An Approach to Organizing an Assistive Living Environment Using Artificial Intelligence
- 作者: Zayats V.V.1, Orlov S.B.1, Chechnev S.V.1
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隶属关系:
- Resource Center for Universal Design and Rehabilitation Technologies
- 期: 编号 1 (2023)
- 页面: 12-18
- 栏目: AI-enabled Systems
- URL: https://journals.rcsi.science/2071-8594/article/view/269750
- DOI: https://doi.org/10.14357/20718594230102
- ID: 269750
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The paper discusses the general theoretical foundations for the creation and functioning of ambient assisted living environment systems based on artificial intelligence. An ambient assisted living environment is a living space that includes equipment and technologies that provide support for impaired functions in people with disabilities. The creation of an ambient assisted living environment for persons with disabilities using artificial intelligence technologies makes it possible to provide ef- fective care for persons of this category by ensuring the autonomy of their life activity and personifying ongoing assistive and rehabilitation measures. Artificial intelligence systems collect and process data from sensors and transducers installed both on technical equipment (wheelchairs and other rehabilitation equipment) and on the patient's body. While processing these data taking into account the peculiar features of the patient, artificial intelligence systems form a program for creating an ambient assisted living environment for a particular person.
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作者简介
Vitaliy Zayats
Resource Center for Universal Design and Rehabilitation Technologies
Email: vvzayats@rcud-rt.ru
Candidate of medical sciences, docent. Director
俄罗斯联邦, MoscowSergey Orlov
Resource Center for Universal Design and Rehabilitation Technologies
Email: SBOrlov@rcud-rt.ru
Head of Design and Methodological Department
俄罗斯联邦, MoscowSergey Chechnev
Resource Center for Universal Design and Rehabilitation Technologies
编辑信件的主要联系方式.
Email: SVChechnev@rcud-rt.ru
Chief Specialist
俄罗斯联邦, Moscow参考
- Gavrilov A.V. Iskusstvennyi Domovoy [Artificial House-keeper]. Iskustvennyi intellect i prinyatie resheniy [Artificial Intelligence and Decision Taking]. 2012. No.2. P.77-89.
- Riva G. Ambient intelligence in health care. Cyberpsychol Behav. 2003 Jun;6(3). P.295-300.
- Pollack M. E. (2005). Intelligent Technology for an Aging Population: The Use of AI to Assist Elders with Cognitive Impairment. AI Magazine, 26(2), 9.
- Büscher A., Rumm P. Ambient Assisted Living und Pflegebedürftigkeit: Wie finden Anspruch und Realität zueinander? [Ambient assisted living and disability: how can claims and reality be reconciled?]. Pflege Z. 2010 May;63(5):272-5. German.
- Frisse M.E. Ubiquitous computing. Acad Med. 1992 Oct;67(10):642-4.
- Vimarlund V., Borycki E. M., Kushniruk A.W., Avenberg K. Ambient Assisted Living: Identifying New Challenges and Needs for Digital Technologies and Service Innovation. Yearb Med Inform. 2021 Apr 21.
- Alan Turing Institute.: AI and Inclusion (2019). Available at: https://www.turing.ac.uk/research/research-projects/ai-and-inclusion (accessed September 1, 2021).
- Lussier M., Aboujaoudé A., Couture M., Moreau M., Laliberté C., Giroux S., Pigot H., Gaboury S., Bouchard K., Belchior P., Bottari C., Paré G., Consel C., Bier N. Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report. JMIR Med Inform. 2020 Nov 13;8(11):e20215.
- Bublitz F., Sahota N.K., Oetomo A., Fadrique L., Morita P.P. Reference architectures for ambient assisted living: a scoping review protocol. BMJ Open. 2020 Oct 1;10(10):e033758.
- ElHady N.E., Jonas S., Provost J., Senner V. Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining. Sensors (Basel). 2020 Nov 26;20(23):6760.
- Thapa K., Abdullah Al ZM, Lamichhane B., Yang S.H. A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition. Sensors (Basel). 2020 Oct 12;20(20):5770.
- Meyer S., Fricke C. Autonome Assistenzroboter für ältere Menschen zu Hause: Eine Erkundungsstudie : „Er ist immer für mich da – und ich auch für ihn“ [Autonomous assitive robots for older people at home: An exploratory study : "He is always there for me-and I for him too"]. Z Gerontol Geriatr. 2020 Nov;53(7). Р.620-629.
- Cicirelli G., Marani R., Petitti A., Milella A., D'Orazio T. Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population. Sensors (Basel). 2021 May 19;21(10):3549.
- Artificial Intelligence and Accessibility: Examples of a Technology that Serves People with Disabilities. Available at: https://www.inclusivecitymaker.com/artificial-intelligence-accessibility-examples-technology-serves-people-disabilities/ (accessed September 1, 2021).
- Sahlab N., Jazdi N. AI-Based Elderly Assistance Systems. Stud Health Technol Inform. 2020 Sep 4;273. Р.163-169.
- Wolf P., Schmidt A., Otte J.P., et al. ОpenAAL the open source middleware for ambient-assisted living (AAL). In: AALIANCE conference, Malaga, Spain (2010).
- Michael Klein, Birgitta König-Ries, Michael Müssig, What is needed for Semantic Service Descriptions - A Proposal for Suitable Language Constructs; International Journal of Web and Grid Services 2005 - Vol. 1, No. ¾.
- Dimitrievski A., Zdravevski E., Lameski P., Trajkovik V. A survey of Ambient Assisted Living systems: Challenges and opportunities. In: Proceedings, IEEE 12th Int. Conference on Intelligent Computer Communication and Processing (ICCP); Cluj-Napoca, Romania, September 8-10, 2016. Р. 49–53.
- El Murabet A., Anouar A., Touhafi A., Tahiri A. Towards an SOA Architectural Model for AAL-Paas Design and Implementation Challenges. IACSA 2017;8,7. Р.52-56.
- Dey S., Jaiswal D., Dasgupta R., Mukherjee A. Organization and management of Semantic Sensor information using SSN ontology: An energy meter use case. In: 9th International Conference on Sensing Technology (ICST); Auckland, New Zealand, 2015. Р. 468–473.
- Knappmeyer M., Kiani S.L., Reetz S., Baker N., Tonjes R. Survey of Context Provisioning Middleware. IEEE Commun. Surv. Tutorials, 2013; 15(3): Р.1492–1519.
- Ari Keränen, Cullen Jennings, Sen ML: simple building block for IoT semantic interoperability. Available at: https://www.iab.org/wp-content/IAB-uploads/2016/03/ IAB_IOTSI_Keranen_Jennings_SenML.pdf (accessed August 9, 2021).
- Amja A.M., Obaid A., Valtchev P. Modeling and reasoning in context-aware systems based on relational concept analysis and description logic. In: 2014 IEEE Symposium on Computational Intelligence for Communication Systems and Networks (CIComms): [part of] IEEE SSCI 2014, 2014 IEEE Symposium Series on Computational Intelligence, Orlando, FL, USA, 2014, Р. 1–8.
- Yuan B., Herbert J. Context-aware hybrid reasoning framework for pervasive healthcare. Pers Ubiquit Comput, 2014;18,4. Р.865–881.
- Haque S.A., Aziz S.M., Rahman M. Review of Cyber-Physical System in Healthcare. International Journal of Distributed Sensor Networks 2014;10,4: 217415.
- Jazdi N. Cyber physical systems in the context of Industry 4.0. In: 2014 IEEE International Conference on Automation, Quality and Testing, Robotics: 22 - 24 May 2014, Cluj-Napoca, Romania.
- Ben Hmida H., Braun A. Enabling an Internet of Things Framework for Ambient Assisted Living. In: Wichert R, Mand B (Edrs) Advanced Technologies and Societal Change, Ambient Assisted Living, Р. 181–196. Cham: Springer International Publishing; 2017.
- Wan J., Gu X., Chen L., Wang J. Internet of Things for Ambient Assisted Living: Challenges and Future Opportunities. In: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery; Nanjing, China, 12-14 October 2017. Р. 354–357.
- El Murabet A., Abtoy A., Touhafi A., Tahiri A. Ambient Assisted living system’s models and architectures: A survey of the state of the art. J of King Saud University - Computer and Information Sciences, 2018.
- Li X., Eckert M., Martinez J-F, Rubio G. Context Aware Middleware Architectures: Survey and Challenges. Sensors (Basel). 2015 Aug; 15,8. Р. 20570–20607.
- Forkan A., Khalil I., Tari Z. CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Generation Computer Systems, 2014; 35. Р.114–127.
- Sahlab N., Jazdi N., Weyrich M. An intelligent medication assistance system. In: AUTOMED'20-14th Symposium on Automation in Medical Engineering, Lübeck, Germany, 02.03.-03.03.2020.
- Kim K., Kim B., Chung A.J., et al. Algorithm and System for improving the medication adherence of tuberculosis patients. In: The 9th International Conference on ICT Convergence; Jeju Island, Korea, Jeju, 2018. Р. 914–916.
- Diemert S., Weber J., Price M. Computable Adherence. In: Proc. 2017 IEEE International Conference on Healthcare Informatics, Park City, UT, USA, 2017. Р. 351–356.
