Nirmalya Thakur
Chia Y. Han
ABSTRACT
The application of affect aware systems in a smart home environment has the potential to address challenges in creation of a smart, adaptive, context-aware and assistive living space for elderly people. Affect aware systems that can predict the user experience even before an activity is performed, would provide a scope for effective communications and interactions between users and systems to ensure an adaptive and ambient living environment thereby ensuring more trust on intelligent systems. This paper therefore proposes an approach for forecasting user experiences in the context of affect aware systems to assist elderly people in smart homes. The preliminary result of the proposed analytical approach is supported by a random-forest based predictive model that achieves an overall prediction accuracy of 67% when evaluated on a subset of the UK DALE Dataset. [1]
- 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. 2015.Google Scholar
Cross Ref
- J T Arokiasamy, SM, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur "Social Problems and Care of the Elderly" Med J Malaysia Vol 52 No 3 September 1997.Google Scholar
- Thato E. Foko, Nomusa Dlodlo, Litsietsi Montsi "An Integrated Smart System for Ambient-Assisted Living" S. Balandin et al. (Eds.): NEW2AN/ruSMART 2013, LNCS 8121, pp. 128--138, 2013. © Springer-Verlag Berlin Heidelberg 2013.Google Scholar
- B. Tang, Z. Chen, G. Hefferman, T. Wei, H. He, Q. Yang, A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities, ASE Big Data & Social Informatics, Kaohsiung, Taiwan, October 2015. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- S. Planalp, Communicating emotion: Social, moral, and cultural processes. Cambridge University Press, 1999.Google ScholarCross Ref
- H. Wallbott, "Bodily expressions of emotion," EUR J SOC PSYCHOL, vol. 28, pp. 879--896, 1998.Google ScholarCross Ref
- P. Ekman and W. Friesen, "The repertoire of nonverbal behavior: Categories, origins, usage, and coding," SEMIOTICA, vol. 1, no. 1, pp. 49--98, 1969.Google ScholarCross Ref
- Ekman, P. and Friesen, W.V. and Hager, J. 1978. Facial Action Coding System - The Manual. Research Nexus Division of Network Information Research Corporation.Google Scholar
- Martin Wollmer, Florian Eyben, Stephan Reiter, Bjorn Schuller, Cate Cox, Ellen Douglas-Cowie, and Roddy Cowie. Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies. In 9th Annual Conference of the International Speech Communication Association (Interspeech), pages 597--600, ISCA, Australia, September 2008.Google ScholarCross Ref
- L C De Silva, T Miyasato, and R Nakatsu. Facial emotion recognition using multi-modal information.1:397.401, September 1997.Google Scholar
- Gonzalo Ferrer and Alberto Sanfeliu "Comparative Analysis of Human Motion Trajectory Prediction using Minimum Variance Curvature" HRI'11, March 6--9, 2011, Lausanne, Switzerland.Google Scholar
- Tao Ma, "A Framework for Modeling and Capturing Social Interactions", PhD. Dissertation, University of Cincinnati, December 2014.Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Nirmalya Thakur and Chia Y. Han, "A Complex Activity Based Emotion Recognition Algorithm For Affect Aware Systems", Proceedings of the IEEE-CCWC 2018 Conference, 08--10 January, 2018, Las Vegas, USA.Google Scholar
- 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.Google Scholar
- Abdulsalam Yassine, Shailendra Singh, And Atif Alamri "Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications" IEEE Special Section On Advances Of Multisensory Services And Technologies For Healthcare In Smart Cities, June 2017.Google Scholar
Recommendations
End User Context Modeling in Ambient Assisted Living
Ambient Assisted Living (AAL) services provide intelligent and context aware assistance for elderly people in their home environment. Following the vision of an open AAL service marketplace, such an approach has to support all lifecycle phases of an AAL ...
Service Platform for Continuous Delivery of Assisted Living Systems
Middleware Doctoral Symposium'16: Proceedings of the Doctoral Symposium of the 17th International Middleware ConferenceA smart home has potential to support independent living of elderly people in their preferred living environments. However, smart home systems do not fully address the aims of Ambient assisted living (AAL), mainly due to limited support outside the ...
An ambient assisted living nighttime wandering system for elderly
iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and ServicesThe Assistive living technologies provide good results for the support of specific activities transforming a home into a smart home. In this paper, we present a personalized ambient support system for elderly suffering from Alzheimer's dementia and ...
Comments