR20","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MPRV.2014.52","volume":"13","author":"FJ Ordonez","year":"2014","unstructured":"Ordonez, F. J., Englebienne, G., De Toledo, P., Van Kasteren, T., Sanchis, A., & Kr\u00f6se, B. (2014). In-home activity recognition: Bayesian inference for hidden Markov models. IEEE Pervasive Computing, 13(3), 67\u201375.","journal-title":"IEEE Pervasive Computing"},{"key":"9819_CR21","doi-asserted-by":"crossref","unstructured":"Hussain, F., Umair, M. B., Ehatisham-ul-Haq, M., Pires, I. M., Valente, T., Garcia, N. M., & Pombo, N. (2019). An Efficient Machine Learning-based Elderly Fall Detection Algorithm.\u00a0arXiv preprint arXiv:1911.11976.","DOI":"10.21203\/rs.3.rs-39065\/v1"},{"issue":"1","key":"9819_CR22","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.cviu.2008.07.006","volume":"113","author":"D Anderson","year":"2009","unstructured":"Anderson, D., Luke, R. H., Keller, J. M., Skubic, M., Rantz, M., & Aud, M. (2009). Linguistic summarization of video for fall detection using voxel person and fuzzy logic. Computer Vision and Image Understanding, 113(1), 80\u201389.","journal-title":"Computer vision and image understanding"},{"issue":"10","key":"9819_CR23","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1007\/s11517-017-1632-z","volume":"55","author":"C Medrano","year":"2017","unstructured":"Medrano, C., Igual, R., Garc\u00eda-Magari\u00f1o, I., Plaza, I., & Azuara, G. (2017). Combining novelty detectors to improve accelerometer-based fall detection. Medical & Biological Engineering & Computing, 55(10), 1849\u20131858.","journal-title":"Medical & Biological Engineering & Computing"},{"key":"9819_CR24","doi-asserted-by":"crossref","unstructured":"\u0160eketa, G., Vugrin, J., & Lackovi\u0107, I. (2017). Optimal threshold selection for acceleration-based fall detection. In\u00a0International Conference on Biomedical and Health Informatics\u00a0(pp. 151\u2013155). Springer.","DOI":"10.1007\/978-981-10-7419-6_26"},{"key":"9819_CR25","doi-asserted-by":"crossref","unstructured":"Cao, H., Wu, S., Zhou, Z., Lin, C. C., Yang, C. Y., Lee, S. T., & Wu, C. T. (2016). A fall detection method based on acceleration data and hidden Markov model. In\u00a02016 IEEE International Conference on Signal and Image Processing (ICSIP)\u00a0(pp. 684\u2013689). IEEE..","DOI":"10.1109\/SIPROCESS.2016.7888350"},{"issue":"2","key":"9819_CR26","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3233\/AIS-160369","volume":"8","author":"G Debard","year":"2016","unstructured":"Debard, G., Mertens, M., Deschodt, M., Vlaeyen, E., Devriendt, E., Dejaeger, E., Milisen, K., Tournoy, J., Croonenborghs, T., Goedem\u00e9, T., & Tuytelaars, T. (2016). Camera-based fall detection using real-world versus simulated data: How far are we from the solution? Journal of Ambient Intelligence and Smart Environments, 8(2), 149\u2013168.","journal-title":"Journal of Ambient Intelligence and Smart Environments"},{"key":"9819_CR27","unstructured":"Yazar, A., Erden, F., & Cetin, A. E. (2014). Multi-sensor ambient assisted living system for fall detection. In\u00a0Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP\u201914)\u00a0(pp. 1\u20133)."},{"issue":"10","key":"9819_CR28","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1109\/TNSRE.2017.2687100","volume":"25","author":"J Howcroft","year":"2017","unstructured":"Howcroft, J., Kofman, J., & Lemaire, E. D. (2017). Prospective fall-risk prediction models for older adults based on wearable sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1812\u20131820.","journal-title":"IEEE transactions on neural systems and rehabilitation engineering"},{"issue":"2","key":"9819_CR29","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1109\/TMC.2016.2557792","volume":"16","author":"Y Wang","year":"2016","unstructured":"Wang, Y., Wu, K., & Ni, L. M. (2016). Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing, 16(2), 581\u2013594.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"9819_CR30","doi-asserted-by":"crossref","unstructured":"Ozdemir, A. T., Tunc, C., & Hariri, S. (2017, September). Autonomic fall detection system. In\u00a02017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS* W)\u00a0(pp. 166\u2013170). IEEE.","DOI":"10.1109\/FAS-W.2017.142"},{"issue":"3","key":"9819_CR31","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s12652-017-0456-x","volume":"9","author":"AN Aicha","year":"2018","unstructured":"Aicha, A. N., Englebienne, G., & Kr\u00f6se, B. (2018). Continuous measuring of the indoor walking speed of older adults living alone. Journal of Ambient Intelligence and Humanized Computing, 9(3), 589\u2013599.","journal-title":"Journal of ambient intelligence and humanized computing"},{"issue":"3","key":"9819_CR32","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/JSEN.2017.2782492","volume":"18","author":"A Jain","year":"2018","unstructured":"Jain, A., & Kanhangad, V. (2018). \u2018Human activity classification in smartphones using accelerometer and gyroscope sensors.\u2019 IEEE Sensors J., 18(3), 1169\u20131177.","journal-title":"IEEE Sensors J."},{"issue":"4","key":"9819_CR33","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/JBHI.2017.2762404","volume":"22","author":"N Jalloul","year":"2018","unstructured":"Jalloul, N., Poree, F., Viardot, G., L\u2019Hostis, P., & Carrault, G. (2018). \u2018Activity recognition using complex network analysis.\u2019 IEEE J Biomed Health Informat, 22(4), 989\u20131000.","journal-title":"IEEE J Biomed Health Informat"},{"issue":"7","key":"9819_CR34","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.3390\/s17071487","volume":"17","author":"MA Guvensan","year":"2017","unstructured":"Guvensan, M. A., Kansiz, A. O., Camgoz, N. C., Turkmen, H., Yavuz, A. G., & Karsligil, M. E. (2017). An energy-efficient multi-tier architecture for fall detection on smartphones. Sensors, 17(7), 1487.","journal-title":"Sensors"},{"key":"9819_CR35","doi-asserted-by":"crossref","unstructured":"Yang, X., Dinh, A., & Chen, L. (2010). A wearable real-time fall detector based on Naive Bayes classifier. In\u00a0CCECE 2010\u00a0(pp. 1\u20134). IEEE.","DOI":"10.1109\/CCECE.2010.5575129"},{"key":"9819_CR36","doi-asserted-by":"publisher","first-page":"9311","DOI":"10.3233\/JIFS-201799","volume":"40","author":"T Kalsum","year":"2021","unstructured":"Kalsum, T., Mehmood, Z., Kulsoom, F., Chaudhry, H. N., Khan, A. R., Rashid, M., & Saba, T. (2021). Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features. Journal of Intelligent & Fuzzy Systems, 40, 9311\u20139331.","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"issue":"17","key":"9819_CR37","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.3390\/electronics10172082","volume":"10","author":"HN Chaudhry","year":"2021","unstructured":"Chaudhry, H. N., Javed, Y., Kulsoom, F., Mehmood, Z., Khan, Z. I., Shoaib, U., & Janjua, S. H. (2021). Sentiment analysis of before and after elections: Twitter data of US election 2020. Electronics, 10(17), 2082.","journal-title":"Electronics"},{"issue":"4","key":"9819_CR38","doi-asserted-by":"publisher","first-page":"800","DOI":"10.1109\/TPAMI.2015.2465955","volume":"38","author":"MR Amer","year":"2015","unstructured":"Amer, M. R., & Todorovic, S. (2015). Sum product networks for activity recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(4), 800\u2013813.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9819_CR39","doi-asserted-by":"publisher","first-page":"2364","DOI":"10.1016\/j.procs.2020.03.289","volume":"167","author":"P Agarwal","year":"2020","unstructured":"Agarwal, P., & Alam, M. (2020). A lightweight deep learning model for human activity recognition on edge devices. Procedia Computer Science, 167, 2364\u20132373.","journal-title":"Procedia Computer Science"},{"key":"9819_CR40","doi-asserted-by":"crossref","unstructured":"Silva, J., Sousa, I., & Cardoso, J. (2018, July). Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls. In\u00a02018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)\u00a0(pp. 3509\u20133512). IEEE.","DOI":"10.1109\/EMBC.2018.8513001"},{"key":"9819_CR41","doi-asserted-by":"crossref","unstructured":"Chouhan, K., Kumar, A., Chakraverti, A. K., & Cholla, R. R. (2022). Human fall detection analysis with image recognition using convolutional neural network approach. In\u00a0Proceedings of Trends in Electronics and Health Informatics\u00a0(pp. 95\u2013106). Springer.","DOI":"10.1007\/978-981-16-8826-3_9"},{"key":"9819_CR42","doi-asserted-by":"crossref","unstructured":"Paul Ijjina, E. (2022). Human Fall Detection in\u00a0Depth-Videos Using Temporal Templates and\u00a0Convolutional Neural Networks. In: Tsihrintzis, G.A., Virvou, M., Esposito, A., Jain, L.C. (eds) Advances in Assistive Technologies Learning and Analytics in Intelligent Systems, vol 28. Springer.","DOI":"10.1007\/978-3-030-87132-1_10"},{"key":"9819_CR43","volume-title":"Inventive computation and information technologies lecture notes in networks and systems","author":"V Muralidharan","year":"2022","unstructured":"Muralidharan, V., & Vijayalakshmi, V. (2022). A real-time approach of fall detection and rehabilitation in elders using kinect xbox 360 and supervised machine learning algorithm. In S. Smys, V. E. Balas, & R. Palanisamy (Eds.), Inventive computation and information technologies lecture notes in networks and systems.  (Vol. 336). Springer."},{"key":"9819_CR44","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Geoffrey Hinton, H. (2013). Speech recognition with deep recurrent neural networks.\" In 2013 IEEE international conference on acoustics, speech and signal processing, pp. 6645\u20136649.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"9819_CR45","first-page":"115","volume":"3","author":"FA Gers","year":"2002","unstructured":"Gers, F. A., Schraudolph, N. N., & Schmidhuber, J. (2002). Learning precise timing with LSTM recurrent networks. Journal of Machine Learning Research, 3, 115\u2013143.","journal-title":"Journal of Machine Learning Research"},{"key":"9819_CR46","doi-asserted-by":"publisher","first-page":"24509","DOI":"10.1109\/ACCESS.2022.3150838","volume":"10","author":"A Gorji","year":"2022","unstructured":"Gorji, A., Bourdoux, A., Pollin, S., & Sahli, H. (2022). Multi-view CNN-LSTM architecture for radar-based human activity recognition. IEEE Access, 10, 24509\u201324519.","journal-title":"IEEE Access"},{"key":"9819_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116287","volume":"191","author":"YA Andrade-Ambriz","year":"2022","unstructured":"Andrade-Ambriz, Y. A., Ledesma, S., Ibarra-Manzano, M. A., Oros-Flores, M. I., & Almanza-Ojeda, D. L. (2022). Human activity recognition using temporal convolutional neural network architecture. Expert Systems with Applications, 191, 116287.","journal-title":"Expert Systems with Applications"},{"key":"9819_CR48","doi-asserted-by":"crossref","unstructured":"Banjarey, K., Sahu, S. P., & Dewangan, D. K. (2022). Human Activity Recognition Using 1D Convolutional Neural Network. In\u00a0Sentimental Analysis and Deep Learning\u00a0(pp. 691\u2013702). Springer.","DOI":"10.1007\/978-981-16-5157-1_54"},{"key":"9819_CR49","unstructured":"Simonyan, K., Zisserman, A. (2015 ). Very deep convolutional networks for large-scale image recognition. In:  3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings,  arXiv:1409.1556"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09819-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-022-09819-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09819-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T10:52:28Z","timestamp":1663671148000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-022-09819-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["9819"],"URL":"https:\/\/doi.org\/10.1007\/s11277-022-09819-3","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,22]]},"assertion":[{"value":"28 May 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors do not have any conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}