research-article

A user-guided personalization methodology to facilitate new smart home occupancy

Authors:

S. M. Murad Ali,

Juan Carlos Augusto,

David Windridge,

Emma WardAuthors Info & Claims

Universal Access in the Information Society, Volume 22, Issue 3

Pages 869 - 891

https://doi.org/10.1007/s10209-022-00883-x

Published: 15 June 2022 Publication History

Abstract

Smart homes are becoming increasingly popular in providing people with the services they desire. Activity recognition is a fundamental task to provide personalised home facilities. Many promising approaches are being used for activity recognition; one of them is data-driven. It has some fascinating features and advantages. However, there are drawbacks such as the lack of ability to providing home automation from the day one due to the limited data available. In this paper, we propose an approach, called READY (useR-guided nEw smart home ADaptation sYstem) for developing a personalised automation system that provides the user with smart home services the moment they move into their new house. The system development process was strongly user-centred, involving users in every step of the system’s design. Later, the user-guided transfer learning approach was introduced that uses an old smart home data set to enhance the existing smart home service with user contributions. Finally, the proposed approach and designed system were tested and validated in the smart lab that showed promising results.

References

[1]

(2015) Sweet home 3d. http://www.sweethome3d.com/it/

[2]

Ali, S.M.M.: A new personalized smart home validation questionnaire. https://figshare.com/s/a3e2e89ac64b0ef97a50 (2021)

[3]

Ali, S.M.M.: Questionnaire for smart home user. https://figshare.com/s/7ab909838bf10a580fca (2021)

[4]

Ali, S.M.M.: Validation 1: solving the cold start problem using the ready approach. https://figshare.com/s/bbbd061ae81f1c830b34 (2021)

[5]

Ali, S.M.M.: Validation 2: Enhance the understanding of the home using the UTL approach. https://figshare.com/s/248d73602cee8e6ed9af (2021)

[6]

Ali SMM, Augusto JC, and Windridge D Bramer M and Petridis M Improving the adaptation process for a new smart home user Artif. Intell. XXXVI 2019 Cham Springer 421-434

[7]

Ali SMM, Augusto JC, and Windridge D A survey of user-centred approaches for smart home transfer learning and new user home automation adaptation Appl. Artif. Intell. 2019 33 8 747-774

[8]

Amiribesheli, M., Bouchachia, A.: Smart homes design for people with dementia. In: International Conference on Intelligent Environments, pp. 156–159 (2015)

[9]

Aranbarri-Zinkunegi, M.: Improving the pattern learning system integrating the reasoning system (2017)

[10]

Ariano, R., Manca, M., Paternò, F., Santoro, C.: Smartphone-based augmented reality for end-user creation of home automations. Behav. Inf. Technol. 1–17 (2022).

[11]

Arifoglu D and Bouchachia A Activity recognition and abnormal behaviour detection with recurrent neural networks Procedia Comput. Sci. 2017 110 86-93

[12]

Augusto J, Kramer D, Alegre U, Covaci A, and Santokhee A The user-centred intelligent environments development process as a guide to co-create smart technology for people with special needs Univ. Access Inf. Soc. 2017 17 1-16

[13]

Augusto JC A general framework for reasoning about change New Gener. Comput. 2003 21 1 209-246

[14]

Augusto, J.C., JoseQuinde, M., Oguego, C.L.: Context-aware systems testing and validation. In: 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT), pp. 7–12 (2019)

[15]

Azkune G, Almeida A, López-de Ipiña D, and Chen L Combining users’ activity survey and simulators to evaluate human activity recognition systems Sensors 2015 15 4 8192-8213

[16]

Aztiria A, Augusto JC, Basagoiti R, Izaguirre A, and Cook DJ Learning frequent behaviors of the users in intelligent environments IEEE Trans. Syst. Man Cybern. Syst. 2013 43 1265-1278

[17]

Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17 (2004)

[18]

Brdiczka O, Crowley JL, and Reignier P Learning situation models in a smart home IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2009 39 1 56-63

[19]

Caivano D, Fogli D, Lanzilotti R, Piccinno A, and Cassano F Supporting end users to control their smart home: design implications from a literature review and an empirical investigation J. Syst. Softw. 2018 144 295-313

[20]

Chiang, Y.T., Hsu, J.Y.J.: Knowledge transfer in activity recognition using sensor profile. In: 2012 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), pp 180–187 (2012)

[21]

Chiang YT, Lu CH, and Hsu JYJ A feature-based knowledge transfer framework for cross-environment activity recognition toward smart home applications IEEE Trans. Hum. Mach. Syst. 2017 47 310-322

[22]

Cook D and Das S Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) 2004 Hoboken Wiley-Interscience

[23]

Cook D, Feuz KD, and Krishnan NC Transfer learning for activity recognition: a survey Knowl. Inf. Syst. 2013 36 3 537-556

[24]

Cook DJ, Augusto JC, and Jakkula VR Review: Ambient intelligence: technologies, applications, and opportunities Pervasive Mob. Comput. 2009 5 4 277-298

[25]

Galton, A., Augusto, J.: Stratified causal theories for reasoning about deterministic devices and protocols. In: Proceedings Ninth International Symposium on Temporal Representation and Reasoning, pp. 52–54 (2002).

[26]

Guralnik, V., Haigh, K.Z.: Learning models of human behaviour with sequential patterns. In: Proceedings of the AAAI-02 Workshop “Automation as Caregiver”, pp. 24–30 (2002)

[27]

Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 1533–1540 (2016)

[28]

Ho, B., Vogts, D., Wesson, J.: A smart home simulation tool to support the recognition of activities of daily living. In: Proceedings of the South African Institute of Computer Scientists and Information Technologists 2019, Association for Computing Machinery, New York, NY, USA, SAICSIT ’19 (2019)

[29]

Ibarra, U.A., Augusto, J.C., Aztiria Goenaga, A.: Temporal reasoning for intuitive specification of context-awareness. In: 2014 International Conference on Intelligent Environments, pp. 234–241 (2014).

[30]

Jebara, T.: Generative versus discriminative learning. In: Machine Learning: Discriminative and Generative, pp. 17–60. Springer, Boston (2004)

[31]

Lei, Z., Yue, S., Yu, C., Yuanchun, S.: SHSim: an OSGI-based smart home simulator. In: IEEE International Conference on Ubi-media Computing, pp. 87–90 (2010)

[32]

Liu L, Peng Y, Liu M, and Huang Z Sensor-based human activity recognition system with a multilayered model using time series shapelets Knowl. Based Syst. 2015 90 138-152

[33]

Mckeever S, Ye J, Coyle L, Bleakley C, and Dobson S Activity recognition using temporal evidence theory J. Ambient Intell. Smart Environ. 2010 2 3 253-269

[34]

Moeslund TB, Hilton A, and Krüger V A survey of advances in vision-based human motion capture and analysis Comput. Vis. Image Underst. 2006 104 2 90-126

[35]

Pal, M., Mather, P.M.: Decision tree based classification of remotely sensed data. In: Proceedings of the 22nd Asian Conference of Remote Sensing, Singapore (2001)

[36]

Quinde M, Khan N, and Augusto JC Miesenberger K and Kouroupetroglou G Personalisation of context-aware solutions supporting asthma management Computers Helping People with Special Needs 2018 Cham Springer 510-519

[37]

Rao SP and Cook DJ Predicting inhabitant action using action and task models with application to smart homes Int. J. Artif. Intell. Tools 2004 13 81-100

[38]

Salovaara, A., Bellucci, A., Vianello, A., Jacucci, G.: Programmable smart home toolkits should better address households’ social needs. In: Association for Computing Machinery, New York, NY, USA, CHI ’21 (2021).

[39]

Sarkar AJ, Lee YK, and Lee S A smoothed naive Bayes-based classifier for activity recognition IETE Tech. Rev. 2010 27 2 107-119

[40]

Serrano E, Botia JA, and Cadenas JM Ubik: a multi-agent based simulator for ubiquitous computing applications J. Phys. Agents 2009 3 2 39-43

[41]

Stankovski V and Trnkoczy J Augusto JC and Nugent CD Application of decision trees to smart homes Designing Smart Homes: The Role of Artificial Intelligence 2006 Berlin Springer 132-145

[42]

Stockwell DR and Peterson A Effects of sample size on accuracy of species distribution models Ecol. Model. 2002 148 1 1-13

Cited By

Gómez-Sirvent JFernández-Sotos DLópez de la Rosa FFernández-Caballero A(2024)Building information modeling and affective occupancy evaluationJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23004616:2(155-166)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/AIS-230046

Recommendations

Improving the Adaptation Process for a New Smart Home User
Artificial Intelligence XXXVI
Abstract
Artificial Intelligence (AI) has been around for many years and plays a vital role in developing automatic systems that require decision using a data- or model-driven approach. Smart homes are one such system; in them, AI is used to recognize user ...
Smart Home Technologies: A Preliminary Review
ICIT '18: Proceedings of the 6th International Conference on Information Technology: IoT and Smart City

In recent years, smart homes have become increasingly popular with the deployment of Internet of Things (IoT). Rapid diffusion of sensing technology also enabled advancement in smart homes. The advancements in these technologies surroundings smart homes ...
Smart Home 2.0: Innovative Smart Home System Powered by Botanical IoT and Emotion Detection

Advances in human-centric technologies, such as artificial intelligence (AI), application-oriented sensing and smart home, along with recent developments in internet of things (IoT) and machine-to-machine (M2M) networks are enabling the design and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Universal Access in the Information Society

Universal Access in the Information Society Volume 22, Issue 3

Aug 2023

395 pages

ISSN:1615-5289

EISSN:1615-5297

Issue’s Table of Contents

© The Author(s) 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 June 2022

Accepted: 28 April 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gómez-Sirvent JFernández-Sotos DLópez de la Rosa FFernández-Caballero A(2024)Building information modeling and affective occupancy evaluationJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23004616:2(155-166)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/AIS-230046

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents