{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:28:42Z","timestamp":1742923722691,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031360237"},{"type":"electronic","value":"9783031360244"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36024-4_15","type":"book-chapter","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T15:17:07Z","timestamp":1687879027000},"page":"200-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Universal Machine-Learning Processing Pattern for\u00a0Computing in\u00a0the\u00a0Video-Oculography"],"prefix":"10.1007","author":[{"given":"Albert","family":"\u015aledzianowski","sequence":"first","affiliation":[]},{"given":"Jerzy P.","family":"Nowacki","sequence":"additional","affiliation":[]},{"given":"Konrad","family":"Sitarz","sequence":"additional","affiliation":[]},{"given":"Andrzej W.","family":"Przybyszewski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"issue":"2","key":"15_CR1","doi-asserted-by":"publisher","first-page":"451","DOI":"10.3758\/s13428-017-0913-7","volume":"50","author":"K Semmelmann","year":"2017","unstructured":"Semmelmann, K., Weigelt, S.: Online webcam-based eye tracking in cognitive science: a first look. Behav. Res. Methods 50(2), 451\u2013465 (2017). https:\/\/doi.org\/10.3758\/s13428-017-0913-7","journal-title":"Behav. Res. Methods"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Aljaafreh, A., Alaqtash, M., Al-Oudat, N., Abukhait, J., Saleh, M.E.: A low-cost webcam-based eye tracker and saccade measurement system 14, 04 (2020)","DOI":"10.46300\/9106.2020.14.16"},{"issue":"2","key":"15_CR3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1049\/iet-cvi.2016.0226","volume":"11","author":"J Naruniec","year":"2017","unstructured":"Naruniec, J., et al.: Webcam-based system for video-oculography. IET Comput. Vis. 11(2), 173\u2013180 (2017)","journal-title":"IET Comput. Vis."},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s11042-012-1202-1","volume":"65","author":"Y-T Lin","year":"2012","unstructured":"Lin, Y.-T., Lin, R.-Y., Lin, Y.-C., Lee, G.C.: Real-time eye-gaze estimation using a low-resolution webcam. Multimedia Tools Appl. 65, 543\u2013568 (2012)","journal-title":"Multimedia Tools Appl."},{"key":"15_CR5","unstructured":"Xu, P., Ehinger, K.A., Zhang, Y., Finkelstein, A., Kulkarni, S.R., Xiao, J.: TurkerGaze: crowdsourcing saliency with webcam based eye tracking (2015)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Akinyelu, A.A., Blignaut, P.: Convolutional neural network-based technique for gaze estimation on mobile devices. Frontiers Artif. Intell. 4 (2022)","DOI":"10.3389\/frai.2021.796825"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"19581","DOI":"10.1109\/ACCESS.2017.2754299","volume":"5","author":"C Meng","year":"2017","unstructured":"Meng, C., Zhao, X.: Webcam-based eye movement analysis using CNN. IEEE Access 5, 19581\u201319587 (2017)","journal-title":"IEEE Access"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Gunawardena, N., Ginige, J.A., Javadi, B., Lui, G.: Performance analysis of CNN models for mobile device eye tracking with edge computing. Procedia Comput. Sci. 207, 2291\u20132300 (2022). Knowledge-Based and Intelligent Information And Engineering Systems: Proceedings of the 26th International Conference KES2022","DOI":"10.1016\/j.procs.2022.09.288"},{"key":"15_CR9","unstructured":"Harenstam-Nielsen, L.: Deep convolutional networks with recurrence for eye-tracking [internet] [dissertation] (2018)"},{"key":"15_CR10","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Softw. Tools (2000)"},{"key":"15_CR11","unstructured":"Intel\u00ae. Intel\u00ae distribution of openvino\u2122 toolkit (2022). https:\/\/docs.openvino.ai\/"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Harris, C.R., et al.: Array programming with NumPy. Nature 585, 357\u2013362 (2020)","DOI":"10.1038\/s41586-020-2649-2"},{"key":"15_CR13","unstructured":"Intel\u00ae. Openvino\u2122 toolkit - open model zoo repository, Intel\u2019s pre-trained models (2023). https:\/\/docs.openvino.ai\/latest\/omz_models_model_face_detection_0205.html"},{"key":"15_CR14","unstructured":"Intel\u00ae. Openvino\u2122 toolkit - open model zoo repository, Intel\u2019s pre-trained models (2022). https:\/\/docs.openvino.ai\/latest\/omz_models_model_facial_landmarks_35_adas_0002.html"},{"key":"15_CR15","unstructured":"Intel\u00ae. Openvino\u2122 toolkit - open model zoo repository, Intel\u2019s pre-trained models (2022). https:\/\/docs.openvino.ai\/2019_r1\/_head_pose_estimation_adas_0001_description_he ad_pose_estimation_adas_0001.html"},{"key":"15_CR16","unstructured":"Intel\u00ae. Openvino\u2122 toolkit - open model zoo repository, Intel\u2019s pre-trained models (2022) https:\/\/docs.openvino.ai\/2019_r1\/_gaze_estimation_adas_0002_des cription_gaze_estimation_adas_0002.html"},{"issue":"4","key":"15_CR17","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1016\/j.neuropsychologia.2009.11.006","volume":"48","author":"JM Chambers","year":"2010","unstructured":"Chambers, J.M., Prescott, T.J.: Response times for visually guided saccades in persons with Parkinson\u2019s disease: a meta-analytic review. Neuropsychologia 48(4), 887\u2013899 (2010)","journal-title":"Neuropsychologia"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Turner, T., Renfroe, J., Delambo, A., Hinson, V.: Validation of a behavioral approach for measuring saccades in Parkinson\u2019s disease. J. Motor Behav. 49, 1\u201311 (2017)","DOI":"10.1080\/00222895.2016.1250720"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Stuart, S., et al.: Pro-saccades predict cognitive decline in Parkinson\u2019s disease: ICICLE-PD. Mov. Disord. 34(11), 1690\u20131698 (2019)","DOI":"10.1002\/mds.27813"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Perneczky, R., Ghosh, B.C.P., Hughes, L., Carpenter, R.H.S., Barker, R.A., Rowe, J.B.: Saccadic latency in Parkinson\u2019s disease correlates with executive function and brain atrophy, but not motor severity. Neurobiol. Dis. 43(1), 79\u201385 (2011). Autophagy and Protein Degradation in Neurological Diseases","DOI":"10.1016\/j.nbd.2011.01.032"},{"key":"15_CR21","first-page":"10","volume":"3","author":"C Antoniades","year":"2013","unstructured":"Antoniades, C., Zheyu, X., Carpenter, R., Barker, R.: The relationship between abnormalities of saccadic and manual response times in Parkinson\u2019s disease. J. Parkinsons Dis. 3, 10 (2013)","journal-title":"J. Parkinsons Dis."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Yang, Q., Wang, T., Su, N., Xiao, S., Kapoula, Z.: Specific saccade deficits in patients with Alzheimer\u2019s disease at mid to moderate stage and in patients with amnestic cognitive impairment. Age (Dordrecht, Netherlands) 35 (2012)","DOI":"10.1007\/s11357-012-9420-z"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Pereira, M.: Saccadic eye movements associated with executive function decline in mild cognitive impairment and Alzheimer\u2019s disease: Biomarkers (non-neuroimaging)\/novel biomarkers. Alzheimer\u2019s Dement. 16 (2020)","DOI":"10.1002\/alz.040036"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Boxer, A.: Saccade abnormalities in autopsy-confirmed frontotemporal lobar degeneration and Alzheimer disease. Arch. Neurol. 69, 509\u2013517 (2012)","DOI":"10.1001\/archneurol.2011.1021"},{"key":"15_CR25","doi-asserted-by":"publisher","unstructured":"\u015aledzianowski, A., Szymanski, A., Drabik, A., Szlufik, S., Koziorowski, D.M., Przybyszewski, A.W.: Combining results of different oculometric tests improved prediction of Parkinson\u2019s disease development. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawi\u0144ski, B., Chittayasothorn, S. (eds.) Intelligent Information and Database Systems, pp. 517\u2013526. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-42058-1_43","DOI":"10.1007\/978-3-030-42058-1_43"},{"key":"15_CR26","unstructured":"Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261\u2013272 (2020)"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Bijvank, J.N., et al.: A standardized protocol for quantification of saccadic eye movements: demons. PLOS ONE 13, e0200695 (2018)","DOI":"10.1371\/journal.pone.0200695"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Andersson, R., Nystr\u00f6m, M., Holmqvist, K.: Sampling frequency and eye-tracking measures: how speed affects durations, latencies, and more. J. Eye Move. Res. 3, 1\u201312 (2010)","DOI":"10.16910\/jemr.3.3.6"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Szymanski, A., Szlufik, S., Koziorowski, D.M., Przybyszewski, A.W.: Building classifiers for Parkinson\u2019s disease using new eye tribe tracking method. In: ACIIDS (2017)","DOI":"10.1007\/978-3-319-54430-4_34"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Przybyszewski, A., Kon, M., Szlufik, S., Szyma\u0144ski, A., Habela, P., Koziorowski, D.: Multimodal learning and intelligent prediction of symptom development in individual Parkinson\u2019s patients. Sensors 16, 1498 (2016)","DOI":"10.3390\/s16091498"},{"issue":"1","key":"15_CR31","first-page":"191","volume":"57","author":"E Ramsperger","year":"1984","unstructured":"Ramsperger, E., Fischer, B.: Human express saccades: extremely short reaction times of goal directed eye movements. Exp. Brain Res. 57(1), 191\u20135 (1984)","journal-title":"Exp. Brain Res."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36024-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T13:06:17Z","timestamp":1723554377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36024-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360237","9783031360244"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36024-4_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","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":"188","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":"94","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":"35% - 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":"2,8","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":"3,2","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}