{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:26:08Z","timestamp":1758709568814,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030588076"},{"type":"electronic","value":"9783030588083"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58808-3_30","type":"book-chapter","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T09:03:00Z","timestamp":1601283780000},"page":"415-428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Gait Characteristics and Their Discriminative Ability in Patients with Fabry Disease with and Without White-Matter Lesions"],"prefix":"10.1007","author":[{"given":"Jos\u00e9","family":"Braga","sequence":"first","affiliation":[]},{"given":"Flora","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Fernandes","sequence":"additional","affiliation":[]},{"given":"Miguel F.","family":"Gago","sequence":"additional","affiliation":[]},{"given":"Olga","family":"Azevedo","sequence":"additional","affiliation":[]},{"given":"Nuno","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Wolfram","family":"Erlhagen","sequence":"additional","affiliation":[]},{"given":"Estela","family":"Bicho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"issue":"3","key":"30_CR1","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1166\/asl.2018.11847","volume":"24","author":"S Aich","year":"2018","unstructured":"Aich, S., Choi, K.W., Pradhan, P.M., Park, J., Kim, H.C.: A performance comparison based on machine learning approaches to distinguish Parkinson\u2019s disease from Alzheimer disease using spatiotemporal gait signals. Adv. Sci. Lett. 24(3), 2058\u20132062 (2018)","journal-title":"Adv. Sci. Lett."},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.jbiomech.2017.12.002","volume":"71","author":"L Alcock","year":"2018","unstructured":"Alcock, L., Galna, B., Perkins, R., Lord, S., Rochester, L.: Step length determines minimum toe clearance in older adults and people with Parkinson\u2019s disease. J. Biomech. 71, 30\u201336 (2018)","journal-title":"J. Biomech."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Azevedo, O., et al.: Natural history of the late-onset phenotype of Fabry disease due to the p. F113L mutation. Mol. Genet. Metab. Rep. 22, 100565 (2020)","DOI":"10.1016\/j.ymgmr.2020.100565"},{"issue":"11","key":"30_CR4","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1136\/jnnp.2008.143693","volume":"79","author":"S Buechner","year":"2008","unstructured":"Buechner, S., et al.: Central nervous system involvement in Anderson-Fabry disease: a clinical and MRI retrospective study. J. Neurol. Neurosurg. Psychiatry 79(11), 1249\u20131254 (2008)","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"2","key":"30_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1177\/0049124104268644","volume":"33","author":"KP Burnham","year":"2004","unstructured":"Burnham, K.P., Anderson, D.R.: Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33(2), 261\u2013304 (2004)","journal-title":"Sociol. Methods Res."},{"issue":"6","key":"30_CR6","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/JBHI.2018.2865218","volume":"22","author":"C Caramia","year":"2018","unstructured":"Caramia, C., et al.: IMU-based classification of Parkinson\u2019s disease from gait: a sensitivity analysis on sensor location and feature selection. IEEE J. Biomed. Health Inform. 22(6), 1765\u20131774 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Fernandes, C., et al.: Gait classification of patients with Fabry\u2019s disease based on normalized gait features obtained using multiple regression models. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2288\u20132295. IEEE (2019)","DOI":"10.1109\/BIBM47256.2019.8983241"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Fernandes, C., et al.: Artificial neural networks classification of patients with Parkinsonism based on gait. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2024\u20132030. IEEE (2018)","DOI":"10.1109\/BIBM.2018.8621466"},{"key":"30_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.jbiomech.2019.05.039","volume":"92","author":"F Ferreira","year":"2019","unstructured":"Ferreira, F., et al.: Gait stride-to-stride variability and foot clearance pattern analysis in idiopathic Parkinson\u2019s disease and vascular parkinsonism. J. Biomech. 92, 98\u2013104 (2019)","journal-title":"J. Biomech."},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/2326409816666298","volume":"4","author":"R Giugliani","year":"2016","unstructured":"Giugliani, R., et al.: A 15-year perspective of the Fabry outcome survey. J. Inborn Errors Metab. Screen. 4, 1\u201312 (2016)","journal-title":"J. Inborn Errors Metab. Screen."},{"key":"30_CR11","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1007\/978-3-642-04962-0_53","volume-title":"Computational Intelligence and Intelligent Systems","author":"Q Gu","year":"2009","unstructured":"Gu, Q., Zhu, L., Cai, Z.: Evaluation measures of the classification performance of imbalanced data sets. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. CCIS, vol. 51, pp. 461\u2013471. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04962-0_53"},{"key":"30_CR12","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157\u20131182 (2003)"},{"key":"30_CR13","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511974175","volume-title":"Multivariable Analysis: A Practical Guide for Clinicians and Public Health Researchers","author":"MH Katz","year":"2011","unstructured":"Katz, M.H.: Multivariable Analysis: A Practical Guide for Clinicians and Public Health Researchers. Cambridge University Press, Cambridge (2011)"},{"issue":"3","key":"30_CR14","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.ymgme.2018.08.014","volume":"125","author":"S K\u00f6rver","year":"2018","unstructured":"K\u00f6rver, S., Vergouwe, M., Hollak, C.E., van Schaik, I.N., Langeveld, M.: Development and clinical consequences of white matter lesions in Fabry disease: a systematic review. Mol. Genet. Metab. 125(3), 205\u2013216 (2018)","journal-title":"Mol. Genet. Metab."},{"issue":"9","key":"30_CR15","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1002\/mds.26693","volume":"31","author":"KJ Kubota","year":"2016","unstructured":"Kubota, K.J., Chen, J.A., Little, M.A.: Machine learning for large-scale wearable sensor data in Parkinson\u2019s disease: concepts, promises, pitfalls, and futures. Mov. Disord. 31(9), 1314\u20131326 (2016)","journal-title":"Mov. Disord."},{"issue":"14","key":"30_CR16","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1212\/WNL.0000000000001450","volume":"84","author":"M L\u00f6hle","year":"2015","unstructured":"L\u00f6hle, M., et al.: Clinical prodromes of neurodegeneration in Anderson-Fabry disease. Neurology 84(14), 1454\u20131464 (2015)","journal-title":"Neurology"},{"issue":"1","key":"30_CR17","doi-asserted-by":"publisher","first-page":"134","DOI":"10.3390\/s16010134","volume":"16","author":"A Mannini","year":"2016","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., Sabatini, A.M.: A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington\u2019s disease patients. Sensors 16(1), 134 (2016)","journal-title":"Sensors"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Mikos, V., et al.: Regression analysis of gait parameters and mobility measures in a healthy cohort for subject-specific normative values. PloS One 13(6), 1\u201311 (2018)","DOI":"10.1371\/journal.pone.0199215"},{"key":"30_CR19","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"issue":"2","key":"30_CR20","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.jelekin.2015.01.004","volume":"25","author":"C Pradhan","year":"2015","unstructured":"Pradhan, C., et al.: Automated classification of neurological disorders of gait using spatio-temporal gait parameters. J. Electromyogr. Kinesiol. 25(2), 413\u2013422 (2015)","journal-title":"J. Electromyogr. Kinesiol."},{"issue":"1","key":"30_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"RZU Rehman","year":"2019","unstructured":"Rehman, R.Z.U., Del Din, S., Guan, Y., Yarnall, A.J., Shi, J.Q., Rochester, L.: Selecting clinically relevant gait characteristics for classification of early Parkinson\u2019s disease: a comprehensive machine learning approach. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"30_CR22","unstructured":"Snir, J.A., Bartha, R., Montero-Odasso, M.: White matter integrity is associated with gait impairment and falls in mild cognitive impairment. Results from the gait and brain study. NeuroImage: Clin. 24, 101975 (2019)"},{"issue":"1","key":"30_CR23","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1136\/jnnp.74.1.94","volume":"74","author":"JM Starr","year":"2003","unstructured":"Starr, J.M., et al.: Brain white matter lesions detected by magnetic resosnance imaging are associated with balance and gait speed. J. Neurol. Neurosurg. Psychiatry 74(1), 94\u201398 (2003)","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"2","key":"30_CR24","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1080\/01973533.2016.1277529","volume":"39","author":"CG Thompson","year":"2017","unstructured":"Thompson, C.G., Kim, R.S., Aloe, A.M., Becker, B.J.: Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic Appl. Soc. Psychol. 39(2), 81\u201390 (2017)","journal-title":"Basic Appl. Soc. Psychol."},{"issue":"2","key":"30_CR25","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1123\/jab.2015-0035","volume":"32","author":"F Wahid","year":"2016","unstructured":"Wahid, F., Begg, R., Lythgo, N., Hass, C.J., Halgamuge, S., Ackland, D.C.: A multiple regression approach to normalization of spatiotemporal gait features. J. Appl. Biomech. 32(2), 128\u2013139 (2016)","journal-title":"J. Appl. Biomech."},{"issue":"6","key":"30_CR26","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1109\/JBHI.2015.2450232","volume":"19","author":"F Wahid","year":"2015","unstructured":"Wahid, F., Begg, R.K., Hass, C.J., Halgamuge, S., Ackland, D.C.: Classification of Parkinson\u2019s disease gait using spatial-temporal gait features. IEEE J. Biomed. Health Inform. 19(6), 1794\u20131802 (2015)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"10","key":"30_CR27","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1093\/gerona\/gls063","volume":"67","author":"JJ Zheng","year":"2012","unstructured":"Zheng, J.J., et al.: Brain white matter hyperintensities, executive dysfunction, instability, and falls in older people: a prospective cohort study. J. Gerontol. Ser. A: Biomed. Sci. Med. Sci. 67(10), 1085\u20131091 (2012)","journal-title":"J. Gerontol. Ser. A: Biomed. Sci. Med. Sci."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58808-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T11:38:30Z","timestamp":1619177910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58808-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030588076","9783030588083"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58808-3_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cyber chair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1450","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"466","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held virtually due to COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}