{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:20:38Z","timestamp":1760145638189,"version":"build-2065373602"},"reference-count":114,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T00:00:00Z","timestamp":1723939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["State Assignment No. 124020600009-2"],"award-info":[{"award-number":["State Assignment No. 124020600009-2"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vestibular neuritis and stroke is primarily clinical. As a part of the initial differential diagnosis, the physician focuses on the characteristics of nystagmus and the results of the video head impulse test (vHIT). Instruments for accurate vHIT are costly and are often utilized exclusively in healthcare settings. The objective of this paper is to review contemporary methodologies for accurately detecting the position of pupil centers in both eyes of a patient and for precisely extracting their coordinates. Additionally, the paper describes methods for accurately determining the head rotation angle under diverse imaging and lighting conditions. Furthermore, the suitability of these methods for vHIT is being evaluated. We assume the maximum allowable error is 0.005 radians per frame to detect pupils\u2019 coordinates or 0.3 degrees per frame while detecting the head position. We found that for such conditions, the most suitable approaches for head posture detection are deep learning (including LSTM networks), search by template matching, linear regression of EMG sensor data, and optical fiber sensor usage. The most relevant approaches for pupil localization for our medical tasks are deep learning, geometric transformations, decision trees, and RASNAC. This study might assist in the identification of a number of approaches that can be employed in the future to construct a high-accuracy system for vHIT based on a smartphone or a home computer, with subsequent signal processing and initial diagnosis.<\/jats:p>","DOI":"10.3390\/computation12080167","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T03:43:28Z","timestamp":1724039008000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Methods for Detecting the Patient\u2019s Pupils\u2019 Coordinates and Head Rotation Angle for the Video Head Impulse Test (vHIT), Applicable for the Diagnosis of Vestibular Neuritis and Pre-Stroke Conditions"],"prefix":"10.3390","volume":"12","author":[{"given":"G. D.","family":"Mamykin","sequence":"first","affiliation":[{"name":"Applied Mathematics Department, Perm National Research Polytechnic University, Komsomolsky Avenue 29, 614990 Perm, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6061-8118","authenticated-orcid":false,"given":"A. A.","family":"Kulesh","sequence":"additional","affiliation":[{"name":"Department of Neurology and Medical Genetics, Vagner Perm State Medical University, 614990 Perm, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1890-6906","authenticated-orcid":false,"given":"Fedor L.","family":"Barkov","sequence":"additional","affiliation":[{"name":"Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences, Lenin Street 13a, 614990 Perm, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7820-7736","authenticated-orcid":false,"given":"Y. A.","family":"Konstantinov","sequence":"additional","affiliation":[{"name":"Perm Federal Research Center of the Ural Branch of the Russian Academy of Sciences, Lenin Street 13a, 614990 Perm, Russia"}]},{"given":"D. P.","family":"Sokol\u2019chik","sequence":"additional","affiliation":[{"name":"Department of Nanotechnologies and Microsystem Engineering, Perm State National Research University, Bukirev Street 15, 614990 Perm, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6770-6292","authenticated-orcid":false,"given":"Vladimir","family":"Pervadchuk","sequence":"additional","affiliation":[{"name":"Applied Mathematics Department, Perm National Research Polytechnic University, Komsomolsky Avenue 29, 614990 Perm, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"389","DOI":"10.3233\/VES-220201","article-title":"Acute unilateral vestibulopathy\/vestibular neuritis: Diagnostic criteria","volume":"32","author":"Strupp","year":"2022","journal-title":"J. Vestib. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3233\/VES-210169","article-title":"Vascular vertigo and dizziness: Diagnostic criteria","volume":"32","author":"Kim","year":"2022","journal-title":"J. Vestib. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"41","DOI":"10.17116\/jnevro202112112241","article-title":"Vestibular vertigo in stroke and vestibular neuronitis","volume":"121","author":"Parfenov","year":"2021","journal-title":"S.S. Korsakov J. Neurol. Psychiatry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.30629\/2658-7947-2021-26-4-50-59","article-title":"Vestibular vertigo in emergency neurology","volume":"26","author":"Kulesh","year":"2021","journal-title":"Russ. Neurol. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1055\/s-0035-1564298","article-title":"Diagnosing Stroke in Acute Vertigo: The HINTS Family of Eye Movement Tests and the Future of the \u201cEye ECG\u201d","volume":"Volume 35","author":"Curthoys","year":"2015","journal-title":"Semin Neurology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s00415-022-11473-5","article-title":"Modern vestibular tests can accurately separate stroke and vestibular neuritis","volume":"270","author":"Nham","year":"2023","journal-title":"J. Neurol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/S0003-438X(05)82329-1","article-title":"\u00abHead impulse test de curthoys & halmagyi\u00bb: Un dispositif d\u2019analyse","volume":"Volume 122","author":"Ulmer","year":"2005","journal-title":"Annales d\u2019Otolaryngologie et de Chirurgie Cervico-Faciale"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rasheed, Z., Ma, Y.-K., Ullah, I., Al-Khasawneh, M., Almutairi, S.S., and Abohashrh, M. (2024). Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors. Bioengineering, 11.","DOI":"10.3390\/bioengineering11070701"},{"key":"ref_9","first-page":"103654","article-title":"An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection","volume":"80","author":"Ahmad","year":"2024","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.61186\/ijbc.15.3.93","article-title":"Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: Considering AI as a disease control strategy in future pandemics","volume":"15","author":"Ghaderzadeh","year":"2023","journal-title":"Iran. J. Blood Cancer"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112","DOI":"10.61186\/ijbc.15.3.112","article-title":"AI-driven malaria diagnosis: Developing a robust model for accurate detection and classification of malaria parasites","volume":"15","author":"Fasihfar","year":"2023","journal-title":"Iran. J. Blood Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2646","DOI":"10.1038\/s41598-021-81284-7","article-title":"Head motion classification using thread-based sensor and machine learning algorithm","volume":"11","author":"Jiang","year":"2021","journal-title":"Sci Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.patrec.2020.10.003","article-title":"Head pose estimation by regression algorithm","volume":"140","author":"Abate","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, Y., and Liu, Y. (2017, January 8). Head pose estimation algorithm based on deep learning. Proceedings of the AIP Conference Proceedings, Hangzhou, China.","DOI":"10.1063\/1.4982509"},{"key":"ref_15","unstructured":"Zhou, Y., and Gregson, J. (2020). WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Chong, E., and Rehg, J.M. (2018, January 18\u201322). Fine-Grained Head Pose Estimation Without Keypoints. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00281"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, J., Scheck, T., Seidel, R., Adya, Y., Nandi, D., and Hirtz, G. (2023, January 17\u201324). Human Pose Estimation in Monocular Omnidirectional Top-View Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00682"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khan, K., Mauro, M., Migliorati, P., and Leonardi, R. (2017, January 10\u201314). Head pose estimation through multi-class face segmentation. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019521"},{"key":"ref_19","unstructured":"Xu, X., and Kakadiaris, I.A. (June, January 30). Joint Head Pose Estimation and Face Alignment Framework Using Global and Local CNN Features. Proceedings of the 12th IEEE Conference on Automatic Face and Gesture Recognition, Washington, DC, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, H., Geng, T., and Xie, M. (2021, January 29\u201331). An multi-task head pose estimation algorithm. Proceedings of the 5th Asian Conference on Artificial Intelligence Technology (ACAIT), Haikou, China.","DOI":"10.1109\/ACAIT53529.2021.9731346"},{"key":"ref_21","first-page":"1745","article-title":"3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks","volume":"66","author":"Khan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TPAMI.2018.2885472","article-title":"Face-from-Depth for Head Pose Estimation on Depth Images","volume":"42","author":"Borghi","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","unstructured":"Paggio, P., Gatt, A., and Klinge, R. (2020, January 11\u201316). Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. Proceedings of the Workshop on People in Language, Vision and the Mind, Marseille, France."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"032003","DOI":"10.1088\/1757-899X\/782\/3\/032003","article-title":"Head posture detection with embedded attention model","volume":"782","author":"Han","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_25","first-page":"322","article-title":"Head posture detection with embedded attention model","volume":"22","author":"Sclaroff","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"012017","DOI":"10.1088\/1742-6596\/1642\/1\/012017","article-title":"Head Posture Recognition Method Based on POSIT Algorithm","volume":"1642","author":"Wenzhu","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107316","DOI":"10.1016\/j.patcog.2020.107316","article-title":"Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing","volume":"103","author":"Yuan","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fanelli, G., Weise, T., Gall, J., and Van Gool, L. (2011). Real Time Head Pose Estimation from Consumer Depth Cameras. Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-23123-0_11"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13673-014-0009-7","article-title":"Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix","volume":"4","author":"Kim","year":"2014","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, H., and Chen, Q. (2012, January 18\u201320). A head pose detection algorithm based on template match. Proceedings of the 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Nanjing, China.","DOI":"10.1109\/ICACI.2012.6463252"},{"key":"ref_31","unstructured":"Lavergne, A. (1999). Computer Vision System for Head Movement Detection and Tracking. [Master\u2019s Thesis, University of British Columbia]."},{"key":"ref_32","unstructured":"Chen, S., Bremond, F., Nguyen, H., and Thomas, H. (2016;, January 23\u201326). Exploring Depth Information for Head Detection with Depth Images. Proceedings of the AVSS 2016-13th International Conference on Advanced Video and Signal-Based Surveillance, Colorado Springs, CO, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saeed, A., Al-Hamadi, A., and Handrich, S. (2016, January 6\u20139). Advancement in the head pose estimation via depth-based face spotting. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7849932"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Neto, E.N.A., Barreto, R.M., Duarte, R.M., Magalhaes, J.P., Bastos, C.A., Ren, T.I., and Cavalcanti, G.D. (2012). Real-Time Head. Pose Estimation for Mobile Devices. Intelligent Data Engineering and Automated Learning-IDEAL 2012, Springer.","DOI":"10.1007\/978-3-642-32639-4_57"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Al-Azzawi, S.S., Khaksar, S., Hadi, E.K., Agrawal, H., and Murray, I. (2021). HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU. Sensors, 21.","DOI":"10.3390\/s21238148"},{"key":"ref_36","unstructured":"Benedetto, M., Gagliardi, A., Buonocunto, P., and Buttazzo, G. (2016, January 22\u201326). A Real-Time Head-Tracking Android Application Using Inertial Sensors. Proceedings of the MOBILITY 2016-6th International Conference on Mobile Services, Resources, and Users, Valencia, Spain."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, M., and Lee, S. (2022). Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time. Sensors, 22.","DOI":"10.3390\/s22134846"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Morishige, K.-I., Kurokawa, T., Kinoshita, M., Takano, H., and Hirahara, T. (October, January 27). Prediction of head-rotation movements using neck EMG signals for auditory tele-existence robot \u201cTeleHead\u201d. Proceedings of the RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, Toyama, Japan.","DOI":"10.1109\/ROMAN.2009.5326245"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"955","DOI":"10.2147\/OPTH.S105347","article-title":"Validation of sensor for postoperative positioning with intraocular gas","volume":"10","author":"Brodie","year":"2016","journal-title":"Clin. Ophthalmol."},{"key":"ref_40","unstructured":"Ba, S.O., and Odobez, J.M. (2006, January 6\u20137). Head Pose Tracking and Focus of Attention Recognition Algorithms in Meeting Rooms. Proceedings of the Multimodal Technologies for Perception of Humans, First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006, Southampton, UK."},{"key":"ref_41","unstructured":"Lunwei, Z., Jinwu, Q., Linyong, S., and Yanan, Z. (May, January 26). FBG sensor devices for spatial shape detection of intelligent colonoscope. Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA."},{"key":"ref_42","first-page":"906","article-title":"Real-Time Estimation of Three-Dimensional Needle Shape and Deflection for MRI-Guided Interventions","volume":"15","author":"Park","year":"2010","journal-title":"IEEE ASME Trans. Mechatron."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1109\/TMECH.2016.2606491","article-title":"Shape Detection Algorithm for Soft Manipulator Based on Fiber Bragg Gratings","volume":"21","author":"Wang","year":"2016","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5094","DOI":"10.2514\/1.J057944","article-title":"Fiber-Optics-Based Aeroelastic Shape Sensing","volume":"57","author":"Freydin","year":"2019","journal-title":"AIAA J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1088\/0957-0233\/15\/8\/036","article-title":"Pitch and roll sensing using fibre Bragg gratings in multicore fibre","volume":"15","author":"MacPherson","year":"2004","journal-title":"Meas. Sci. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.optlaseng.2004.04.009","article-title":"Embedded fiber Bragg grating sensor for internal strain measurements in polymeric materials","volume":"43","author":"Botsis","year":"2005","journal-title":"Opt. Lasers Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1109\/JLT.2017.2764951","article-title":"Long Period Gratings in Multicore Optical Fibers for Directional Curvature Sensor Implementation","volume":"36","author":"Barrera","year":"2017","journal-title":"J. Light. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Duncan, R.G., Froggatt, M.E., Kreger, S.T., Seeley, R.J., Gifford, D.K., Sang, A.K., and Wolfe, M.S. (2007, January 19\u201321). High-accuracy fiber-optic shape sensing. Proceedings of the Sensor Systems and Networks: Phenomena, Technology, and Applications for NDE and Health Monitoring 2007, San Diego, CA, USA.","DOI":"10.1117\/12.720914"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lally, E.M., Reaves, M., Horrell, E., Klute, S., and Froggatt, M.E. (2012, January 6). Fiber optic shape sensing for monitoring of flexible structures. Proceedings of the SPIE, San Diego, CA, USA.","DOI":"10.1117\/12.917490"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Brousseau, B., Rose, J., and Eizenman, M. (2020). Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model. Sensors, 20.","DOI":"10.3390\/s20020543"},{"key":"ref_51","first-page":"543","article-title":"Accelerating eye movement research via accurateand affordable smartphone eye tracking","volume":"20","author":"Valliappan","year":"2020","journal-title":"Sensors"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Feng, Y., Goulding-Hotta, N., Khan, A., Reyserhove, H., and Zhu, Y. (2022, January 12\u201316). Real-Time Gaze Tracking with Event-Driven Eye Segmentation. Proceedings of the 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Christchurch, New Zealand.","DOI":"10.1109\/VR51125.2022.00059"},{"key":"ref_53","unstructured":"Ji, Q., and Zhu, Z. (2004, January 8). Eye and gaze tracking for interactive graphic display. Machine vision and applications. Proceedings of the 2nd International Symposium on Smart Graphics, New York, NY, USA."},{"key":"ref_54","first-page":"1439312","article-title":"Real Time Eye Detector with Cascaded Convolutional Neural Networks","volume":"2018","author":"Li","year":"2018","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_55","first-page":"8718956","article-title":"CNN-Based Pupil Center Detection for Wearable Gaze Estimation System","volume":"2017","author":"Chinsatit","year":"2017","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_56","unstructured":"Fuhl, W., Santini, T., Kasneci, G., and Kasneci, E. (2016). Convolutional Neural Networks for Robust Pupil Detection. Computer Vision and Pattern Recognition. arXiv."},{"key":"ref_57","first-page":"1137","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"Comput. Vis. Pattern Recognit."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"125","DOI":"10.32604\/csse.2023.034546","article-title":"The Human. Eye Pupil Detection System Using. BAT Optimized Deep Learning Architecture","volume":"46","author":"Navaneethan","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_59","first-page":"4568929","article-title":"An Efficient and Robust Iris Segmentation Algorithm UsingDeep Learning","volume":"2019","author":"Li","year":"2019","journal-title":"Mobile Inf. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIFS.2020.2980791","article-title":"Towards Complete and Accurate Iris SegmentationUsing Deep Multi-task Attention Network forNon-Cooperative Iris Recognition","volume":"15","author":"Wang","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_61","first-page":"269","article-title":"Neural Network Approach for Eye Detection","volume":"2","author":"Biradar","year":"2012","journal-title":"Comput. Sci. Inf. Technol."},{"key":"ref_62","first-page":"6929762","article-title":"Efficient eye-blinking detection on smartphones: A hybrid approach based on deep learning","volume":"2018","author":"Han","year":"2018","journal-title":"Mob. Inf. Syst."},{"key":"ref_63","unstructured":"Zhu, Z., Ji, Q., Fujimura, K., and Lee, K. (2002, January 1\u201315). Combining Kalman Filtering and Mean Shift for Real Time Eye Tracking Under Active IR Illumination. Proceedings of the International Conference on Pattern Recognition, Quebec City, QC, Canada."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.3906\/elk-1312-150","article-title":"An efficient hybrid eye detection method","volume":"24","author":"Yu","year":"2016","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.imavis.2016.10.003","article-title":"Eye detection in a facial image under pose variation based on multi-scale iris shape feature","volume":"57","author":"Kim","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"60","DOI":"10.4018\/IJACI.2018010104","article-title":"Novel Technique for 3D Face Recognition Using Anthropometric Methodology","volume":"9","author":"Sghaier","year":"2018","journal-title":"Int. J. Ambient. Comput. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3931713","DOI":"10.1155\/2019\/3931713","article-title":"Optical Mouse Sensor for Eye Blink Detection and Pupil Tracking:Application in a Low-Cost Eye-Controlled Pointing Device","volume":"2019","author":"Tresanchez","year":"2019","journal-title":"J. Sensors"},{"key":"ref_68","unstructured":"Raj, A., Bhattarai, D., and Van Laerhoven, K. (2023). An Embedded and Real-Time Pupil Detection Pipeline. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Javadi, A.-H., Hakimi, Z., Barati, M., Walsh, V., and Tcheang, L. (2015). SET: A pupil detection method using sinusoidal approximation. Front. Neuroeng., 8.","DOI":"10.3389\/fneng.2015.00004"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"6655","DOI":"10.1007\/s11042-018-6371-0","article-title":"An adaptive localization of pupil degraded by eyelash occlusion and poor contrast","volume":"78","author":"Gautam","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_71","first-page":"143","article-title":"Fast Iris Localization Based on Image Algebra and Morphological Operations","volume":"27","author":"Hashim","year":"2019","journal-title":"J. Univ. Babylon Pure Appl. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1016\/j.ijleo.2014.04.009","article-title":"Iris segmentation for visible wavelength and near infrared eye images","volume":"125","author":"Jan","year":"2014","journal-title":"Optik"},{"key":"ref_73","first-page":"975","article-title":"Pupil Segmentation from IRIS Images using Modified Peak Detection Algorithm","volume":"37","author":"Perumal","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"30126","DOI":"10.3390\/s151229792","article-title":"Pupil and Glint Detection Using Wearable CameraSensor and Near-Infrared LED Array","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.optlaseng.2010.08.020","article-title":"Automatic localization of pupil using eccentricity and iris using gradient based method","volume":"49","author":"Khan","year":"2011","journal-title":"Opt. Lasers Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TIFS.2009.2033225","article-title":"Iris Segmentation Using Geodesic Active Contours","volume":"4","author":"Shah","year":"2009","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.optlaseng.2007.06.006","article-title":"Localization of iris in gray scale images using intensity gradient","volume":"45","author":"Basit","year":"2007","journal-title":"Opt. Lasers Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"319","DOI":"10.4015\/S1016237206000476","article-title":"An eye tracking system and its application in aids for people with severe disabilities","volume":"18","author":"Su","year":"2006","journal-title":"Biomed. Eng. Appl. Basis Commun."},{"key":"ref_79","first-page":"127","article-title":"A Robust Algorithm for Eye Detection on Gray Intensity Face without Spectacles","volume":"5","author":"Peng","year":"2005","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_80","unstructured":"Timm, F., and Barth, E. (2011, January 5\u20137). Accurate Eye Centre Localisation By Means Of Gradients. Proceedings of the VISAPP 2011-Sixth International Conference on Computer Vision Theory and Applications, Vilamoura, Algarve, Portugal."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Araujo, G.M., Ribeiro, F.M.L., Silva, E.A.B., and Goldenstein, S.K. (2014, January 27\u201330). Fast eye localization without a face model using inner product detectors. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025273"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.optlaseng.2014.11.003","article-title":"Novel automatic eye detection and tracking algorithm","volume":"67","author":"Ghazali","year":"2015","journal-title":"Opt. Lasers Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"033033","DOI":"10.1117\/1.JEI.22.3.033033","article-title":"Unsupervised approach for the accurate localizationof the pupils in near-frontal facial images","volume":"22","author":"Leo","year":"2013","journal-title":"J. Electron. Imaging"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"02008","DOI":"10.1051\/e3sconf\/201910402008","article-title":"Head-mounted eye tracker based on android smartphone","volume":"104","author":"Fisunov","year":"2019","journal-title":"Proc. E3S Web Conf."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"105754","DOI":"10.1109\/ACCESS.2020.3000063","article-title":"Pupil Detection Based on Oblique Projection Using a Binocular Camera","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1515\/pjbr-2018-0002","article-title":"Real-time gaze estimation via pupil center tracking","volume":"9","author":"Cazzato","year":"2018","journal-title":"J. Behav. Robot."},{"key":"ref_87","unstructured":"Kang, S., Kim, S., Lee, Y.-S., and Jeon, G. (2012, January 23\u201325). Analysis of Screen Resolution According to Gaze Estimation in the 3D Space. Proceedings of the Convergence and Hybrid Information Technology: 6th International Conference, ICHIT 2012, Daejeon, Republic of Korea."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"De Santis, A., and Iacoviello, D. (2009). A Robust Eye Tracking Procedure for Medical and Industrial Applications. Advances in Computational Vision and Medical Image Processing, Springer Netherlands.","DOI":"10.1007\/978-1-4020-9086-8_10"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kacete, A., Seguier, R., Royan, J., Collobert, M., and Soladie, C. (2016, January 7\u201310). Real-time eye pupil localization using Hough regression forest. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477666"},{"key":"ref_90","unstructured":"Mosa, A.H., Ali, M., and Kyamakya, K. (2013, January 24\u201326). A Computerized Method to Diagnose Strabismus Based on a Novel Method for Pupil Segmentation. Proceedings of the ISTET 2013: International Symposiumon Theoretical Electrical Engineering, Pilsen, Czech Republic."},{"key":"ref_91","first-page":"578","article-title":"A Computerized Method to Diagnose Strabismus Based on a Novel Method for Pupil Segmentation","volume":"47","author":"Frljak","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patcog.2017.01.023","article-title":"A joint cascaded framework for simultaneous eye detection and eye state estimation","volume":"67","author":"Goua","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"208","DOI":"10.25046\/aj040627","article-title":"Eye Feature Extraction with Calibration Model using Viola-Jones and Neural Network Algorithms","volume":"4","author":"Ibrahim","year":"2018","journal-title":"Adv. Sci. Technol. Eng. Syst. J."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Haq, Z.A., and Hasan, Z. (2016, January 12\u201314). Eye-Blink rate detection for fatigue determination. Proceedings of the 2016 1st India International Conference on Information Processing (IICIP), Delhi, India.","DOI":"10.1109\/IICIP.2016.7975348"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"He, H., She, Y., Xiahou, J., Yao, J., Li, J., Hong, Q., and Ji, Y. (2018, January 11\u201314). Real-Time Eye-Gaze Based Interaction for Human Intention Prediction and Emotion Analysis. Proceedings of the CGI 2018: Proceedings of Computer Graphics International 2018, New York, NY, USA.","DOI":"10.1145\/3208159.3208180"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Swirski, L., Bulling, A., and Dodgson, N. (2012, January 28\u201330). Robust real-time pupil tracking in highly off-axis images. Proceedings of the ETRA \u201812: Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara CA, USA.","DOI":"10.1145\/2168556.2168585"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Raudonis, V., Simutis, R., and Narvydas, G. (2009, January 24\u201327). Discrete eye tracking for medical applications. Proceedings of the 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, Bratislava, Slovakia.","DOI":"10.1109\/ISABEL.2009.5373675"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Bozomitu, R.G., P\u0103s\u0103ric\u0103, A., Cehan, V., Lupu, R.G., Rotariu, C., and Coca, E. (2015, January 19\u201321). Implementation of Eye-tracking System Based on Circular Hough Transform Algorithm. Proceedings of the 2015 E-Health and Bioengineering Conference, EHB 2015, Iasi, Romania.","DOI":"10.1109\/EHB.2015.7391384"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.protcy.2016.08.133","article-title":"Effective Iris Recognition System","volume":"25","author":"Thomas","year":"2016","journal-title":"Procedia Technol."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Halmagyi, G.M., Chen, L., MacDougall, H.G., Weber, K.P., McGarvie, L.A., and Curthoys, I.S. (2017). The Video Head Impulse Test. Front. Neurol., 8.","DOI":"10.3389\/fneur.2017.00258"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1134\/S0020441221050067","article-title":"Comparative Analysis of the Brillouin Frequency Shift Determining Accuracy in Extremely Noised Spectra by Various Correlation Methods","volume":"64","author":"Krivosheev","year":"2021","journal-title":"Gen. Exp. Tech."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1070\/QE2009v039n11ABEH014171","article-title":"Polarisation reflectometry of anisotropic optical fibres","volume":"39","author":"Konstantinov","year":"2009","journal-title":"Quantum Electron."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Turov, A.T., Barkov, F.L., Konstantinov, Y.A., Korobko, D.A., Lopez-Mercado, C.A., and Fotiadi, A.A. (2023). Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors. Algorithms, 16.","DOI":"10.3390\/a16090440"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Turov, A.T., Konstantinov, Y.A., Barkov, F.L., Korobko, D.A., Zolotovskii, I.O., Lopez-Mercado, C.A., and Fotiadi, A.A. (2023). Enhancing the Distributed Acoustic Sensors\u2019 (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application. Algorithms, 16.","DOI":"10.3390\/a16050217"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Nordin, N.D., Abdullah, F., Zan, M.S.D., Bakar, A.A.A., Krivosheev, A.I., Barkov, F.L., and Konstantinov, Y.A. (2022). Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring. Sensors, 22.","DOI":"10.3390\/s22072677"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"6769","DOI":"10.1364\/OE.24.006769","article-title":"Signal processing using artificial neural network for BOTDA sensor system","volume":"24","author":"Azad","year":"2016","journal-title":"Opt. Express"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Yao, Y., Zhao, Z., and Tang, M. (2023). Advances in Multicore Fiber Interferometric Sensors. Sensors, 23.","DOI":"10.3390\/s23073436"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Cuando-Espitia, N., Fuentes-Fuentes, M.A., Vel\u00e1zquez-Ben\u00edtez, A., Amezcua, R., Hern\u00e1ndez-Cordero, J., and May-Arrioja, D.A. (2021). Vernier effect using in-line highly coupled multicore fibers. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-97646-0"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1364\/AO.385324","article-title":"Tapered multicore fiber interferometer for refractive index sensing with graphene enhancement","volume":"59","author":"Guo","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"41647","DOI":"10.1109\/ACCESS.2021.3061250","article-title":"A Comprehensive Study of Optical Frequency Domain Reflectometry","volume":"9","author":"Liang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Belokrylov, M.E., Kambur, D.A., Konstantinov, Y.A., Claude, D., and Barkov, F.L. (2024). An Optical Frequency Domain Reflectometer\u2019s (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing. Sensors, 24.","DOI":"10.3390\/s24041253"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1134\/S0020441223050172","article-title":"Method for Increasing the Signal-to-Noise Ratio of Rayleigh Back-Scattered Radiation Registered by a Frequency Domain Optical Reflectometer Using Two-Stage Erbium Amplification","volume":"66","author":"Belokrylov","year":"2023","journal-title":"Instrum. Exp. Tech."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1364\/OL.516067","article-title":"OFDR shape sensor based on a femtosecond-laser-inscribed weak fiber Bragg grating array in a multicore fiber","volume":"49","author":"Fu","year":"2024","journal-title":"Opt. Lett."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Monet, F., Sefati, S., Lorre, P., Poiffaut, A., Kadoury, S., Armand, M., Iordachita, I., and Kashyap, R. (August, January 31). High-Resolution Optical Fiber Shape Sensing of Continuum Robots: A Comparative Study. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197454"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/8\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:38:38Z","timestamp":1760110718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/12\/8\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,18]]},"references-count":114,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["computation12080167"],"URL":"https:\/\/doi.org\/10.3390\/computation12080167","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2024,8,18]]}}}