Yıl 2019, Cilt 1 , Sayı 1, Sayfalar 14 - 24 2019-06-30

Assisted Aging: The Future Confluence of Gerontology, Internet of Things and Psychological AI
Yardımlı Yaşlanma: Yaşlılık Bilimi, Nesnelerin İnterneti ve Psikolojik Yapay Zekanın Buluşması

Ulaş Başar GEZGİN [1] , Alper YAMAN [2]


In this article, we propose a new term, ‘assisted aging’ which is an offshoot of the discussions on assisted living of aging populations. The article consists of 5 major sections: In the first section after introduction, we review mostly technical accounts of health applications of IoT. In the second section, we introduce mostly social science-oriented research on health applications of IoT. In the third section, we present and discuss gerontological research on IoT. In the fourth section, we focus on yet another relatively new term which is ‘psychological AI’. We show how this will be central to future IoT for gerontological purposes. Finally, we reflect on assisted living studies to develop our notion of assisted aging.
Bu makalede, yaşlanmakta olan nüfusların yardımlı yaşamaları konulu tartışmaların bir uzantısı olarak ‘yardımlı yaşlanma’ biçiminde yeni bir terim ileri sürüyoruz. Makale, 5 ana bölümden oluşuyor: Girişten sonraki ilk bölümde, nesnelerin internetinin sağlık uygulamalarının çoğunlukla teknik açıklamalarını gözden geçiriyoruz. İkinci bölümde, nesnelerin internetinin sağlık uygulamalarına ilişkin çoğunlukla toplum bilimleri yönelimli araştırmalara giriş yapıyoruz. Üçüncü bölümde, nesnelerin internetiyle ilgili yaşlılık bilimi araştırmalarını sunuyor ve tartışıyoruz. Dördüncü bölümde, bir diğer görece yeni terim olan ‘psikolojik Yapay Zeka’ terimine odaklanıyoruz. Bunun gelecekteki yaşlılık bilimi amaçlı nesnelerin interneti için ne kadar merkezi olacağını gösteriyoruz. Son olarak, yardımlı yaşlanma kavramsallaştırmamızı geliştirmek üzere yardımlı yaşama çalışmaları üstüne düşünüyoruz.
  • Anumala, H., & Busetty, S. M. (2015, December). Distributed device health platform using Internet of Things devices. In Data Science and Data Intensive Systems (DSDIS), 2015 IEEE International Conference on (pp. 525-531). IEEE. Accessed https://ieeexplore.ieee.org/abstract/document/7396553
  • Anzanpour, A., Rahmani, A. M., Liljeberg, P., & Tenhunen, H. (2015, December). Internet of things enabled in-home health monitoring system using early warning score. In Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare (pp. 174-177). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
  • Astington, J. W., & Jenkins, J. M. (1999). A longitudinal study of the relation between language and theory-of-mind development. Developmental Psychology, 35(5), 1311-1320.
  • Atee, M., Hoti, K., & Hughes, J. D. (2018). A technical note on the PainChek™ system: a web portal and mobile medical device for assessing pain in people with dementia. Frontiers in Aging Neuroscience, 10, 1-13.
  • Baldissera, T. A., & De Faveri, C. (2016). Emergent Technologies for Active Aging. Revista ComInG-Communications and Innovations Gazette, 1(1), 68-78.
  • Banerjee, T., Peterson, M., Oliver, Q., Froehle, A., & Lawhorne, L. (2018). Validating a commercial device for continuous activity measurement in the older adult population for dementia management. Smart Health, 5, 51-62.
  • Barrué, C., Cortés, A., Cortés, U., Tétard, F., & Gironès, X. (2017). CAREGIVERSPRO-MMD: community services, helping patients with dementia and caregivers connect with others for evaluation, support and to improve the care experience. Computación y Sistemas, 21(1), 23-33.
  • Baum, S. D. (2017). On the promotion of safe and socially beneficial artificial intelligence. AI & Society, 32(4), 543-551.
  • Baumgaertner, B., & Weiss, A. (2014). Do emotions matter in the ethics of human–robot interaction? Artificial empathy and companion robots. In International Symposium on New Frontiers in Human–Robot Interaction, London, UK. https://pdfs.semanticscholar.org/55e0/c6339f4b4541ea479160bcb7177cca93534c.pdf
  • Bengs, A., Hagglund, S., & Wiklund-Engblom, A. (2018). Applying experience design to facilitate wellbeing and social inclusion of older adults. Interaction Design & Architecture (s), 36, 11-30.
  • Boissy, P., Choquette, S., Hamel, M., & Noury, N. (2007). User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemedicine and e-Health, 13(6), 683-694.
  • Botelho, L. M., & Coelho, H. (1998, May). Artificial Autonomous Agents with Artificial Emotions. In Agents (pp. 449-450). Accessed https://www.researchgate.net/profile/Helder_Coelho/
  • Brabazon, T. (2015). Digital fitness: Self-monitored fitness and the commodification of movement. Communication, Politics & Culture, 48(2), 1-23.
  • Bretherton, I., & Beeghly, M. (1982). Talking about internal states: The acquisition of an explicit theory of mind. Developmental Psychology, 18(6), 906.
  • Cahill, J., McLoughlin, S., O’Connor, M., Stolberg, M., & Wetherall, S. (2017, July). Addressing issues of need, adaptability, user acceptability and ethics in the participatory design of new technology enabling wellness, independence and dignity for seniors living in residential homes. In International Conference on Human Aspects of IT for the Aged Population (pp. 90-109). Springer, Cham. Accessed https://pdfs.semanticscholar.org/db27/1bba909107336af43180e8cab0cb1cdd7a07.pdf
  • Chi, N. C., & Demiris, G. (2017). The Roles of Telehealth Tools in Supporting Family Caregivers: Current Evidence, Opportunities, and Limitations. Journal of Gerontological Nursing, 43(2), 3-5.
  • Choi, S., & Youm, S. (2017). A study on a fall detection monitoring system for falling elderly using open source hardware. Multimedia Tools and Applications, 1-12.doi: 10.1007%2Fs11042-017-5452-9
  • Colby, K. M. (1986). Ethics of computer-assisted psychotherapy. Psychiatric Annals, 16(7), 414-415.
  • Crandall, A. S., Cook, D. J., & Schmitter-Edgecombe, M. (2016, January). Introduction to the Technologies for Healthy Aging Minitrack. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3437-3437). IEEE. Accessed https://ieeexplore.ieee.org/abstract/document/7427612
  • da Cunha, L. F. C., Baixinho, C. L., & Henriques, M. A. (2018, October). Preventing falls in hospitalized elderly: design and validation of an intervention for the team. In 3rd World Conference on Qualitative Research (Vol. 2). Accessed https://proceedings.wcqr.info/index.php/wcqr2018/article/download/60/59
  • Damodaran, L., & Olphert, W. (2015). How are attitudes and behaviours to the ageing process changing in light of new media and new technology? How might these continue to evolve by 2025 and 2040?. The Government Office for Science–Foresight, Future of an ageing population: evidence review. Accessed https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/455176/gs-15-17-future-ageing-attitudes-new-technology-er08.pdf
  • Del Din, S., Godfrey, A., Galna, B., Lord, S., & Rochester, L. (2016). Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length. Journal of Neuroengineering and Rehabilitation, 13(1), 1-12.
  • Del Din, S., Godfrey, A., Mazzà, C., Lord, S., & Rochester, L. (2016). Free‐living monitoring of Parkinson's disease: Lessons from the field. Movement Disorders, 31(9), 1293-1313.
  • Dumit, E. M., Novillo-Ortiz, D., Contreras, M., Velandia, M., & Danovaro-Holliday, M. C. (2018). The use of eHealth with immunizations: An overview of systematic reviews. Vaccine. doi: 10.1016/j.vaccine.2018.06.076
  • Enshaeifar, S., Zoha, A., Markides, A., Skillman, S., Acton, S. T., Elsaleh, T., Hassanpour, M., Ahrabian, A., Kenny, M., Klein, S. & Rostill, H. (2018). Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques. PloS one, 13(5), e0195605.
  • Eskofier, B., Lee, S., Baron, M., Simon, A., Martindale, C., Gaßner, H., & Klucken, J. (2017). An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring. Applied Sciences, 7(10), 986-1002.
  • Fox, N. J. (2017). Personal health technologies, micropolitics and resistance: a new materialist analysis. Health: An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine, 21(2), 136-153.
  • Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Mental Health, 5(4), e64.
  • Gaggioli, A. (2017). Artificial Intelligence: The Future of Cybertherapy?. Cyberpsychology, Behavior, and Social Networking, 20(6), 402-403.
  • Gezgin, U.B. (2018). An invitation to critical social science of big data: from critical theory and critical research to omniresistance. AI & Society. Doi:https://link.springer.com/article/10.1007%2Fs00146-018-0868-y
  • Gibson, G., Dickinson, C., Brittain, K., & Robinson, L. A. (2018). Personalisation, customisation and bricolage: How people with dementia and their families make assistive technology work for them. Ageing & Society. Accessed http://nrl.northumbria.ac.uk/34241/1/bricolage%20paper%20final%20version.pdf
  • Gigras, Y., & Gupta, K. (2011). Ambient intelligence in ubiquitous robotics. International Journal of Computer Science and Information Technologies (IJCSIT), 2(4), 1438-1440.
  • Gill, S., Hearn, J., Powell, G., & Scheme, E. (2017, November). Design of a multi-sensor IoT-enabled assistive device for discrete and deployable gait monitoring. In Healthcare Innovations and Point of Care Technologies (HI-POCT), 2017 IEEE (pp. 216-220). IEEE. https://ieeexplore.ieee.org/abstract/document/8227623
  • Godfrey, A. (2017). Wearables for independent living in older adults: Gait and falls. Maturitas, 100, 16-26.
  • Gomez, D., Saavedra-Martinez, G., Villarreal, L., Santos-Moreno, P., Bello-Gualtero, J., Giraldo, V., ... & Boon, M. (2015). SAT0108 Misdiagnosis of Rheumatoid Arthritis-The Photography. Annals of the Rheumatic Diseases, 74, 689.
  • Gomes, Y. F., Santos, D. F., Almeida, H. O., & Perkusich, A. (2015, January). Integrating MQTT and ISO/IEEE 11073 for health information sharing in the Internet of Things. In Consumer Electronics (ICCE), 2015 IEEE International Conference on (pp. 200-201). IEEE.
  • Goonawardene, N., Leong, C., & Tan, H. P. (2018). An Action Design Research of a Sensor-Based Elderly Monitoring System for Aging-in-Place. Thirty Ninth International Conference on Information Systems, San Francisco 2018. Accessed https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1038&context=icis2018
  • Ha, D. C. (2016). Scripts and Re-scriptings of Self-Tracking Technologies: Health and Labor in an Age of Hyper-Connectivity. Asia Pacific Journal of Health Law & Ethics, 10(3), 67-86.
  • Hagen, N., & Kasperowski, D. (2017). Self-Quantification of Body and Health-An Explorative Study of Epistemological Relations to Data Among Self-Tracking Individuals. Accessed https://osf.io/br3en/download?format=pdf
  • Harford, T. (2014). Big data: A big mistake?. Significance, 11(5), 14-19.
  • Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., ... & Andreescu, S. (2015, June). Health monitoring and management using Internet-of-Things (IoT) sensing with cloud-based processing: Opportunities and challenges. In 2015 IEEE International Conference on Services Computing (SCC) (pp. 285-292). IEEE.
  • Hong, Y. S. (2018). Smart Care Beds for Elderly Patients with Impaired Mobility. Wireless Communications and Mobile Computing. 1780904
  • Hong, Y. A., Zhou, Z., Fang, Y., & Shi, L. (2017). The digital divide and health disparities in China: Evidence from a national survey and policy implications. Journal of Medical Internet Research, 19(9), 1-18.
  • Jenkins, N. (2017). No substitute for human touch? Towards a critically posthumanist approach to dementia care. Ageing & Society, 37(7), 1484-1498.
  • Kan, C., Chen, Y., Leonelli, F., & Yang, H. (2015, August). Mobile sensing and network analytics for realizing smart automated systems towards health internet of things. In Automation Science and Engineering (CASE), 2015 IEEE International Conference on (pp. 1072-1077). IEEE.
  • Kanemura, A., Morales, Y., Kawanabe, M., Morioka, H., Kallakuri, N., Ikeda, T., Miyashita, T., Hagita, N. & Ishii, S. (2013, November). A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration. In Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on (pp. 865-870). IEEE.
  • Kim, J. H., Jeong, I. B., Park, I. W., & Lee, K. H. (2009). Multi-layer architecture of ubiquitous robot system for integrated services. International Journal of Social Robotics, 1(1), 19-28.
  • Kim, J. Y., Liu, N., Tan, H. X., & Chu, C. H. (2017). Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sensors Journal, 17(17), 5694-5704.
  • Kon, B., Lam, A., & Chan, J. (2017, April). Evolution of Smart Homes for the Elderly. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1095-1101). International World Wide Web Conferences Steering Committee. Accessed https://pdfs.semanticscholar.org/b6be/3aa8810f6fd924b9e6bb4980f2626e285e97.pdf
  • Kopeć, W., Nielek, R., & Wierzbicki, A. (2018). Guidelines Towards Better Participation of Older Adults in Software Development Processes using a new SPIRAL Method and Participatory Approach. Accessed https://arxiv.org/pdf/1803.10177
  • Kwan, R. Y. C., Cheung, D. S. K., & Kor, P. P. K. (2018). The use of smartphones for wayfinding by people with mild dementia. Dementia, 1471301218785461. Accessed https://journals.sagepub.com/doi/abs/10.1177/1471301218785461
  • Leong, T. W., & Johnston, B. (2016, November). Co-design and robots: a case study of a robot dog for aging people. In International Conference on Social Robotics (pp. 702-711). Springer, Cham. Accessed https://opus.lib.uts.edu.au/bitstream/10453/63000/1/Co-Design%20and%20robots.pdf
  • Lewy, H. (2015, August). Wearable devices-from healthy lifestyle to active ageing. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 7748-7751). IEEE. Accessed https://ieeexplore.ieee.org/abstract/document/7320188
  • Lupton, D. (2016). The diverse domains of quantified selves: self-tracking modes and dataveillance. Economy and Society, 45(1), 101-122.
  • Lupton, D. (2015). Lively data, social fitness and biovalue: The intersections of health self-tracking and social media. Accessed https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2666324
  • Lupton, D. (2014). Self-tracking modes: Reflexive self-monitoring and data practices. Accessed https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2483549
  • Lupton, D. (2013). Digitized Health Promotion: Personal Responsibility for Health in the Web 2.0 Era. Sydney Health & Society Group Working Paper No. 5. Sydney: Sydney Health & Society Group. Accessed http://ses.library.usyd.edu.au/bitstream/2123/9190/1/Working%20paper%20No.%205%20-%20Digitized%20health%20promotion.pdf
  • Luxton, D. D. (2014). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 45(5), 332-339.
  • Lyall, B., & Robards, B. (2018). Tool, toy and tutor: Subjective experiences of digital self-tracking. Journal of Sociology, 54(1), 108-124.
  • Lyon, D. (2014). Surveillance, Snowden, and big data: Capacities, consequences, critique. Big Data & Society, 1(2), 2053951714541861.
  • Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., & Deen, M. (2017). Smart homes for elderly healthcare—Recent advances and research challenges. Sensors, 17(11), 2496.
  • Maksimović, M., Vujović, V., & Perišić, B. (2016). Do It Yourself solution of Internet of Things Healthcare System: Measuring body parameters and environmental parameters affecting health. Journal of Information Systems Engineering & Management, 1(1), 25-39.
  • Mallat, H. K., Yared, R., & Abdulrazak, B. (2015, May). Assistive Technology for Risks Affecting Elderly People in Outdoor Environment. In ICT4AgeingWell (pp. 5-16). Accessed https://www.researchgate.net/profile/Rami_Yared/
  • Maresova, P., Tomsone, S., Lameski, P., Madureira, J., Mendes, A., Zdravevski, E., Chorbev, I., Trajkovik, V., Ellen, M. & Rodile, K. (2018). Technological Solutions for Older People with Alzheimer's Disease. Current Alzheimer Research, 15(10), 975-983.
  • Meacham, S., & Phalp, K. (2016). Requirements engineering methods for an Internet of Things application: fall-detection for ambient assisted living. BCS SQM/Inspire Conference. Accessed https://www. researchgate. net/publication/30938535
  • Milovich, M., & Burleson, D. (2018). Social Media and Older Adults: Understanding Cognitive Training and Social Network. Proceedings of the 51st Hawaii International Conference on System Sciences 2018. Accessed https://scholarspace.manoa.hawaii.edu/bitstream/10125/50304/1/paper0417.pdf
  • Minocha, S., McNulty, C., & Evans, S. (2015). Imparting digital skills to people aged 55 years and over in the UK. Accessed http://oro.open.ac.uk/44009/7/DigitalSkills-People-over-55-11Aug2015-FINAL-SUBMITTED-ORO-2Sept2015.pdf
  • Moore, P., Thomas, A., Tadros, G., Xhafa, F., & Barolli, L. (2013). Detection of the onset of agitation in patients with dementia: real-time monitoring and the application of big-data solutions. International Journal of Space-Based and Situated Computing, 3(3), 136-154.
  • Moosavi, S. R., Rahmani, A. M., Westerlund, T., Yang, G., Liljeberg, P., & Tenhunen, H. (2014, November). Pervasive health monitoring based on internet of things: Two case studies. In Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on (pp. 275-278). IEEE.
  • Nguyen, H., Mirza, F., Naeem, M. A., & Baig, M. M. (2018). Falls management framework for supporting an independent lifestyle for older adults: a systematic review. Aging Clinical and Experimental Research, 1-12. doi: 10.1007/s40520-018-1026-6
  • Nieto-Riveiro, L., Groba, B., Miranda, M. C., Concheiro, P., Pazos, A., Pousada, T., & Pereira, J. (2018). Technologies for participatory medicine and health promotion in the elderly population. Medicine, 97(20), 1-7.
  • Panicker, N. V., & Kumar, S. (2015, October). Design of a telemonitoring system for detecting falls of the elderly. In Green Computing and Internet of Things (ICGCIoT), 2015 International Conference on (pp. 800-803). IEEE. Accessed https://ieeexplore.ieee.org/abstract/document/7380572
  • Pantzar, M., & Ruckenstein, M. (2015). The heart of everyday analytics: emotional, material and practical extensions in self-tracking market. Consumption Markets & Culture, 18(1), 92-109.
  • Paré, G., Leaver, C., & Bourget, C. (2018). Diffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey. Journal of Medical Internet Research, 20(5), e177.
  • Ray, P. P. (2014, November). Home Health Hub Internet of Things (H 3 IoT): An architectural framework for monitoring health of elderly people. In Science Engineering and Management Research (ICSEMR), 2014 International Conference on (pp. 1-3). IEEE.
  • Rodrigues, N., & Pereira, A. (2018). A User-Centred Well-Being Home for the Elderly. Applied Sciences, 8(6), 850-865.
  • Rubio Pastor, M. Á., Plaza García, I., & Orive Serrano, V. (2017). Soluciones TIC para Personas
  • Mayores: Preferencias Manifestadas en el Medio Rural Español (ICT Solutions for Elder People: Manifested Preferences in Spanish Rural Areas). International and Multidisciplinary Journal of Social Sciences, 6(2), 137-177.
  • Russo, D. (2018). Brief introduction to Domotics, Robotics, and to a their possible evolution. Accessed http://www.dariorusso.org/blog/sites/default/files/field/pdf/Domotics_Robotics_0.pdf
  • Schüll, N. D. (2016). Data for life: Wearable technology and the design of self-care. BioSocieties, 11(3), 317-333.
  • Schulz, R., Wahl, H. W., Matthews, J. T., De Vito Dabbs, A., Beach, S. R., & Czaja, S. J. (2014). Advancing the aging and technology agenda in gerontology. The Gerontologist, 55(5), 724-734.
  • Selcuk, B., Brink, K. A., Ekerim, M., & Wellman, H. M. (2018). Sequence of theory‐of‐mind acquisition in Turkish children from diverse social backgrounds. Infant and Child Development, e2098.
  • Sharma, J., & Kaur, S. (2017, August). Gerontechnology—The study of Alzheimer disease using cloud computing. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3726-3733). IEEE. Accessed https://ieeexplore.ieee.org/abstract/document/8390159
  • Singhal, P., Mandhanya, R., & Verma, S. (2016). Emotional manipulation with the help of emotional recognition - A survey. International Journal of Engineering Sciences & Research Technology, 5(10), 160-164.
  • Stragier, J., Abeele, M. V., Mechant, P., & De Marez, L. (2016). Understanding persistence in the use of online fitness communities: comparing novice and experienced users. Computers in Human Behavior, 64, 34-42.
  • Stranks, A. (2017). Ambient Assistive Living (AAL) Technology for Dementia and Aging in Place: An inclusive approach to knowledge acquisition for the design community. Accessed http://openresearch.ocadu.ca/id/eprint/1794/7/Stranks_Anna_2017_MDES_INCD_MRP.pdf
  • Talanov, M., & Toschev, A. (2018). Artificial Emotions via Virtual Neuromodulators. Accessed https://www.researchgate.net/profile/Max_Talanov
  • Tang, A. Y. C., Azman, F., Ahmad, A., Ismail, S., & Mustapha, A. (2018). A multi-agent precaution-detection-action (PDA) framework for fall detection at geriatric centers. Journal of Fundamental and Applied Sciences, 10(6S), 2727-2740.
  • Thapliyal, H., Nath, R. K., & Mohanty, S. P. (2018). Smart Home Environment for Mild Cognitive Impairment Population: Solutions to Improve Care and Quality of Life. IEEE Consumer Electronics Magazine, 7(1), 68-76.https://ieeexplore.ieee.org/abstract/document/8197479
  • Tiberghien, T., Mokhtari, M., Aloulou, H., & Biswas, J. (2012, November). Semantic reasoning in context-aware assistive environments to support ageing with dementia. In International Semantic Web Conference (pp. 212-227). Springer, Berlin, Heidelberg.https://link.springer.com/content/pdf/10.1007/978-3-642-35173-0_14.pdf
  • Till, C. (2014). Exercise as labour: Quantified self and the transformation of exercise into labour. Societies, 4(3), 446-462.
  • van Deursen, A. J., & Mossberger, K. (2018). Any Thing for Anyone? A New Digital Divide in Internet‐of‐Things Skills. Policy & Internet, 10(2), 122-140.
  • Yu, J., Wang, Z., Xie, L., Xia, Y., & Qiao, X. (2008). Research on Artificial Psychology Based on Multimodal Interactive Service Robot. The International Journal of Virtual Reality, 7(2), 33-40.
  • Zanwar, P., Heyn, P. C., McGrew, G., & Raji, M. (2018, October). Assistive technology megatrends to support persons with Alzheimer's disease and related dementias age in habitat: challenges for usability, engineering and public policy. In Proceedings of the Workshop on Human-Habitat for Health (H3): Human-Habitat Multimodal Interaction for Promoting Health and Well-Being in the Internet of Things Era (p. 1). ACM. Accessed https://dl.acm.org/citation.cfm?id=3279971
  • Zhang, C., Lai, C. F., Lai, Y. H., Wu, Z. W., & Chao, H. C. (2017). An inferential real-time falling posture reconstruction for Internet of healthcare things. Journal of Network and Computer Applications, 89, 86-95.
Birincil Dil en
Konular Sosyal Bilimler, Disiplinler Arası
Bölüm Derleme Makaleler
Yazarlar

Orcid: 0000-0002-6075-3501
Yazar: Ulaş Başar GEZGİN (Sorumlu Yazar)
Ülke: Vietnam


Orcid: 0000-0003-1767-4020
Yazar: Alper YAMAN
Ülke: Germany


Tarihler

Yayımlanma Tarihi : 30 Haziran 2019

APA GEZGİN, U , YAMAN, A . (2019). Assisted Aging: The Future Confluence of Gerontology, Internet of Things and Psychological AI. İzmir Sosyal Bilimler Dergisi , 1 (1) , 14-24 . Retrieved from https://dergipark.org.tr/tr/pub/izsbd/issue/49441/624669