{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:35:13Z","timestamp":1787564113702,"version":"build-2736575974"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"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":["Grant No. 51805469"],"award-info":[{"award-number":["Grant No. 51805469"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and technology plan project of drug regulatory system of Zhejiang province","award":["2020016"],"award-info":[{"award-number":["2020016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioMed Eng OnLine"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we design a pathological gait-recognition system (PGRS) with an 8\u2009\u00d7\u20098 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12938-021-00898-0","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T09:02:48Z","timestamp":1624352568000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Machine-learning-based children\u2019s pathological gait classification with low-cost gait-recognition system"],"prefix":"10.1186","volume":"20","author":[{"given":"Linghui","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiansong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8177-1071","authenticated-orcid":false,"given":"Wei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Canjun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"898_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.medengphy.2017.12.006","volume":"53","author":"J Figueiredo","year":"2018","unstructured":"Figueiredo J, Santos CP, Moreno JC. Automatic recognition of gait patterns in human motor disorders using machine learning: A review. Med Eng Phys. 2018;53:1\u201312. https:\/\/doi.org\/10.1016\/j.medengphy.2017.12.006.","journal-title":"Med Eng Phys"},{"key":"898_CR2","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S1050-6411(03)00077-4","volume":"14","author":"EB Titianova","year":"2004","unstructured":"Titianova EB, Mateev PS, Tarkka IM. Footprint analysis of gait using a pressure sensor system. J Electromyogr Kinesiol. 2004;14:275\u201381. https:\/\/doi.org\/10.1016\/S1050-6411(03)00077-4.","journal-title":"J Electromyogr Kinesiol"},{"key":"898_CR3","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","volume":"51","author":"A Salarian","year":"2004","unstructured":"Salarian A, Russmann H, Vingerhoets FJG, Dehollain C, Blanc Y, Burkhard PR, et al. Gait Assessment in Parkinson\u2019s disease: toward an ambulatory system for long-term monitoring. IEEE Trans Biomed Eng. 2004;51:1434\u201343. https:\/\/doi.org\/10.1109\/TBME.2004.827933.","journal-title":"IEEE Trans Biomed Eng"},{"key":"898_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/s0966-6362(00)00094-1","volume":"13","author":"T Chau","year":"2001","unstructured":"Chau T. A review of analytical techniques for gait data Part 1: Fuzzy, statistical and fractal methods. Gait Posture. 2001;13:49\u201366. https:\/\/doi.org\/10.1016\/s0966-6362(00)00094-1.","journal-title":"Gait Posture"},{"key":"898_CR5","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1109\/tnsre.2017.2736939","volume":"25","author":"E Dolatabadi","year":"2017","unstructured":"Dolatabadi E, Taati B, Mihailidis A. An automated classification of pathological gait using unobtrusive sensing technology. IEEE Trans Neural Syst Rehabil Eng. 2017;25:2336\u201346. https:\/\/doi.org\/10.1109\/tnsre.2017.2736939.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"898_CR6","doi-asserted-by":"publisher","first-page":"1553","DOI":"10.1007\/s11517-018-1795-2","volume":"56","author":"J Ortells","year":"2018","unstructured":"Ortells J, Herrero-Ezquerro MT, Mollineda RA. Vision-based gait impairment analysis for aided diagnosis. Med Biol Eng Comput. 2018;56:1553\u201364. https:\/\/doi.org\/10.1007\/s11517-018-1795-2.","journal-title":"Med Biol Eng Comput"},{"key":"898_CR7","doi-asserted-by":"publisher","unstructured":"Zakaria NK. ASD Children gait classification based on principal component analysis and linear discriminant analysis. IJETER 2020;8:2438\u201345. https:\/\/doi.org\/10.30534\/ijeter\/2020\/38862020.","DOI":"10.30534\/ijeter\/2020\/38862020"},{"key":"898_CR8","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1186\/1475-925X-10-99","volume":"10","author":"S-W Chen","year":"2011","unstructured":"Chen S-W, Lin S-H, Liao L-D, Lai H-Y, Pei Y-C, Kuo T-S, et al. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis. BioMed Eng OnLine. 2011;10:99. https:\/\/doi.org\/10.1186\/1475-925X-10-99.","journal-title":"BioMed Eng OnLine"},{"key":"898_CR9","doi-asserted-by":"publisher","first-page":"268","DOI":"10.3390\/app11010268","volume":"11","author":"P Farag\u00f3","year":"2021","unstructured":"Farag\u00f3 P, Grama L, Farago M-A, Hintea S. A novel wearable foot and ankle monitoring system for the assessment of gait biomechanics. Appl Sci. 2021;11:268. https:\/\/doi.org\/10.3390\/app11010268.","journal-title":"Appl Sci"},{"key":"898_CR10","doi-asserted-by":"publisher","unstructured":"Farag\u00f3 P, Cirlugea M, Hintea S. A Novel Smart-Shoe Architecture for Podiatric Monitoring. 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), 2020, p. 180\u20133. https:\/\/doi.org\/10.1109\/TSP49548.2020.9163549.","DOI":"10.1109\/TSP49548.2020.9163549"},{"key":"898_CR11","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s11517-016-1532-7","volume":"55","author":"Z Mei","year":"2017","unstructured":"Mei Z, Ivanov K, Zhao G, Li H, Wang L. An explorative investigation of functional differences in plantar center of pressure of four foot types using sample entropy method. Med Biol Eng Comput. 2017;55:537\u201348. https:\/\/doi.org\/10.1007\/s11517-016-1532-7.","journal-title":"Med Biol Eng Comput"},{"key":"898_CR12","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/1475-925X-12-101","volume":"12","author":"Z Mei","year":"2013","unstructured":"Mei Z, Zhao G, Ivanov K, Guo Y, Zhu Q, Zhou Y, et al. Sample entropy characteristics of movement for four foot types based on plantar centre of pressure during stance phase. BioMed Eng OnLine. 2013;12:101. https:\/\/doi.org\/10.1186\/1475-925X-12-101.","journal-title":"BioMed Eng OnLine"},{"key":"898_CR13","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1109\/10.58601","volume":"37","author":"H Zhu","year":"1990","unstructured":"Zhu H, Maalej N, Webster JG, Tompkins WJ. An umbilical data-acquisition system for measuring pressures between the foot and shoe. IEEE Trans Biomed Eng. 1990;37:908\u201311. https:\/\/doi.org\/10.1109\/10.58601.","journal-title":"IEEE Trans Biomed Eng"},{"key":"898_CR14","first-page":"390","volume":"72","author":"HS Zhu","year":"1991","unstructured":"Zhu HS, Wertsch JJ, Harris GF, Loftsgaarden JD, Price MB. Foot pressure distribution during walking and shuffling. Arch Phys Med Rehabil. 1991;72:390\u20137.","journal-title":"Arch Phys Med Rehabil"},{"key":"898_CR15","doi-asserted-by":"publisher","first-page":"1350039","DOI":"10.1142\/S0219519413500395","volume":"13","author":"Y-C Lin","year":"2013","unstructured":"Lin Y-C, Lin Y-T. Human recognition based on plantar pressure patterns during gait. J Mech Med Biol. 2013;13:1350039. https:\/\/doi.org\/10.1142\/S0219519413500395.","journal-title":"J Mech Med Biol"},{"key":"898_CR16","unstructured":"Sazonov ES, Bumpus T, Zeigler S, Marocco S. Classification of Plantar Pressure and Heel Acceleration Patterns Using Neural Networks, 2005."},{"key":"898_CR17","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1109\/ROBOT.2008.4543503","volume":"2008","author":"M Chen","year":"2008","unstructured":"Chen M, Huang B, Yangsheng Xu. Intelligent shoes for abnormal gait detection. IEEE Int Conf Robot Autom. 2008;2008:2019\u201324. https:\/\/doi.org\/10.1109\/ROBOT.2008.4543503.","journal-title":"IEEE Int Conf Robot Autom"},{"key":"898_CR18","doi-asserted-by":"publisher","unstructured":"Robb J. In-Toeing and Out-Toeing Gait. In: Joseph B, Robb J, Loder RT, Torode I, editors. Paediatric Orthopaedic Diagnosis: Asking the Right Questions, Springer India; 2015, p. 207\u201320. https:\/\/doi.org\/10.1007\/978-81-322-2392-4_21.","DOI":"10.1007\/978-81-322-2392-4_21"},{"key":"898_CR19","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/S0958-2592(96)90055-3","volume":"6","author":"C Thackeray","year":"1996","unstructured":"Thackeray C, Beeson P. Is in-toeing gait a developmental stage? Foot. 1996;6:19\u201324. https:\/\/doi.org\/10.1016\/S0958-2592(96)90055-3.","journal-title":"Foot"},{"key":"898_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0958-2592(96)90051-6","volume":"6","author":"C Thackeray","year":"1996","unstructured":"Thackeray C, Beeson P. In-toeing gait in children. A review of the literature. The Foot. 1996;6:1\u20134. https:\/\/doi.org\/10.1016\/S0958-2592(96)90051-6.","journal-title":"The Foot"},{"key":"898_CR21","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.paed.2010.03.001","volume":"20","author":"B Jacobs","year":"2010","unstructured":"Jacobs B. Toe walking, flat feet and bow legs, in-toeing and out-toeing. Paediatr Child Health. 2010;20:221\u20134. https:\/\/doi.org\/10.1016\/j.paed.2010.03.001.","journal-title":"Paediatr Child Health"},{"key":"898_CR22","doi-asserted-by":"publisher","DOI":"10.1542\/peds.103.6.e84","volume":"103","author":"A Garc\u00eda-Rodr\u00edguez","year":"1999","unstructured":"Garc\u00eda-Rodr\u00edguez A, Mart\u00edn-Jim\u00e9nez F, Carnero-Varo M, G\u00f3mez-Gracia E, G\u00f3mez-Aracena J, Fern\u00e1ndez-Crehuet J. Flexible flat feet in children: a real problem? Pediatrics. 1999;103: e84. https:\/\/doi.org\/10.1542\/peds.103.6.e84.","journal-title":"Pediatrics"},{"key":"898_CR23","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1542\/peds.2005-2126","volume":"118","author":"M Pfeiffer","year":"2006","unstructured":"Pfeiffer M, Kotz R, Ledl T, Hauser G, Sluga M. Prevalence of flat foot in preschool-aged children. Pediatrics. 2006;118:634\u20139. https:\/\/doi.org\/10.1542\/peds.2005-2126.","journal-title":"Pediatrics"},{"key":"898_CR24","first-page":"360","volume":"5","author":"L Yh","year":"1999","unstructured":"Yh L, Jc L. Intoeing gait in children. Hong Kong Med J. 1999;5:360\u20136.","journal-title":"Hong Kong Med J"},{"key":"898_CR25","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1097\/00004694-200501000-00018","volume":"25","author":"TAL Wren","year":"2005","unstructured":"Wren TAL, Rethlefsen S, Kay RM. Prevalence of specific gait abnormalities in children with cerebral palsy: influence of cerebral palsy subtype, age, and previous surgery. J Pediatr Orthop. 2005;25:79\u201383. https:\/\/doi.org\/10.1097\/00004694-200501000-00018.","journal-title":"J Pediatr Orthop"},{"key":"898_CR26","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1016\/j.gaitpost.2013.04.001","volume":"38","author":"D Rosenbaum","year":"2013","unstructured":"Rosenbaum D. Foot loading patterns can be changed by deliberately walking with in-toeing or out-toeing gait modifications. Gait Posture. 2013;38:1067\u20139. https:\/\/doi.org\/10.1016\/j.gaitpost.2013.04.001.","journal-title":"Gait Posture"},{"key":"898_CR27","unstructured":"S Y-L, H J-L, X F-Q. Survey on the occurrence of flatfoot among 1 001 children aged 3\u201312 years old in CangZhou city. Modern Preventive Medicine 2008."},{"key":"898_CR28","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/S0966-6362(96)01070-3","volume":"5","author":"JG Barton","year":"1997","unstructured":"Barton JG, Lees A. An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams. Gait Posture. 1997;5:28\u201333. https:\/\/doi.org\/10.1016\/S0966-6362(96)01070-3.","journal-title":"Gait Posture"},{"key":"898_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/VPPC.2017.8331040","volume":"2017","author":"AH Detti","year":"2017","unstructured":"Detti AH, Jemei S, Morando S, Steiner NY. Classification Based Method Using Fast Fourier Transform (FFT) and Total Harmonic Distortion (THD) Dedicated to Proton Exchange Membrane Fuel Cell (PEMFC) Diagnosis. IEEE Vehicle Power and Propulsion Conference (VPPC). 2017;2017:1\u20136. https:\/\/doi.org\/10.1109\/VPPC.2017.8331040.","journal-title":"IEEE Vehicle Power and Propulsion Conference (VPPC)"},{"key":"898_CR30","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10916-005-5184-7","volume":"29","author":"NF G\u00fcler","year":"2005","unstructured":"G\u00fcler NF, Ko\u00e7er S. Classification of EMG Signals Using PCA and FFT. J Med Syst. 2005;29:241\u201350. https:\/\/doi.org\/10.1007\/s10916-005-5184-7.","journal-title":"J Med Syst"},{"key":"898_CR31","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1016\/0021-9290(93)90028-D","volume":"26","author":"SH Holzreiter","year":"1993","unstructured":"Holzreiter SH, K\u00f6hle ME. Assessment of gait patterns using neural networks. J Biomech. 1993;26:645\u201351. https:\/\/doi.org\/10.1016\/0021-9290(93)90028-D.","journal-title":"J Biomech"},{"key":"898_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1080\/13102818.2017.1389303","volume":"32","author":"BVP Prasad","year":"2018","unstructured":"Prasad BVP, Parthasarathy V. Detection and classification of cardiovascular abnormalities using FFT based multi-objective genetic algorithm. Biotechnol Biotechnol Equip. 2018;32:183\u201393. https:\/\/doi.org\/10.1080\/13102818.2017.1389303.","journal-title":"Biotechnol Biotechnol Equip"},{"key":"898_CR33","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/IMOC.2009.5427579","volume":"2009","author":"MM Ram\u00f3n","year":"2009","unstructured":"Ram\u00f3n MM, Atwood T, Barbin S, Christodoulou CG. Signal classification with an SVM-FFT approach for feature extraction in cognitive radio. SBMO\/IEEE MTT-S Int Microw Optoelectr Conf. 2009;2009:286\u20139. https:\/\/doi.org\/10.1109\/IMOC.2009.5427579.","journal-title":"SBMO\/IEEE MTT-S Int Microw Optoelectr Conf"},{"key":"898_CR34","volume-title":"Support Vector Machines","author":"I Steinwart","year":"2008","unstructured":"Steinwart I, Christmann A. Support Vector Machines. 1st ed. Incorporated: Springer Publishing Company; 2008.","edition":"1"},{"key":"898_CR35","doi-asserted-by":"publisher","DOI":"10.1017\/9781108679930","volume-title":"Mathematics for Machine Learning","author":"MP Deisenroth","year":"2020","unstructured":"Deisenroth MP, Faisal AA, Ong CS. Mathematics for Machine Learning. Cambridge: New York, NY; 2020."},{"key":"898_CR36","doi-asserted-by":"publisher","unstructured":"Chen M, Huang B, Lee KK, Xu Y. An Intelligent Shoe-Integrated System for Plantar Pressure Measurement. 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China: IEEE; 2006, p. 416\u201321. https:\/\/doi.org\/10.1109\/ROBIO.2006.340213.","DOI":"10.1109\/ROBIO.2006.340213"},{"key":"898_CR37","doi-asserted-by":"crossref","unstructured":"Platt JC. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods: Support Vector Learning 1999:185\u2013208.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"898_CR38","doi-asserted-by":"publisher","first-page":"R16","DOI":"10.1088\/0957-0233\/10\/1\/017","volume":"10","author":"S Urry","year":"1999","unstructured":"Urry S. Plantar pressure-measurement sensors. Meas Sci Technol. 1999;10:R16-32. https:\/\/doi.org\/10.1088\/0957-0233\/10\/1\/017.","journal-title":"Meas Sci Technol"},{"key":"898_CR39","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1038\/s41586-019-1234-z","volume":"569","author":"S Sundaram","year":"2019","unstructured":"Sundaram S, Kellnhofer P, Li Y, Zhu J-Y, Torralba A, Matusik W. Learning the signatures of the human grasp using a scalable tactile glove. Nature. 2019;569:698\u2013702. https:\/\/doi.org\/10.1038\/s41586-019-1234-z.","journal-title":"Nature"},{"key":"898_CR40","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/S0924-4247(98)00204-0","volume":"72","author":"T D\u2019Alessio","year":"1999","unstructured":"D\u2019Alessio T. Measurement errors in the scanning of piezoresistive sensors arrays. Sens Actuators, A. 1999;72:71\u20136. https:\/\/doi.org\/10.1016\/S0924-4247(98)00204-0.","journal-title":"Sens Actuators, A"},{"key":"898_CR41","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1177\/027836499000900603","volume":"9","author":"TH Speeter","year":"1990","unstructured":"Speeter TH. A tactile sensing system for robotic manipulation. Int J Robot Res. 1990;9:25\u201336. https:\/\/doi.org\/10.1177\/027836499000900603.","journal-title":"Int J Robot Res"},{"key":"898_CR42","doi-asserted-by":"publisher","unstructured":"Casiez G, Roussel N, Vogel D. 1\u20ac Filter: A Simple Speed-based Low-pass Filter for Noisy Input in Interactive Systems, ACM; 2012, p. 2527. https:\/\/doi.org\/10.1145\/2207676.2208639.","DOI":"10.1145\/2207676.2208639"},{"issue":"1","key":"898_CR43","first-page":"32","volume":"19","author":"W Qihang","year":"2000","unstructured":"Qihang W, Wenlian L, Zuyun F. A new system for foot pressure measurement and gait analysis. Chin J Biomed Eng. 2000;19(1):32\u201340.","journal-title":"Chin J Biomed Eng."}],"container-title":["BioMedical Engineering OnLine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-021-00898-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12938-021-00898-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-021-00898-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T23:53:19Z","timestamp":1672530799000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedical-engineering-online.biomedcentral.com\/articles\/10.1186\/s12938-021-00898-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,22]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["898"],"URL":"https:\/\/doi.org\/10.1186\/s12938-021-00898-0","relation":{},"ISSN":["1475-925X"],"issn-type":[{"value":"1475-925X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,22]]},"assertion":[{"value":"13 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2021","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 study was performed following the principles outlined in the Helsinki Declaration and it was approved by Clinical Research Ethics Committee. All procedures were carried out with the adequate understanding and written informed consent of each subject.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The participants acknowledged their consent to publish the acquired data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"62"}}