Use of Speech Analyses within a Mobile Application for the Assessment of Cognitive Impairment in Elderly People
Abstract
Background Various types of dementia and Mild Cognitive Impairment (MCI) are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progress ion. Therefore, automatic speech analytics provided by a mobile application may be a useful tool in providing additional indicators for assessment and detection of early stage dementia and MCI. Method 165 participants (subjects with subjective cognitive impairment (SCI), MCI patients, Alzheimer's disease (AD) and mixed dementia (MD) patients) were recorded with a mobile application while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description, counting down and a free speech task. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their 'power' to distinguish between SCI, MCI, AD and MD. The second step included training automatic classifiers for detecting MCI and AD, based on machine learning methods, and testing the detection accuracy. Results The fluency and free speech tasks obtain the highest accuracy rates of classifying AD vs. MD vs. MCI vs. SCI. Using the data, we demonstrated classification accuracy as follows: SCI vs. AD = 92% accuracy; SCI vs. MD = 92% accuracy; SCI vs. MCI = 86% accuracy and MCI vs. AD = 86%. Conclusions Our results…
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Researchers are actively investigating ways to assess cognitive impairment and dementia using mobile applications as well as conversational SAR acting as a psychologist.
“Furthermore, researchers are actively investigating ways to assess cognitive impairment and dementia using mobile applications [123], [124] as well as conversational SAR acting as a psychologist [125].”
From Conversational Affective Social Robots for Ageing and Dementia Support (2022) · cites this paper as [123]
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Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854 · see citing works
Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J. (2018) · Current Alzheimer Research · doi:10.2174/1567205014666171121114930 · Cited by 290 · see citing works
Piau, A., Wild, K., Mattek, N., & Kaye, J. (2019) · Journal of Medical Internet Research · doi:10.2196/12785 · Cited by 252 · see citing works
García, S. D. L. F., Ritchie, C. W., & Luz, S. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06047 · Cited by 265 · see citing works
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Koo, B. M., & Vizer, L. M. (2019) · Innovation in Aging · doi:10.1093/geroni/igy038 · Cited by 194 · see citing works
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Martínez‐Nicolás, I., Llorente, T. E., Martínez‐Sánchez, F., & Meilán, J. J. G. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.620251 · Cited by 182 · see citing works
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Li, R., Wang, X., Lawler, K., Garg, S., Bai, Q., & Alty, J. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104030 · Cited by 144 · see citing works
Khatun, S., Morshed, B. I., & Bidelman, G. M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2911970 · Cited by 109 · see citing works
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Robin, J., Xu, M., Kaufman, L. D., & Simpson, W. S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.749758 · Cited by 68 · see citing works
Teipel, S. J., König, A., Hoey, J., Kaye, J. A., Krüger, F., Robillard, J. M., Kirste, T., & Babiloni, C. (2018) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2018.05.003 · Cited by 66 · see citing works
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Bertini, F., Allevi, D., Lutero, G., Calzà, L., & Montesi, D. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2021.101298 · Cited by 66 · see citing works
Díaz-Orueta, U., Blanco‐Campal, A., Lamar, M., Libon, D. J., & Burke, T. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.00361 · Cited by 64 · see citing works
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Whelan, R., Barbey, F. M., Cominetti, M. R., Gillan, C. M., & Rosická, A. M. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02237-w · Cited by 63 · see citing works
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Bertini, F., Allevi, D., Lutero, G., Montesi, D., & Calzà, L. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3469089 · Cited by 49 · see citing works
Shimoda, A., Li, Y., Hayashi, H., & Kondo, N. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253988 · Cited by 39 · see citing works
Ivanova, O., Martínez‐Nicolás, I., & Meilán, J. J. G. (2024) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12888 · Cited by 38 · see citing works
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Andargoli, A. E., Ulapane, N. N. B., Nguyen, T. A., Shuakat, M. N., Zelcer, J., & Wickramasinghe, N. S. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102815 · Cited by 39 · see citing works
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Ambrosini, E., Caielli, M., Milis, M., Loizou, C. P., Azzolino, D., Damanti, S., Bertagnoli, L., Cesari, M., Moccia, S., Cid, M., et al. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856768 · Cited by 37 · see citing works
Qiao, Y., Xie, X., Lin, G., Zou, Y., Chen, S., Ren, R., & Wang, G. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191056 · Cited by 34 · see citing works
Jang, H., Soroski, T., Rizzo, M., Barral, O., Harisinghani, A., Newton‐Mason, S. M., Granby, S., Vasco, T. D. C., Lewis, C., Tutt, P., et al. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.716670 · Cited by 37 · see citing works
Ivanova, O., Meilán, J. J. G., Martínez‐Sánchez, F., Martínez‐Nicolás, I., Llorente, T. E., & Carcavilla-González, N. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101341 · Cited by 35 · see citing works
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Gupta, A. S. (2022) · Seminars in Neurology · doi:10.1055/s-0041-1741495 · Cited by 32 · see citing works
Yamada, Y., Shinkawa, K., Kobayashi, M., Nishimura, M., Nemoto, M., Tsukada, E., Ota, M., Nemoto, K., & Arai, T. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.653904 · Cited by 29 · see citing works
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Sanborn, V. E., Ostrand, R., Ciesla, J. A., & Gunstad, J. (2020) · Applied Neuropsychology Adult · doi:10.1080/23279095.2020.1864733 · Cited by 27 · see citing works
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Wang, T., Hong, Y., Wang, Q., Su, R., Ng, M. L., Xu, J. L., Wang, L., & Yan, N. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201387 · Cited by 23 · see citing works
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Park, C.-Y., Kim, M., Shim, Y., Ryoo, N., Choi, H., Jeong, H. T., Yun, G., Lee, H., Kim, H., Kim, S., et al. (2024) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2024.23.1.1 · Cited by 22 · see citing works
Al-Hammadi, M., Fleyeh, H. A., Åberg, A. C., Halvorsen, K., & Thomas, I. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231459 · Cited by 23 · see citing works
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Takeshige‐Amano, H., Oyama, G., Ogawa, M., Fusegi, K., Kambe, T., Shiina, K., Ueno, S., Okuzumi, A., Hatano, T., Motoi, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77220-0 · Cited by 23 · see citing works
Alghamdi, M. A. (2025) · Pharmaceuticals · doi:10.3390/ph18071041 · Cited by 22 · see citing works
Gagliardi, G. (2023) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12870 · Cited by 25 · see citing works
Ambrosini, E., Giangregorio, C., Lomurno, E., Moccia, S., Milis, M., Loizou, C. P., Azzolino, D., Cesari, M., Gala, M. C., de Isla, C. G., et al. (2024) · JMIR Aging · doi:10.2196/50537 · Cited by 20 · see citing works
Diaz-Asper, C. M., Chandler, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2022) · Cortex · doi:10.1016/j.cortex.2022.08.005 · Cited by 19 · see citing works
Gregory, S., Linz, N., König, A., Langel, K., Pullen, H., Luz, S. F., Harrison, J., & Ritchie, C. W. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-052250 · Cited by 18 · see citing works
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Šubert, M., Šimek, M., Novotný, M., Tykalová, T., Bezdíček, O., Růžička, E., Šonka, K., Dušek, P., & Rusz, J. (2022) · Movement Disorders · doi:10.1002/mds.29140 · Cited by 18 · see citing works
Zhao, X., Hu, R., Wen, H., Xu, G., Pang, T., He, X., Zhang, Y., Zhang, J., Chen, C., Wu, X., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.899729 · Cited by 18 · see citing works
König, A., Linz, N., Baykara, E., Tröger, J., Ritchie, C. W., Saunders, S., Teipel, S. J., Köhler, S., Sánchez‐Benavides, G., Grau‐Rivera, O., et al. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.11 · Cited by 20 · see citing works
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Kaser, A. N., Lacritz, L. H., Winiarski, H. R., Gabirondo, P., Schaffert, J., Coca, A. J., Jiménez-Raboso, J., Rojo, T., Zaldua, C., Honorato, I., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1342907 · Cited by 17 · see citing works
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Parsapoor, M. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13395 · Cited by 15 · see citing works
Martínez‐Nicolás, I., Martínez‐Sánchez, F., Ivanova, O., & Meilán, J. J. G. (2023) · Scientific Reports · doi:10.1038/s41598-023-36804-y · Cited by 14 · see citing works
Neurocognitive correlates of semantic memory navigation in Parkinson’s disease
Toro-Hernández, F. D., Migeot, J., Marchant, N., Olivares, D., Ferrante, F. J., González-Gómez, R., Campo, C. G., Fittipaldi, S., Rojas-Costa, G. M., Moguilner, S., et al. (2024) · npj Parkinson s Disease · doi:10.1038/s41531-024-00630-4 · Cited by 14 · see citing works
Dementia Scale Classification Based on Ubiquitous Daily Activity and Interaction Sensing
Okada, S., Inoue, K., Imai, T., Noguchi, M., & Kuwamura, K. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925454 · Cited by 14 · see citing works
Acceptability of collecting speech samples from the elderly via the telephone
Diaz-Asper, C. M., Chandler, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2021) · Digital Health · doi:10.1177/20552076211002103 · Cited by 14 · see citing works
Yamada, Y., Shinkawa, K., Nemoto, M., & Arai, T. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.712251 · Cited by 13 · see citing works
Fraser, K., Linz, N., Li, B., Fors, K. L., Rudzicz, F., König, A., Alexandersson, J., Robert, P. H., & Kokkinakis, D. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1367 · Cited by 13 · see citing works
Self-citationEgas-López, J. V., Balogh, R., Imre, N., Hoffmann, I., Szabó, M. K., Tóth, L., Pákáski, M., Kálmán, J., & Gosztolya, G. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101377 · Cited by 12 · see citing works
Kálmán, J., Devanand, D. P., Gosztolya, G., Balogh, R., Imre, N., Tóth, L., Hoffmann, I., Kovács, I., Vincze, V., & Pákáski, M. (2022) · Current Alzheimer Research · doi:10.2174/1567205019666220418155130 · Cited by 12 · see citing works
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Lima, M. R. D., Capstick, A., Geranmayeh, F., Nilforooshan, R., Matarić, M. J., Vaidyanathan, R., & Barnaghi, P. M. (2025) · Communications Medicine · doi:10.1038/s43856-025-01263-1 · Cited by 13 · see citing works
Yamada, Y., Shinkawa, K., Kobayashi, M., Takagi, H., Nemoto, M., Nemoto, K., & Arai, T. (2021) · Journal of Medical Internet Research · doi:10.2196/27667 · Cited by 10 · see citing works
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Chen, S., Li, L., Han, S., Luo, W., Wang, W., Yang, Y., Wang, X., Zhang, W., Chen, M., & Wang, Z. (2024) · Interdisciplinary Nursing Research · doi:10.1097/nr9.0000000000000065 · Cited by 10 · see citing works
Šubert, M., Tykalová, T., Novotný, M., Dušek, P., Klempíř, J., & Rusz, J. (2025) · Journal of Neurology · doi:10.1007/s00415-024-12828-w · Cited by 9 · see citing works
Gramkow, M. H., Gleerup, H. S., Simonsen, A. H., Waldemar, G., & Frederiksen, K. S. (2026) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2026.125734 · Cited by 9 · see citing works
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Avola, D., Cinque, L., Fagioli, A., Foresti, G. L., Marini, M. R., Mecca, A., & Pannone, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06427-2_41 · Cited by 9 · see citing works
Kiyoshige, E., Ogata, S., Kwon, N., Nakaoku, Y., Hayashi, C., Blaylock, N., Brueckner, R., Subramanian, V., OConnell, H. J., Yoshikawa, Y., et al. (2025) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2025.101598 · Cited by 8 · see citing works
Loizou, C. P. (2021) · Speech Communication · doi:10.1016/j.specom.2021.04.001 · Cited by 8 · see citing works
MemScreen: A smartphone application for detection of mild cognitive impairment: A validation study
Dumurgier, J., Paquet, C., Hugon, J., Planche, V., Gaubert, S., Epelbaum, S., Bombois, S., Teichmann, M., Lévy, R., Baudouin, E., et al. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100077 · Cited by 7 · see citing works
Smartwatch-derived Acoustic Markers for Deficits in Cognitively Relevant Everyday Functioning
Yamada, Y., Shinakwa, K., Kobayashi, M., Nemoto, M., Ota, M., Nemoto, K., & Arai, T. (2023) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh60066.2023.00015 · Cited by 8 · see citing works
HAMRICK, P. J. J., Sanborn, V. E., Ostrand, R., & Gunstad, J. (2023) · JMIR Aging · doi:10.2196/46483 · Cited by 7 · see citing works
Clinical translation of fluid, imaging, and digital biomarkers for Alzheimer’s disease
Bacci, J. R., Karagianni, S., Alexopoulou, Z.-S., Moukaled, S., Tato-Fernández, C., Arunachalam, P., Aslanyan, A., Aye, S., Rocha, A. S. L., Crugel, M., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01921-5 · Cited by 6 · see citing works
Niemelä, M., von Bonsdorff, M. B., Äyrämö, S., Kärkkäinen, T., & Wellbeing Services County of Central Finland, F. (2024) · Applied Computing and Intelligence · doi:10.3934/aci.2024004 · Cited by 7 · see citing works
Liu, L.-Y., Xing, Y., Zhang, Z., Zhang, Q.-G., Dong, M., Wang, H., Cai, L., Wang, X., & Tang, Y. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230416 · Cited by 6 · see citing works
Detection of Alzheimer’s Disease Through Speech Features and Machine Learning Classifiers
Gulapalli, A. S., & Mittal, V. K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-6309-3_59 · Cited by 6 · see citing works
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Farrús, M., & Codina, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09272 · Cited by 6 · see citing works
de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5 · see citing works
Analysis of Speech Features in Alzheimer’s Disease with Machine Learning: A Case-Control Study
Noto, S., Sekiyama, Y., Nagata, R., Yamamoto, G., & Tamura, T. (2024) · Healthcare · doi:10.3390/healthcare12212194 · Cited by 7 · see citing works
Linguistic Markers in Spontaneous Speech: Insights into Subjective Cognitive Decline (Review)
Segkouli, S., Γκιόκα, Μ., Kokkas, S., Votis, K., Valero, S., Miguel, A., Antoniades, A., Charalambous, E., & Manias, G. (2025) · Healthcare · doi:10.3390/healthcare13222888 · Cited by 5 · see citing works
Gregory, S., Harrison, J., Herrmann, J., Hunter, M. B., Jenkins, N. D., König, A., Linz, N., Luz, S. F., Mallick, E., Pullen, H., et al. (2023) · Frontiers in Dementia · doi:10.3389/frdem.2023.1271156 · Cited by 6 · see citing works
Plug-and-Play Microphones for Recording Speech and Voice with Smart Devices
Noffs, G., Cobler-Lichter, M., Perera, T., Kolbe, S. C., Butzkueven, H., Boonstra, F. M. C., van der Walt, A., & Vogel, A. P. (2024) · Folia Phoniatrica et Logopaedica · doi:10.1159/000535152 · Cited by 5 · see citing works
Erickson, C. M., Wexler, A., & Largent, E. A. (2023) · Informatics for Health and Social Care · doi:10.1080/17538157.2023.2294203 · Cited by 6 · see citing works
Voice biomarkers in middle and later adulthood as predictors of cognitive changes
Mahon, E., & Lachman, M. E. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1422376 · Cited by 4 · see citing works
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Oh, C., & Park, M. S. (2025) · Digital Health · doi:10.1177/20552076251360901 · Cited by 4 · see citing works
Age-Invariant Speaker Embedding for Diarization of Cognitive Assessments
Xu, S. S., Mak, M., Wong, K. H., Meng, H. M. L., & Kwok, T. C. Y. (2021) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp49672.2021.9362084 · Cited by 4 · see citing works
An automated speech analysis system for the detection of cognitive decline in elderly
Loizou, C. P., & Pantzaris, M. (2023) · International Journal of Speech Technology · doi:10.1007/s10772-023-10016-1 · Cited by 4 · see citing works
Hu, Z., Zhang, Z., Li, H., & Yang, L. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02584-0 · Cited by 3 · see citing works
Determining factors on active aging in Asia and Oceania: a systematic review
Liu, Y., Verma, R., & Theng, Y. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823761-8.00023-9 · Cited by 3 · see citing works
Yang, Y., Zhao, X., Zhao, P., Ying, D., Wang, J., Jiang, Y., & Wan, Q. (2025) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2025-113759 · Cited by 2 · see citing works
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Kim, M., Youn, Y. C., Won, Y., Choi, H., Shim, Y., Ryoo, N., Jeong, H. T., Yun, G., Lee, H., & Kim, S. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03268-1 · Cited by 2 · see citing works
Choi, Y. H., Kim, H.-J., Hong, S., Baek, C., Wang, B., Shim, Y., Hong, Y. J., Byun, S., Song, I., Na, S., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251401202 · Cited by 2 · see citing works
Yoon, B., Lee, H.-J., Kim, W., Shim, Y. S., Lim, E. Y., Cho, A.-H., Song, I. W., Hong, Y. J., Shin, H. E., Ku, B. D., et al. (2026) · Neurological Sciences · doi:10.1007/s10072-025-08658-x · Cited by 2 · see citing works
König, A., Tröger, J., Mallick, E., Linz, N., Ritchie, C. W., Gregory, S., Hunter, M. B., Johnson, K., Sánchez‐Benavides, G., Grau‐Rivera, O., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71462 · Cited by 2 · see citing works
Speech-Based Digital Biomarkers for Alzheimer’s Research
Schäfer, S., Herrmann, J., Tovar, S., Linz, N., & Tröger, J. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3774-6_18 · Cited by 2 · see citing works
Digital Measures of Drawing Process to Predict Multiple Cognitive and Gait Measures in Older Adults
Yamada, Y., Kobayashi, M., Shinakwa, K., Nemoto, M., Ota, M., Nemoto, K., & Arai, T. (2024) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh62654.2024.00042 · Cited by 2 · see citing works
Estimation of Dementia Severity Using SVM based on Patient's Engagement Levels in Conversation
Yamazaki, R., Nishio, S., Shigenobu, K., Maalouly, E., & Ishiguro, H. (2021) · IEEE International Conference on Intelligence and Safety for Robotics (ISR) · doi:10.1109/isr50024.2021.9419571 · Cited by 2 · see citing works
Fandiño, J. C. R., & Salazar, A. M. (2021) · Revista Ciencias de la Salud · doi:10.12804/revistas.urosario.edu.co/revsalud/a.9877 · Cited by 2 · see citing works
Influence of ASR and Language Model on Alzheimer's Disease DetectionPreprint
Codina, J., Cámbara, G., Luque, J., & Farrús, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15704 · Cited by 2 · see citing works
Speaker-turn aware diarization for speech-based cognitive assessments
Xu, S. S., Ke, X., Mak, M., Wong, K. H., Meng, H. M. L., Kwok, T. C. Y., Gu, J. J., Zhang, J., Tao, W., & Chang, C. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1351848 · Cited by 1 · see citing works
Hegedűs, É., Pákáski, M., Gosztolya, G., Hoffmann, I., Kovács, I., & Kálmán, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308409 · Cited by 1 · see citing works
- Authors
- Konig, A., Satt, A., Sorin, A., Hoory, R., Derreumaux, A., David, R., & Robert, P. H.
- Affiliations
- EA CoBTeK, University of Nice Sophia Antipolis, Nice, France
- IBM (United States) (US)
- Cognition Behaviour Technology (FR)
- Venue
- Current Alzheimer Research
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-01-03
- Volume
- 15
- Issue
- 2
- Pages
- 120-129
- ISSN
- 1567-2050 (ISSN-L)
- OpenAlex
- W2752744672
- Cited by
- 119
- References
- 0
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 18 · 2023: 18 · 2022: 14 · 2021: 22 · 2020: 10 · 2019: 8 · 2018: 3 · 2017: 1
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Alzheimer Research (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Eli Lilly and Company
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- speech analysis, mild cognitive impairment, dementia detection, Alzheimer's disease, mixed dementia, subjective cognitive impairment, older adults, mobile application, verbal fluency, picture description, automatic classification, early diagnosis
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Dementia, Diagnosis, Computer-Assisted, Diagnosis, Differential, Female, Humans, Male, Neuropsychological Tests, Pattern Recognition, Automated, Speech, Cognitive Dysfunction, Mobile Applications
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