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Framework for A Personalized Intelligent Assistant to Elderly People for Activities of Daily Living
Nirmalya Thakur, Chia Y. Han
Pages - 1 - 22     |    Revised - 31-01-2019     |    Published - 28-02-2019
Volume - 9   Issue - 1    |    Publication Date - February 2019  Table of Contents
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KEYWORDS
Affect Aware Systems, Behavior Analysis, Smart and Assisted Living, Smart Home, User Experience, Affective States, Human Computer Interaction, Elderly People.
ABSTRACT
The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an "average user" and a "specific user" respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a "specific user", which is considerably higher than its performance while modelling an "average user", this upholds the relevance for development and implementation of this proposed framework.
1 Google Scholar 
2 BibSonomy 
3 ResearchGate 
4 Scribd 
5 SlideShare 
1 David E. Bloom and Dara Lee Luca, "The Global Demography of Aging: Facts, Explanations, Future" Working Paper Series, PROGRAM ON THE GLOBAL DEMOGRAPHY OF AGING AT HARVARD UNIVERSITY (PGDA), Paper No. 130, August 2016.
2 Tommy Bengtsson and Kirk Scott, "The Ageing Population", 2nd Chapter in Population Ageing - A Threat to the Welfare State?, ISBN 978-3-642-12611-6, eISBN 978-3-642-12612-3.
3 Tony Carter and Peter Beresford, "Age and change", Joseph Rowntree Foundation 2000, Published York Publishing Services, 2000, ISBN 1 902633 81 4.
4 Hajime Orimo, Hideki Ito, Takao Suzuki, Atsushi Araki, Takayuki Hosoi, Motoji Sawabe, "Reviewing the definition of elderly", International Journal of Geriatrics and Gerontology International 6(3), Feb 2006.
5 Kowal P, Dowd JE. Definition of an older person. Proposed working definition of an older person in Africa for the MDS Project. Geneva: World Health Organization; 2001.
6 Jong-bum Woo, Youn-kyung Lim, "User experience in do-it-yourself-style smart homes" Proceedings of UbiComp2015, Osaka, Japan, DOI: 10.1145/2750858.2806063.
7 Xuesheng Qian, Yifeng Xu, Jing Zhang, Zhengchuan Xu,"Happy Index: Analysis Based on Automatic Recognition of Emotion Flow" ICMSS '17: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences, January 2017.
8 Thorpe, L., Davidson, P., & Janicki, M.P. (2000). Healthy Ageing - Adults with Intellectual Disabilities: Biobehavioural Issues. Geneva, Switzerland: World Health Organization.
9 World Health Organization, "World Report on Ageing and Health" Technical Report 2015, ISBN 978 92 4 156504 2.
10 Seltzer, Judith A. and Jenjira J. Yahirun, "Diversity in Old Age: The Elderly in Changing Economic and Family Contexts", In Diversity and Disparities: America Enters a New Century, edited by John Logan. The Russel Sage Foundation, November 2014, ISBN: 978-1-61044-846-8.
11 Azkune, Gorka & Almeida, Aitor & López-de-Ipiña, Diego & Chen, Liming. (2015). Extending knowledge-driven activity models through data-driven learning techniques. Expert Systems with Applications. 42. 10.1016/j.eswa.2014.11.063.
12 Riboni, D., Bettini, C., 2009. Context-aware activity recognition through a combination of ontological and statistical reasoning. In: Proceedings of the international conference on Ubiquitous Intelligence and Computing. Springer Berlin/Heidelberg, pp. 39-53.
13 Nevatia, R., Hobbs, J., Bolles, B., 2004. An ontology for video event representation. In: CVPRW '04: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop Volume 7. IEEE Computer Society, Washington, DC, USA, p. 119.
14 van Kasteren, T., Noulas, A., Englebienne, G., Krose, B., Sep. 2008. Accurate activity recognition in a home setting. In: UbiComp '08: Proceedings of the 10th International Conference on Ubiquitous Computing. ACM, Seoul, Korea, pp. 1-9.
15 Zhongwei Cheng, Lei Qin, Qingming Huang, Shuqiang Jiang, Shuicheng Yan, Qi Tian, "Human Group Activity Analysis with Fusion of Motion and Appearance Information", Proceedings of the 19th ACM international conference on Multimedia, Pages 1401-1404, Scottsdale, Arizona, USA - November 28 - December 01, 2011.
16 Pavle Skocir, Petar Krivic, Matea Tomeljak, Mario Kusek, Gordan Jezic, "Activity detection in smart home environment", Proceedings of the 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems , Sept. 5-7, 2016.
17 Afsaneh Doryab and Jakob E. Bardram, "Designing Activity-aware Recommender Systems for Operating Rooms", Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, Feb 13, 2011.
18 Nirmalya Thakur and Chia Y. Han, "A Context Driven Complex Activity Framework for Smart Home", Proceedings of the 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2018, Vancouver, Canada, Nov. 1-3, 2018.
19 Katharina Rasch, "An unsupervised recommender system for smart homes" Journal of Ambient Intelligence and Smart Environments 6 (2014) 21-37 21 DOI 10.3233/AIS-130242.
20 Vavilov, D.; Melezhik, A.; Platonov, I. Healthcare Application of Smart Home User's Behavior Prediction. In Proceedings of the 2014 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 10-13 January 2014, pp. 323-326.
21 Claypool, M. Combining Content-Based and Collaborative Filters in an Online Newspaper. In Proceedings of Recommender Systems Workshop at ACM SIGIR, Berkeley, CA, USA, 19 August 1999.
22 Gong, J.; Gao, M.L.; Xu, B.; Wang, W. A hybrid recommendation algorithm based on social networks. In Proceedings of the International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Taiwan, 19-20 August 2015.
23 Lai, S.; Liu, Y.; Gu, H.; Xu, L.; Liu, K.; Xiang, S.; Zhao, J.; Diao, R.; Xiang, L.; Li, H.; Wang, D. Hybrid Recommendation Models for Binary User Preference Prediction Problem. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, San Diego, USA, 21-24 August 2011.
24 Saguna, Arkady Zaslavsky and Dipanjan Chakraborty, "Complex Activity Recognition Using Context-Driven Activity Theory and Activity Signatures", ACM Transactions on Computer-Human Interaction, Vol. 20, No.6, Article 32, Dec. 2013.
25 Nirmalya Thakur and Chia Y. Han, "A Complex Activity Based Emotion Recognition Algorithm For Affect Aware Systems", Proceedings of the IEEECCWC 2018 Conference,08-10 January, 2018, Las Vegas, USA.
26 Nirmalya Thakur and Chia Y. Han, "Methodology for Forecasting User Experience for Smart and Assisted Living in Affect Aware Systems" Proceedings of the 8th International Conference on the Internet of Things (IoT 2018), Santa Barbara, California, 15-18 October 2018.
27 K. Jack and K.William, ``The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes,' Sci.Data, vol. 2, p. 150007, Sep. 2.
28 O. Ritthoff, R. Klinkenberg, S. Fisher, I. Mierswa, S. Felske. YALE: Yet Another Learning Environment. LLWA'01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen Adaptivitat. University of Dortmund, Dortmund, Germany. Technical Report 763. 2001: 84-92.
29 Ordóñez, F.J.; de Toledo, P.; Sanchis, A. Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors. Sensors 2013, 13, 5460-5477.
30 Tibor Bosse, Edwin Zwanenburg "There's Always Hope: Enhancing Agent Believability through Expectation-Based Emotions" Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 10-12 Sept. 2009, Amsterdam, Netherlands.
Mr. Nirmalya Thakur
Department of Electrical Engineering and Computer Science University of Cincinnati - United States of America
thakurna@mail.uc.edu
Dr. Chia Y. Han
Department of Electrical Engineering and Computer Science University of Cincinnati - United States of America