{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:51:37Z","timestamp":1781765497237,"version":"3.54.5"},"reference-count":72,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate early screening of Alzheimer\u2019s disease (AD) is crucial, yet traditional diagnostic methods are often limited by invasiveness or high costs. Therefore, there is a critical need for non-invasive biomarkers that enable precise and accessible screening. In this study, we propose a multi-modal digital biomarker framework designed to accurately detect AD by evaluating impairments across multiple cognitive domains, such as language, working memory, and visuospatial attention. By leveraging voice and video data, our approach significantly enhances user accessibility and real-world applicability. We validated the proposed framework using a dataset of 128 participants, comprising 77 healthy controls (HCs) and 51 patients with AD. While individual cognitive tasks yielded F1-scores ranging from 69.23% to 77.78% and sensitivities from 69.23% to 80.77%, our ensemble strategy significantly enhanced detection performance, achieving an F1-score of 83.64% and a sensitivity of 88.46%. These findings confirm that the proposed multi-modal digital biomarker framework, enhanced via ensembling, provides a highly accurate, scalable, and practical solution for the non-invasive screening and detection of AD.<\/jats:p>","DOI":"10.3390\/s26123833","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T03:27:21Z","timestamp":1781666841000},"page":"3833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Early Detection of Alzheimer\u2019s Disease: An Ensemble Model for Multi-Domain Cognitive Assessment Using Voice and Video"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6205-7747","authenticated-orcid":false,"given":"Shinwoo","family":"Ham","sequence":"first","affiliation":[{"name":"Voinosis Inc., Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3345-2695","authenticated-orcid":false,"given":"Donghun","family":"Min","sequence":"additional","affiliation":[{"name":"Voinosis Inc., Seoul 05029, Republic of Korea"},{"name":"Artificial Intelligence & Computer Vision Laboratory, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1580-5851","authenticated-orcid":false,"given":"Hyo Jin","family":"Jon","sequence":"additional","affiliation":[{"name":"Voinosis Inc., Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6044-9342","authenticated-orcid":false,"given":"Jung Eun","family":"Shin","sequence":"additional","affiliation":[{"name":"Voinosis Inc., Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6944-5863","authenticated-orcid":false,"given":"Eun Yi","family":"Kim","sequence":"additional","affiliation":[{"name":"Voinosis Inc., Seoul 05029, Republic of Korea"},{"name":"Artificial Intelligence & Computer Vision Laboratory, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5 (R)), American Psychiatric Association Publishing. [5th ed.].","DOI":"10.1176\/appi.books.9780890425596"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2019). 2019 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 15, 321\u2013387.","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.clinbiochem.2019.04.015","article-title":"Clinical aspects of Alzheimer\u2019s disease","volume":"72","year":"2019","journal-title":"Clin. Biochem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1148\/radiol.2282020915","article-title":"Cost-Effectiveness of PET in the Diagnosis of Alzheimer Disease","volume":"228","author":"McMahon","year":"2003","journal-title":"Radiology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3233\/ADR-180098","article-title":"Screening for Dementia Caused by Modifiable Lifestyle Choices Using Hybrid PET\/MRI","volume":"3","author":"Prato","year":"2019","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1055\/s-0028-1082884","article-title":"A Disorder of Executive Function and Its Role in Language Processing","volume":"29","author":"Martin","year":"2008","journal-title":"Semin. Speech Lang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.2147\/CIA.S39959","article-title":"Language impairment in Alzheimer\u2019s disease and benefits of acetylcholinesterase inhibitors","volume":"8","author":"Ferris","year":"2013","journal-title":"Clin. Interv. Aging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Meghanani, A., Anoop, C.S., and Ramakrishnan, A.G. (2021). Recognition of Alzheimer\u2019s Dementia From the Transcriptions of Spontaneous Speech Using fastText and CNN Models. Front. Comput. Sci., 3.","DOI":"10.3389\/fcomp.2021.624558"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"96","DOI":"10.4218\/etrij.2023-0356","article-title":"Alzheimer\u2019s disease recognition from spontaneous speech using large language models","volume":"46","author":"Bang","year":"2024","journal-title":"ETRI J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jon, H.J., Jung, H., Jin, L., and Kim, E.Y. (2025). Identifying Alzheimer\u2019s Disease Across Cognitive Impairment Spectrum Using Acoustic Features Only. Pattern Recognition, Springer.","DOI":"10.1007\/978-3-031-78195-7_27"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sun, J., Liu, Y., Wu, H., Jing, P., and Ji, Y. (2022). A novel deep learning approach for diagnosing Alzheimer\u2019s disease based on eye-tracking data. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.972773"},{"key":"ref_12","unstructured":"Sriram, H., Conati, C., and Field, T. (2023, January 9\u201313). Classification of Alzheimer\u2019s Disease with Deep Learning on Eye-Tracking Data. Proceedings of the 25th International Conference on Multimodal Interaction (ICMI \u201923), Paris, France."},{"key":"ref_13","first-page":"e70238","article-title":"Eye tracking as a diagnostic tool in Alzheimer\u2019s disease, mild cognitive impairment, and related dementias: A systematic review","volume":"18","author":"Juantorena","year":"2026","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T.T., Lee, H.H., Huang, L.K., Hu, C.J., Yeh, C.Y., Yang, W.C.V., and Lin, M.C. (2023). Heterogeneity of Alzheimer\u2019s disease identified by neuropsychological test profiling. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0292527"},{"key":"ref_15","unstructured":"Patterson, C. (2018). World Alzheimer Report 2018, Alzheimer\u2019s Disease International."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/S0140-6736(20)30367-6","article-title":"Dementia prevention, intervention, and care: 2020 report of the Lancet Commission","volume":"396","author":"Livingston","year":"2020","journal-title":"Lancet"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2551","DOI":"10.1001\/jama.2014.13806","article-title":"The diagnosis and management of mild cognitive impairment: A clinical review","volume":"312","author":"Langa","year":"2014","journal-title":"JAMA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1016\/j.neuroimage.2011.09.085","article-title":"Individual subject classification for Alzheimer\u2019s disease based on incremental learning using a spatial frequency representation of cortical thickness data","volume":"59","author":"Cho","year":"2012","journal-title":"NeuroImage"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhou, Y., Wang, R., Cao, H., Reid, S., Gao, R., Han, D., and Initiative, A.D.N. (2016). Potential Clinical Value of Multiparametric PET in the Prediction of Alzheimer\u2019s Disease Progression. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154406"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00234-008-0463-x","article-title":"Support vector machine-based classification of Alzheimer\u2019s disease from whole-brain anatomical MRI","volume":"51","author":"Magnin","year":"2009","journal-title":"Neuroradiology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e100485","DOI":"10.1136\/bmjhci-2021-100485","article-title":"Operationalising fairness in medical AI adoption: Detection of early Alzheimer\u2019s disease with 2D CNN","volume":"29","author":"Heising","year":"2022","journal-title":"BMJ Health Care Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Folego, G., Weiler, M., Casseb, R.F., Pires, R., and Rocha, A. (2020). Alzheimer\u2019s Disease Detection Through Whole-Brain 3D-CNN MRI. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.534592"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13195-023-01181-1","article-title":"Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer\u2019s disease using EEG technology","volume":"15","author":"Jiao","year":"2023","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_24","first-page":"S94","article-title":"EEG biomarkers for Alzheimer\u2019s disease: A novel automated pipeline for detecting and monitoring disease progression","volume":"108","author":"Simmatis","year":"2025","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Zhao, Y. (2025). The role of quantitative EEG biomarkers in Alzheimer\u2019s disease and mild cognitive impairment: Applications and insights. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1522552"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109321","DOI":"10.1016\/j.bspc.2025.109321","article-title":"Alzheimer\u2019s disease classification from EEG using a multiscale temporal deep network","volume":"114","author":"Zini","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Aljanabi, E., and T\u00fcrker, \u0130. (2025). Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer\u2019s Disease Detection. Diagnostics, 15.","DOI":"10.3390\/diagnostics15111441"},{"key":"ref_28","first-page":"e79881","article-title":"Blood-based \u03b2-amyloid and phosphorylated tau (p-tau) biomarkers in Alzheimer\u2019s disease: A systematic review of their diagnostic potential","volume":"17","author":"Dasari","year":"2025","journal-title":"Cureus"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e108988","DOI":"10.1002\/alz70861_108988","article-title":"The Impact of Comorbid Depression on Cognitive Reserve in Patients with Alzheimer\u2019s Disease: A Case-Control Study","volume":"21","author":"Kira","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e70987","DOI":"10.1002\/alz.70987","article-title":"Plasma P-tau217, GFAP, and NfL as biomarkers for Alzheimer\u2019s disease: Role in disease stratification, pathological progression, and cognitive decline","volume":"21","author":"Pan","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5860","DOI":"10.1002\/alz.13390","article-title":"Artificial intelligence for biomarker discovery in Alzheimer\u2019s disease and dementia","volume":"19","author":"Winchester","year":"2023","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103142","DOI":"10.1016\/j.eclinm.2025.103142","article-title":"Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer\u2019s disease diagnosis: A multicohort diagnostic study","volume":"81","author":"Jiao","year":"2025","journal-title":"eClinicalMedicine"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vigo, I., Coelho, L., and Reis, S. (2022). Speech- and language-based classification of Alzheimer\u2019s Disease: A systematic review. Bioengineering, 9.","DOI":"10.3390\/bioengineering9010027"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13195-022-01131-3","article-title":"Deep learning-based speech analysis for Alzheimer\u2019s disease detection: A literature review","volume":"14","author":"Yang","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115538","DOI":"10.1016\/j.psychres.2023.115538","article-title":"Speech and language processing with deep learning for dementia diagnosis: A systematic review","volume":"329","author":"Shi","year":"2023","journal-title":"Psychiatry Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Noto, S., Sekiyama, Y., Nagata, R., Yamamoto, G., and Tamura, T. (2024). Analysis of Speech Features in Alzheimer\u2019s Disease with Machine Learning: A Case-Control Study. Healthcare, 12.","DOI":"10.3390\/healthcare12212194"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8345","DOI":"10.1167\/iovs.13-12877","article-title":"Eye movement alterations during reading in patients with early Alzheimer disease","volume":"54","author":"Mandolesi","year":"2013","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/JAD-141173","article-title":"Eye movements in Alzheimer\u2019s disease","volume":"44","author":"Molitor","year":"2015","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vallejo, V., Cazzoli, D., Rampa, L., Zito, G.A., Feuerstein, F., Gruber, N., M\u00fcri, R.M., Mosimann, U.P., and Nef, T. (2016). Effects of Alzheimer\u2019s Disease on Visual Target Detection: A \u201cPeripheral Bias\u201d. Front. Aging Neurosci., 8.","DOI":"10.3389\/fnagi.2016.00200"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Readman, M.R., Polden, M., Gibbs, M.C., Wareing, L., and Crawford, T.J. (2021). The Potential of Naturalistic Eye Movement Tasks in the Diagnosis of Alzheimer\u2019s Disease: A Review. Brain Sci., 11.","DOI":"10.3390\/brainsci11111503"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e11143","DOI":"10.2196\/11143","article-title":"Device-Embedded Cameras for Eye Tracking\u2013Based Cognitive Assessment: Validation with Paper-Pencil and Computerized Cognitive Composites","volume":"20","author":"Bott","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Greenaway, A.M., Nasuto, S., Ho, A., and Hwang, F. (2021, January 18\u201322). Is home-based webcam eye-tracking with older adults living with and without Alzheimer\u2019s disease feasible?. Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility, Virtual.","DOI":"10.1145\/3441852.3476565"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neurobiolaging.2019.03.022","article-title":"Brain reserve, cognitive reserve, compensation, and maintenance: Operationalization, validity, and mechanisms of cognitive resilience","volume":"83","author":"Stern","year":"2019","journal-title":"Neurobiol. Aging"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1001\/archneur.61.1.73","article-title":"Cognitive Reserve-Mediated Modulation of Positron Emission Tomographic Activations During Memory Tasks in Alzheimer Disease","volume":"61","author":"Scarmeas","year":"2004","journal-title":"Arch. Neurol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109438","DOI":"10.1016\/j.compbiomed.2024.109438","article-title":"Multi-scale multimodal deep learning framework for Alzheimer\u2019s disease diagnosis","volume":"184","author":"Abdelaziz","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s40846-023-00801-3","article-title":"Explainable deep-learning-based diagnosis of Alzheimer\u2019s disease using multimodal input fusion of PET and MRI images","volume":"43","author":"Odusami","year":"2023","journal-title":"J. Med. Biol. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5210","DOI":"10.1038\/s41598-024-56001-9","article-title":"Automated detection of Alzheimer\u2019s disease: A multi-modal approach with 3D MRI and amyloid PET","volume":"14","author":"Castellano","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_48","first-page":"231","article-title":"Functional neuroimaging (fMRI, PET and MEG): What do we measure?","volume":"63","author":"Gosseries","year":"2008","journal-title":"Rev. Med. Liege"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"40334","DOI":"10.1038\/s41598-025-28456-x","article-title":"Functional PET for mapping metabolic dynamics in Parkinson\u2019s disease","volume":"15","author":"Heinecke","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"133","DOI":"10.3991\/ijoe.v22i01.58513","article-title":"Parkinson\u2019s Disease Detection Through Offline Handwriting Analysis: A CNN-Based Approach","volume":"22","author":"Bensefia","year":"2026","journal-title":"Int. J. Online Biomed. Eng. (iJOE)"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ilias, L., and Askounis, D. (2022). Multimodal deep learning models for detecting dementia from speech and transcripts. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.830943"},{"key":"ref_52","first-page":"1","article-title":"Machine learning approaches for dementia detection through speech and gait analysis: A systematic literature review","volume":"100","author":"Fleyeh","year":"2024","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e70051","DOI":"10.1111\/coin.70051","article-title":"Speech2Dementia: A Novel Deep Learning Framework Integrating Enhanced CNN and Large Language Models for Automatic Detection of Alzheimer\u2019s Dementia","volume":"41","author":"Chakravarthi","year":"2025","journal-title":"Comput. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s13311-022-01185-z","article-title":"Heterogeneity in Alzheimer\u2019s disease diagnosis and progression rates: Implications for therapeutic trials","volume":"19","author":"Duara","year":"2022","journal-title":"Neurotherapeutics"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1212\/01.wnl.0000335972.35970.70","article-title":"Cognitive profiles in dementia: Alzheimer disease vs healthy brain aging","volume":"71","author":"Johnson","year":"2008","journal-title":"Neurology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"\u201cMini-mental state\u201d: A practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"ref_57","first-page":"107","article-title":"The development of Korean activities of daily living (K-ADL) and Korean instrumental activities of daily living (K-IADL) scale","volume":"6","author":"Won","year":"2002","journal-title":"J. Korean Geriatr. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1177\/089198873258818","article-title":"Caregiver-administered neuropsychiatric inventory (CGA-NPI)","volume":"17","author":"Kang","year":"2004","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1017\/S1041610297004870","article-title":"Clinical dementia rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type","volume":"9","author":"Morris","year":"1997","journal-title":"Int. Psychogeriatr."},{"key":"ref_60","unstructured":"Edition, F. (1994). Diagnostic and statistical manual of mental disorders. DSM IV, American Psychiatric Association. [4th ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, R.C., Plakal, M., Platt, D., Saurous, R.A., and Seybold, B. (2017, January 5\u20139). CNN architectures for large-scale audio classification. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952132"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wang, Z., Jin, J.S., Han, X., and Li, C. (2019). Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network. Sensors, 19.","DOI":"10.3390\/s19122730"},{"key":"ref_63","first-page":"12449","article-title":"wav2vec 2.0: A framework for self-supervised learning of speech representations","volume":"33","author":"Baevski","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Nam, U., Lee, K., Ko, H., Lee, J.Y., and Lee, E.C. (2020). Analyzing facial and eye movements to screen for Alzheimer\u2019s disease. Sensors, 20.","DOI":"10.3390\/s20185349"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jung, H., Ham, S., Kil, H., Shin, J.E., and Kim, E.Y. (2025). GazeMap: Dual-Pathway CNN Approach for Diagnosing Alzheimer\u2019s Disease from Gaze and Head Movements. Mathematics, 13.","DOI":"10.3390\/math13111867"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Ververas, E., Kotsia, I., and Zafeiriou, S. (2020, January 13\u201319). Retinaface: Single-shot multi-level face localisation in the wild. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Abdelrahman, A.A., Hempel, T., Khalifa, A., Al-Hamadi, A., and Dinges, L. (2023). L2cs-net: Fine-grained gaze estimation in unconstrained environments. Proceedings of the 2023 8th International Conference on Frontiers of Signal Processing (ICFSP), IEEE.","DOI":"10.1109\/ICFSP59764.2023.10372944"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Hempel, T., Abdelrahman, A.A., and Al-Hamadi, A. (2022). 6d rotation representation for unconstrained head pose estimation. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP46576.2022.9897219"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.apnu.2016.01.015","article-title":"Comparing the sensitivity, specificity, and predictive values of the Montreal Cognitive Assessment and Mini-Mental State Examination when screening people for mild cognitive impairment and dementia in Chinese population","volume":"30","author":"Tsai","year":"2016","journal-title":"Arch. Psychiatr. Nurs."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/ggi.12237","article-title":"Validity of the C antonese C hinese M ontreal C ognitive A ssessment in Southern Chinese","volume":"15","author":"Chu","year":"2015","journal-title":"Geriatr. Gerontol. Int."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1177\/1533317516679304","article-title":"Validity of the Georgian Montreal Cognitive Assessment for the screening of mild cognitive impairment and dementia","volume":"32","author":"Janelidze","year":"2017","journal-title":"Am. J. Alzheimer\u2019s Dis. Other Dement."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1017\/S1041610218001370","article-title":"Is the Montreal Cognitive Assessment (MoCA) screening superior to the Mini-Mental State Examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer\u2019s Disease (AD) in the elderly?","volume":"31","author":"Pinto","year":"2019","journal-title":"Int. Psychogeriatr."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3833\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:58:20Z","timestamp":1781762300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3833"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":72,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123833"],"URL":"https:\/\/doi.org\/10.3390\/s26123833","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]}}}