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Biometrics-Based Mobile User Authentication for the Elderly: Accessibility, Performance, and Method Design

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Pages 2153-2167 | Received 30 Jun 2022, Accepted 29 Nov 2022, Published online: 01 Jan 2023

References

  • Açık, A., Sarwary, A., Schultze-Kraft, R., Onat, S., & König, P. (2010). Developmental changes in natural viewing behavior: Bottom-up and top-down differences between children, young adults and older adults. Frontiers in Psychology, 1, 207. https://doi.org/10.3389/fpsyg.2010.00207
  • Ahmed, E., DeLuca, B., Hirowski, E., Magee, C., Tang, I., & Coppola, J. F. (2017). Biometrics: Password replacement for elderly? In 2017 IEEE Long Island Systems, Applications and Technology Conference (LISAT) (pp. 1–6). https://doi.org/10.1109/LISAT.2017.8001958
  • Allah, K. K., Ismail, N. A., & Elrobaa, H. (2021). Empathy map instrument for analyzing human–computer interaction in using web search UI by elderly users. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1–5). https://doi.org/10.1109/ICOTEN52080.2021.9493548
  • Al-Showarah, S. A. (2019). Dynamic recognition for user age-group classification using hand-writing based finger on smartphones. In 2019 10th International Conference on Information and Communication Systems (ICICS) (pp. 140–146). https://doi.org/10.1109/IACS.2019.8809083
  • Alt, F., & Schneegass, S. (2022). Beyond passwords—Challenges and opportunities of future authentication. IEEE Security & Privacy, 20(1), 82–86. https://doi.org/10.1109/MSEC.2021.3127459
  • Andronico, P., Minutoli, S., & Kuruoglu, E. E. (2014). Understanding elderly needs for designing a digitally extended environment via tablets. In C. Stephanidis (Ed.), HCI International 2014—Posters’ Extended Abstracts (pp. 284–287). Springer International Publishing. https://doi.org/10.1007/978-3-319-07854-0_50
  • Azimi, M., Rasoulinejad, S. A., & Pacut, A. (2019a). The effects of gender factor and diabetes mellitus on the iris recognition system’s accuracy and reliability. In 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) (pp. 273–278). https://doi.org/10.23919/SPA.2019.8936757
  • Azimi, M., Rasoulinejad, S. A., & Pacut, A. (2019b). Iris recognition under the influence of diabetes. Biomedizinische Technik. Biomedical Engineering, 64(6), 683–689. https://doi.org/10.1515/bmt-2018-0190
  • Bainbridge, W. A. (2019). Chapter one – Memorability: How what we see influences what we remember. In K. D. Federmeier & D. M. Beck (Eds.), Psychology of learning and motivation (Vol. 70, pp. 1–27). Academic Press. https://doi.org/10.1016/bs.plm.2019.02.001
  • Bazrafkan, S., & Corcoran, P. (2018). Enhancing iris authentication on handheld devices using deep learning derived segmentation techniques. In 2018 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1–2). https://doi.org/10.1109/ICCE.2018.8326219
  • Blanco-Gonzalo, R., Sanchez-Reillo, R., Martinez-Normand, L., Fernandez-Saavedra, B., & Liu-Jimenez, J. (2015). Accessible mobile biometrics for elderly. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (pp. 419–420). https://doi.org/10.1145/2700648.2811332
  • Bong, W. K., Chen, W., & Bergland, A. (2018). Tangible user interface for social interactions for the elderly: A review of literature. Advances in Human–Computer Interaction, 2018(2018), 1–15. https://doi.org/10.1155/2018/7249378
  • Brooke, j. (1996). SUS: A “quick and dirty” usability scale. In Usability evaluation in industry. CRC Press.
  • Campbell, O. (2015). Designing for the elderly: Ways older people use digital technology differently. Smashing Magazine. https://www.smashingmagazine.com/2015/02/designing-digital-technology-for-the-elderly/
  • Chakraborty, B., Nakano, K., Tokoi, Y., & Hashimoto, T. (2019). An approach for designing low cost deep neural network based biometric authentication model for smartphone user. In TENCON 2019 – 2019 IEEE Region 10 Conference (TENCON) (pp. 772–777). https://doi.org/10.1109/TENCON.2019.8929241
  • Chatzaki, C., Skaramagkas, V., Tachos, N., Christodoulakis, G., Maniadi, E., Kefalopoulou, Z., Fotiadis, D. I., & Tsiknakis, M. (2021). The smart-insole dataset: Gait analysis using wearable sensors with a focus on elderly and Parkinson’s patients. Sensors, 21(8), 2821. https://doi.org/10.3390/s21082821
  • Clegg, A., Young, J., Iliffe, S., Rikkert, M. O., & Rockwood, K. (2013). Frailty in elderly people. The Lancet, 381(9868), 752–762. https://doi.org/10.1016/S0140-6736(12)62167-9
  • Coren, S., & Hakstian, A. R. (1988). Color vision screening without the use of technical equipment: Scale development and cross-validation. Perception & Psychophysics, 43(2), 115–120. https://doi.org/10.3758/BF03214188
  • Corpus of Social Touch (2016). [Data set]. 4TU.ResearchData. https://doi.org/10.4121/uuid:5ef62345-3b3e-479c-8e1d-c922748c9b29
  • Corsetti, B., Sanchez-Reillo, R., Guest, R. M., & Santopietro, M. (2019). Face image analysis in mobile biometric accessibility evaluations. In 2019 International Carnahan Conference on Security Technology (ICCST) (pp. 1–5). https://doi.org/10.1109/CCST.2019.8888437
  • Crews, J. E., Chou, C.-F., Sekar, S., & Saaddine, J. B. (2017). The prevalence of chronic conditions and poor health among people with and without vision impairment, aged ≥65 years, 2010–2014. American Journal of Ophthalmology, 182, 18–30. https://doi.org/10.1016/j.ajo.2017.06.038
  • Damant, J., & Knapp, M. (2015, May). What are the likely changes in society and technology which will impact upon the ability of older adults to maintain social (extra-familial) networks of support now, in 2025 and in 2040? [Monograph]. Government Office for Science. https://www.gov.uk/government/organisations/government-office-for-science
  • Dandachi, G., El Hassan, B., & El Husseini, A. (2013). A novel identification/verification model using smartphone’s sensors and user behavior. In 2013 2nd International Conference on Advances in Biomedical Engineering (pp. 235–238). https://doi.org/10.1109/ICABME.2013.6648891
  • Fang, L., Zhu, H., Lv, B., Liu, Z., Meng, W., Yu, Y., Ji, S., & Cao, Z. (2020). HandiText: Handwriting recognition based on dynamic characteristics with incremental LSTM. ACM/IMS Transactions on Data Science, 1(4), 25:1–25:18. https://doi.org/10.1145/3385189
  • Fierrez, J., Galbally, J., Ortega-Garcia, J., Freire, M. R., Alonso-Fernandez, F., Ramos, D., Toledano, D. T., Gonzalez-Rodriguez, J., Siguenza, J. A., Garrido-Salas, J., Anguiano, E., Gonzalez-de-Rivera, G., Ribalda, R., Faundez-Zanuy, M., Ortega, J. A., Cardeñoso-Payo, V., Viloria, A., Vivaracho, C. E., Moro, Q. I., … Gracia-Roche, J. J. (2010). BiosecurID: A multimodal biometric database. Pattern Analysis and Applications, 13(2), 235–246. https://doi.org/10.1007/s10044-009-0151-4
  • Fisk, A. D., Czaja, S. J., Rogers, W. A., Charness, N., & Sharit, J. (2020). Designing for older adults: Principles and creative human factors approaches. CRC Press.
  • Giot, R., Dorizzi, B., & Rosenberger, C. (2015). A review on the public benchmark databases for static keystroke dynamics. Computers & Security, 55 (November), 46–61. https://doi.org/10.1016/j.cose.2015.06.008
  • González-Bañales, D. L., & Ortíz, L. E. S. (2017). Empathy map as a tool to analyze human–computer interaction in the elderly. In Proceedings of the 8th Latin American Conference on Human–Computer Interaction (pp. 1–3). https://doi.org/10.1145/3151470.3156642
  • Gorodnichy, D., & Chumakov, M. (2019). Analysis of the effect of ageing, age, and other factors on iris recognition performance using NEXUS scores dataset. IET Biometrics, 8(1), 29–39. https://doi.org/10.1049/iet-bmt.2018.5105
  • Gubernatorov, A. M., Teslenko, I. B., Muravyova, N. V., Vinogradov, D. V., & Subbotina, N. O. (2020). Information security of mobile systems. In E. G. Popkova (Ed.), Growth poles of the global economy: Emergence, changes and future perspectives (pp. 677–686). Springer International Publishing. https://doi.org/10.1007/978-3-030-15160-7_68
  • Hone, K. S., & Graham, R. (2000). Towards a tool for the subjective assessment of speech system interfaces (SASSI). Natural Language Engineering, 6(3&4), 287–303. https://doi.org/10.1017/S1351324900002497
  • Hong, H.-T., Su, T.-Y., Lee, P.-H., Hsieh, P.-C., & Chiu, M.-J. (2017). VisualLink: Strengthening the connection between hearing-impaired elderly and their family. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 67–73). https://doi.org/10.1145/3027063.3049269
  • Huang, Y.-P., Luo, S.-W., & Chen, E.-Y. (2002). An efficient iris recognition system. In Proceedings. International Conference on Machine Learning and Cybernetics (Vol. 1, pp. 450–454). https://doi.org/10.1109/ICMLC.2002.1176794
  • Huh, J.-H., & Seo, K. (2015). Design and implementation of the basic technology for solitary senior citizen’s lonely death monitoring system using PLC. Journal of Korea Multimedia Society, 18(6), 742–752. https://doi.org/10.9717/kmms.2015.18.6.742
  • Iancu, I., & Iancu, B. (2020). Designing mobile technology for elderly. A theoretical overview. Technological Forecasting and Social Change, 155 (June), 119977. https://doi.org/10.1016/j.techfore.2020.119977
  • Interaction Design Foundation (2016). Improving the user experience for the elderly. Interaction Design Foundation. https://www.interaction-design.org/literature/article/improving-the-user-experience-for-the-elderly
  • Iqbal, S., Irfan, M., Ahsan, K., Hussain, M. A., Awais, M., Shiraz, M., Hamdi, M., & Alghamdi, A. (2020). A novel mobile wallet model for elderly using fingerprint as authentication factor. IEEE Access. 8, 177405–177423. https://doi.org/10.1109/ACCESS.2020.3025429
  • Kappen, D. L., Mirza-Babaei, P., & Nacke, L. E. (2019). Older adults’ physical activity and exergames: A systematic review. International Journal of Human–Computer Interaction, 35(2), 140–167. https://doi.org/10.1080/10447318.2018.1441253
  • Kim, H., Heo, J., Shim, J., Kim, M., Park, S., & Park, S. (2007). Contextual research on elderly users’ needs for developing universal design mobile phone. In C. Stephanidis (Ed.), Universal access in human computer interaction. Coping with diversity (pp. 950–959). Springer. https://doi.org/10.1007/978-3-540-73279-2_106
  • Kirakowski, J., & Corbett, M. (1993). SUMI: The Software Usability Measurement Inventory. British Journal of Educational Technology, 24(3), 210–212. https://doi.org/10.1111/j.1467-8535.1993.tb00076.x
  • Klaib, A. F., Alsrehin, N. O., Melhem, W. Y., & Bashtawi, H. O. (2019). IoT smart home using eye tracking and voice interfaces for elderly and special needs people. Journal of Communications, 14(7), 614–621. https://doi.org/10.12720/jcm.14.7.614-621
  • Klein, Y., Djaldetti, R., Keller, Y., & Bachelet, I. (2017). Motor dysfunction and touch-slang in user interface data. Scientific Reports, 7(1), 4702. https://doi.org/10.1038/s41598-017-04893-1
  • Klimova, B., & Marešová, P. (2016). Elderly people and their attitude towards mobile phones and their applications—A review study. https://doi.org/10.1007/978-981-10-1536-6_5
  • Kobayashi, M., Kosugi, A., Takagi, H., Nemoto, M., Nemoto, K., Arai, T., & Yamada, Y. (2019). Effects of age-related cognitive decline on elderly user interactions with voice-based dialogue systems. In D. Lamas, F. Loizides, L. Nacke, H. Petrie, M. Winckler, & P. Zaphiris (Eds.), Human–computer interaction – INTERACT 2019 (pp. 53–74). Springer International Publishing. https://doi.org/10.1007/978-3-030-29390-1_4
  • Kocejko, T., & Wtorek, J. (2012). Gaze pattern lock for elders and disabled. In E. Piętka & J. Kawa (Eds.), Information technologies in biomedicine (pp. 589–602). Springer. https://doi.org/10.1007/978-3-642-31196-3_59
  • Kononova, A., Li, L., Kamp, K., Bowen, M., Rikard, R. V., Cotten, S., & Peng, W. (2019). The use of wearable activity trackers among older adults: Focus Group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR mHealth and uHealth, 7(4), e9832. https://doi.org/10.2196/mhealth.9832
  • Kowtko, M. A. (2014). Biometric authentication for older adults. In IEEE Long Island Systems, Applications and Technology (LISAT) Conference 2014 (pp. 1–6). https://doi.org/10.1109/LISAT.2014.6845213
  • Krishnamoorthy, S., Rueda, L., Saad, S., & Elmiligi, H. (2018). Identification of user behavioral biometrics for authentication using keystroke dynamics and machine learning. In Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications (pp. 50–57). https://doi.org/10.1145/3230820.3230829
  • Kunda, D., & Chishimba, M. (2021). A survey of android mobile phone authentication schemes. Mobile Networks and Applications, 26, 2558–2566. https://doi.org/10.1007/s11036-018-1099-7
  • Lanitis, A. (2010). A survey of the effects of aging on biometric identity verification. International Journal of Biometrics, 2(1), 34–52. https://doi.org/10.1504/IJBM.2010.030415
  • Lewis, J. E., & Neider, M. B. (2017). Designing wearable technology for an aging population. Ergonomics in Design: The Quarterly of Human Factors Applications, 25(3), 4–10. https://doi.org/10.1177/1064804616645488
  • Liu, S., Shao, W., Li, T., Xu, W., & Song, L. (2022). Recent advances in biometrics-based user authentication for wearable devices: A contemporary survey. Digital Signal Processing, 125 (June), 103120. https://doi.org/10.1016/j.dsp.2021.103120
  • Ma, K., Wang, X., Yang, X., Zhang, M., Girard, J. M., & Morency, L.-P. (2019). ElderReact: A multimodal dataset for recognizing emotional response in aging adults. In 2019 International Conference on Multimodal Interaction (pp. 349–357). https://doi.org/10.1145/3340555.3353747
  • Majumder, S., & Deen, M. J. (2019). Smartphone sensors for health monitoring and diagnosis. Sensors, 19(9), 2164. https://doi.org/10.3390/s19092164
  • Matsumoto, T., Matsumoto, H., Yamada, K., & Hoshino, S. (2002). Impact of artificial “gummy” fingers on fingerprint systems. IS&T/SPIE Electronic Imaging. https://doi.org/10.1117/12.462719
  • Meurer, J., Stein, M., Randall, D., & Wulf, V. (2018). Designing for way-finding as practices – A study of elderly people’s mobility. International Journal of Human–Computer Studies, 115 (July), 40–51. https://doi.org/10.1016/j.ijhcs.2018.01.008
  • Minaee, S., Azimi, E., & Abdolrashidi, A. (2019). FingerNet: Pushing the limits of fingerprint recognition using convolutional neural network. ArXiv:1907.12956 [Cs]. http://arxiv.org/abs/1907.12956
  • Mohammadi, A., Bhattacharjee, S., & Marcel, S. (2018). Deeply vulnerable: A study of the robustness of face recognition to presentation attacks. IET Biometrics, 7(1), 15–26. https://doi.org/10.1049/iet-bmt.2017.0079
  • Nedopil, C., Schauber, C., & Glende, S. (2013). Knowledge base: AAL stakeholders and their requirements. Ambient Assisted Living Association; A collection of characteristics and requirements of primary, secondary, and tertiary users of AAL solutions, and a guideline for user-friendly AAL design.
  • Nimrod, G. (2016). The hierarchy of mobile phone incorporation among older users. Mobile Media & Communication, 4(2), 149–168. https://doi.org/10.1177/2050157915617336
  • Orimo, H., Ito, H., Suzuki, T., Araki, A., Hosoi, T., & Sawabe, M. (2006). Reviewing the definition of “elderly. Geriatrics and Gerontology International, 6(3), 149–158. https://doi.org/10.1111/j.1447-0594.2006.00341.x
  • OU-ISIR Biometric Database (n.d.). Retrieved April 8, 2022, from http://www.am.sanken.osaka-u.ac.jp/BiometricDB/InertialGait.html
  • Pereira, C. R., Weber, S. A. T., Hook, C., Rosa, G. H., Papa, J. P. (2016). Deep learning-aided Parkinson’s disease diagnosis from handwritten dynamics. In 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 340–346). https://doi.org/10.1109/SIBGRAPI.2016.054
  • Petrovčič, A., Slavec, A., & Dolničar, V. (2018). The ten shades of silver: Segmentation of older adults in the mobile phone market. International Journal of Human–Computer Interaction, 34(9), 845–860. https://doi.org/10.1080/10447318.2017.1399328
  • Sadeghi, Z., Homayounvala, E., & Borhani, M. (2020). HCI for elderly, measuring visual complexity of webpages based on machine learning. In 2020 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–6). https://doi.org/10.1109/DICTA51227.2020.9363381
  • Sakdulyatham, R., Preeyanont, S., Lipikorn, R., & Watakakosol, R. (2017). User interface on smartphone for elderly users. International Journal of Automation and Smart Technology, 7(4), 147–155. https://doi.org/10.5875/ausmt.v7i4.1339
  • Scheidat, T., Heinze, J., Vielhauer, C., Dittmann, J., & Kraetzer, C. (2011). Comparative review of studies on aging effects in context of biometric authentication. In Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V (Vol. 7881, pp. 788110–788316). https://doi.org/10.1117/12.872417
  • Schmeck, A., Opfermann, M., van Gog, T., Paas, F., & Leutner, D. (2015). Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings. Instructional Science, 43(1), 93–114. https://doi.org/10.1007/s11251-014-9328-3
  • Second International Fingerprint Verification Competition (2002). http://bias.csr.unibo.it/fvc2002/databases.asp
  • Selva (2015). GUI environmental sound recognition. https://de.mathworks.com/matlabcentral/fileexchange/45054-gui-environmental-sound-recognition
  • Shien, L. K., & Singh, M. M. (2017). A secure mobile crowdsensing (MCS) location tracker for elderly in smart city. AIP Conference Proceedings, 1891(1), 020085. https://doi.org/10.1063/1.5005418
  • Shuwandy, M. L., Zaidan, B. B., Zaidan, A. A., Albahri, A. S., Alamoodi, A. H., Albahri, O. S., & Alazab, M. (2020). mHealth authentication approach based 3D touchscreen and microphone sensors for real-time remote healthcare monitoring system: Comprehensive review, open issues and methodological aspects. Computer Science Review, 38 (November), 100300. https://doi.org/10.1016/j.cosrev.2020.100300
  • Solé-Casals, J., Vancea, M., & Miquel March, J. (2015). A preliminary review of behavioural biometrics for health monitoring in the elderly. In Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies (Vol. 4, pp. 365–371). https://doi.org/10.5220/0005321603650371
  • Still, J. D., Cain, A., & Schuster, D. (2017). Human-centered authentication guidelines. Information & Computer Security, 25(4), 437–453. https://doi.org/10.1108/ICS-04-2016-0034
  • Stylios, I., Kokolakis, S., Thanou, O., & Chatzis, S. (2021). Behavioral biometrics & continuous user authentication on mobile devices: A survey. Information Fusion, 66 (February), 76–99. https://doi.org/10.1016/j.inffus.2020.08.021
  • Sun, F., Zang, W., Gravina, R., Fortino, G., & Li, Y. (2020). Gait-based identification for elderly users in wearable healthcare systems. Information Fusion, 53 (January), 134–144. https://doi.org/10.1016/j.inffus.2019.06.023
  • Sundararajan, K., & Woodard, D. L. (2019). Deep learning for biometrics. ACM Computing Surveys, 51(3), 1–34. https://doi.org/10.1145/3190618
  • Teh, P. S., Zhang, N., Teoh, A. B. J., & Chen, K. (2016). A survey on touch dynamics authentication in mobile devices. Computers & Security, 59 (June), 210–235. https://doi.org/10.1016/j.cose.2016.03.003
  • Vassli, L. T., & Farshchian, B. A. (2018). Acceptance of health-related ICT among elderly people living in the community: A systematic review of qualitative evidence. International Journal of Human–Computer Interaction, 34(2), 99–116. https://doi.org/10.1080/10447318.2017.1328024
  • Villegas, M. F., Torres Munoz, J. C., Alrashidi, A. M. R., & Dow, D. E. (2019). Tabletop human computer interface to assist elderly with tasks of daily living. In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 596–599). https://doi.org/10.1109/UEMCON47517.2019.8992972
  • Vuletic, T., Duffy, A., Hay, L., McTeague, C., Campbell, G., & Grealy, M. (2019). Systematic literature review of hand gestures used in human computer interaction interfaces. International Journal of Human–Computer Studies, 129 (September), 74–94. https://doi.org/10.1016/j.ijhcs.2019.03.011
  • Wang, C., Wang, Y., Chen, Y., Liu, H., & Liu, J. (2020). User authentication on mobile devices: Approaches, threats and trends. Computer Networks, 170 (April), 107118. https://doi.org/10.1016/j.comnet.2020.107118
  • Wang, F., Chen, L., Li, C., Huang, S., Chen, Y., Qian, C., & Loy, C. C. (2018). The Devil of Face Recognition is in the Noise (pp. 765–780). https://openaccess.thecvf.com/content_ECCV_2018/html/Liren_Chen_The_Devil_of_ECCV_2018_paper.html
  • Wang, K., Zhou, L., & Zhang, D. (2019). User preferences and situational needs of mobile user authentication methods. In 2019 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 18–23). https://doi.org/10.1109/ISI.2019.8823274
  • Wang, K., Zhou, L., Zhang, D., Liu, Z., & Lim, J. (2020). What is more important for touch dynamics based mobile user authentication? In PACIS 2020 Proceedings. https://aisel.aisnet.org/pacis2020/174
  • Wang, K., Zhu, Z., Wang, S., Sun, X., & Li, L. (2016). A database for emotional interactions of the elderly. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1–6). https://doi.org/10.1109/ICIS.2016.7550902
  • Wang, X., Yan, Z., Zhang, R., & Zhang, P. (2021). Attacks and defenses in user authentication systems: A survey. Journal of Network and Computer Applications, 188 (August), 103080. https://doi.org/10.1016/j.jnca.2021.103080
  • Wasnik, P., Ramachandra, R., Raja, K., Busch, C. (2018). An empirical evaluation of deep architectures on generalization of smartphone-based face image quality assessment. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) (pp. 1–7). https://doi.org/10.1109/BTAS.2018.8698593
  • Watzman, S. (2002). Visual design principles for usable interfaces. In The human–computer interaction handbook: Fundamentals, evolving technologies and emerging applications (pp. 263–285). L. Erlbaum Associates Inc.
  • White-Chu, E. F., & Reddy, M. (2011). Dry skin in the elderly: Complexities of a common problem. Clinics in Dermatology, 29(1), 37–42. https://doi.org/10.1016/j.clindermatol.2010.07.005
  • World Population Prospects—United Nations (2019). https://population.un.org/wpp/Download/Standard/Population/
  • Wu, S., & Wang, D. (2019). Effect of subject’s age and gender on face recognition results. Journal of Visual Communication and Image Representation, 60 (April), 116–122. https://doi.org/10.1016/j.jvcir.2019.01.013
  • Wulf, L., Garschall, M., Himmelsbach, J., & Tscheligi, M. (2014). Hands free-care free: Elderly people taking advantage of speech-only interaction. In Proceedings of the 8th Nordic Conference on Human–Computer Interaction: Fun, Fast, Foundational (pp. 203–206). https://doi.org/10.1145/2639189.2639251
  • Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M. (2019). Feature transfer learning for deep face recognition with under-represented data. ArXiv:1803.09014 [Cs]. http://arxiv.org/abs/1803.09014
  • Yu-Huei, C., Ja-Shen, C., & Ming-Chao, W. (2019). Why do older adults use wearable devices: A case study adopting the senior technology acceptance model (STAM). In 2019 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1–8). https://doi.org/10.23919/PICMET.2019.8893767
  • Zheng, G., Yang, W., Johnstone, M., Shankaran, R., & Valli, C. (2020). 3—Securing the elderly in cyberspace with fingerprints. In N. K. Suryadevara & S. C. Mukhopadhyay (Eds.), Assistive technology for the elderly (pp. 59–79). Academic Press. https://doi.org/10.1016/B978-0-12-818546-9.00003-8
  • Zhou, L., Kang, Y., Zhang, D., & Lai, J. (2016). Harmonized authentication based on ThumbStroke dynamics on touch screen mobile phones. Decision Support Systems, 92 (December), 14–24. https://doi.org/10.1016/j.dss.2016.09.007
  • Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020). Deep learning-based gait recognition using smartphones in the wild. IEEE Transactions on Information Forensics and Security, 15, 3197–3212. https://doi.org/10.1109/TIFS.2020.2985628

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