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An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space

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Abstract

In this paper, a context-sensitive descriptive language is proposed to design and model the daily living activities of elderly people. The objective is to simplify and represent correctly the knowledge collected by sensors (low level) and to have a relevant recognition of the person’s knowledge (high level). The proposed language is based on several rules and constraints through intelligent meaning. It is dedicated to a better understanding and semantic design and description of the behavior of elderly people. Subsequently, in order to provide a powerful knowledge recognition system, a hybrid Markov model is proposed to recognize and predict the activities designed by the proposed language. The proposed model is adapted to the reasoning of the new language. This allows providing a hierarchical and temporal relationship within the knowledge. It is responsible to recognize and predict the behavior of the elderly people efficiently. The flexibility and the intelligibility of the proposed language is proven and the accuracy of the recognition model is demonstrated which ensures the efficiency of the proposed knowledge recognition system.

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References

  • Abdelrahman G, Wang Q (2019) Knowledge tracing with sequential key-value memory networks. In: Proceedings of the 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR), Paris, France, pp 175–184

  • Abubaker E, Ahmad L, Caroline L (2019) The human behaviour indicator: a measure of behavioural evolution. Expert Syst Appl 118:493–505

    Article  Google Scholar 

  • Asad Masood K, Noman A, Mohammad A, Taqdir A, Adil Mehmood K, Seokhee J, Myunggwon H, Sungyoung L (2014) Context representation and fusion: advancements and opportunities. Sensors 14(6):9628–9668

    Article  Google Scholar 

  • Aysenur B, Hani H, van Joy H, Daniyal A (2016) A linear general type-2 fuzzy-logic-based computing with words approach for realizing an ambient intelligent platform for cooking recipe recommendation. IEEE Trans Fuzzy Syst 24(2):306–329

    Article  Google Scholar 

  • Bae I-H (2014) An ontology-based approach to ADL recognition in smart homes. Future Gener Comput Syst 33:32–41

    Article  Google Scholar 

  • Bobillo F, Straccia U (2016) The fuzzy ontology reasoner fuzzyDL. Knowl Based Syst 95:12–34

    Article  Google Scholar 

  • Das S, Ghosh PK, Kar S (2013) Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system. In: Proceedings of the 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–7

  • Debajyoti P, Tuul T, Suree F, Wichian C (2018) Smart homes and quality of life for the elderly: perspective of competing models. IEEE Access 6:8109–8122

    Article  Google Scholar 

  • Diane JC, Aaron SC, Brian LT, Narayanan CK (2013) Casas: a smart home in a box. IEEE Comput 46:62–69

    Google Scholar 

  • Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Process 21(5):2248–2266

    Article  MathSciNet  Google Scholar 

  • Elena L, Michele Q, Francesco S, Demetris V (2018) The effect of digital technologies adoption in healthcare industry: a case based analysis. Bus Process Manag J 24(5):1124–1144

    Article  Google Scholar 

  • Ferilli S (2014) A smart home agent for plan recognition of cognitively-impaired patients. IEEE Trans Syst Man Cybern Syst 44(6):744–756

    Article  Google Scholar 

  • Francesco G, Stefano S, Mario C (2019) Exploit hierarchical label knowledge for deep learning. In: 2019 IEEE 32nd International Symposium on Computer- Based Medical Systems (CBMS), pp 539–542

  • Gayathri K, Easwarakumar K, Elias S (2017) Probabilistic ontology based activity recognition in smart homes using Markov logic network. Knowl Based Syst 121:173–184

    Article  Google Scholar 

  • Georgios M, Ioannis K (2017) iKnow: ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob Comput 40:17–41

    Article  Google Scholar 

  • Hill R, Betts LR, Gardner SE (2015) Older adults’ experiences and perceptions of digital technology: (dis)empowerment, wellbeing, and inclusion. Comput Hum Behav 48:415–423

    Article  Google Scholar 

  • Hossain HS, Khan MAAH, Roy N (2017) Active learning enabled activity recognition. Pervasive Mob Comput 38:312–330

    Article  Google Scholar 

  • Hussein A, Adda M, Atieh M, Fahs W (2014) Smart home design for disabled people based on neural networks. Procedia Comput Sci 37:117–126

    Article  Google Scholar 

  • Kabir MH, Hoque MR, Thapa K, Yang S-H (2016) Two-layer hidden Markov model for human activity recognition in home environments. Int J Distrib Sens Netw 12(1):4560365

    Article  Google Scholar 

  • Kim Y, Kang B, Kim D (2015) Hidden Markov model ensemble for activity recognition using tri-axis accelerometer. In: Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp 3036–3041

  • Kolekar MH, Dash DP (2016) Hidden Markov model based human activity recognition using shape and optical ow based features. In: Proceedings of the 2016 IEEE Region 10 Conference (TENCON), pp 393–397

  • Kong W, Dong ZY, Hill DJ, Ma J, Zhao JH, Luo FJ (2018) A hierarchical hidden Markov model framework for home appliance modeling. IEEE Trans Smart Grid 9(4):3079–3090

    Article  Google Scholar 

  • Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2016a) A genetic neural network approach for unusual behavior prediction in smart home. In: International Conference on Intelligent Systems Design and Applications, Springer, pp 738–748

  • Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2016b) A Markovian based approach for daily living activities recognition. In: Proceedings of the 5th International Confererence on Sensor Networks (SENSORNETS), Rome, Italy, pp 214–219

  • Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2016c) An improved Elman neural network for daily living activities recognition. In: International Conference on Intelligent Systems Design and Applications, Springer, pp 697–707

  • Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2018) An improved extreme learning machine model for the prediction of human scenarios in smart homes. J Appl Intell 48:2017–2030

    Article  Google Scholar 

  • Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  • Liu Z, Song Y, Shang Y, Wang J (2015) Posture recognition algorithm for the elderly based on BP neural networks. In: Proceedings of the 27th Chinese Control and Decision Conference (CCDC), pp 1446–1449

  • Mansur Kazemi B, Masud Kazemi B, Seyed A H RE (2014) Introduce an object oriented simulator for analyzing discrete events in smart buildings. In: International Congress on Technology, Communication and Knowledge (ICTCK), pp 1–5

  • McDonald H, Nugent CD, Finlay DD, Moore G, Burns W, Hallberg J (2013) Assessing the impact of the homeML format and the homeML suite within the research community. J UCS 19(17):2559–2576

    Google Scholar 

  • Mshali H, Lemlouma T, Magoni D (2018) Adaptive monitoring system for e-health smart homes. Pervasive Mob Comput 43:1–19

    Article  Google Scholar 

  • Muhammad Mahtab A, Hassan M, Muhidul Islam K, Tamas P, Alar K, Yannick LM (2018) A survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access 6:36611–36631

    Article  Google Scholar 

  • Noor MHM, Salcic Z, Kevin I, Wang K (2018) Ontology-based sensor fusion activity recognition. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0668-0

  • Parisa R, Alex M (2013) A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inform 17(3):579–590

    Article  Google Scholar 

  • Rahman MS, Ko M, Warren J, Carpenter D (2016) Healthcare technology self-efficacy (HTSE) and its in uence on individual attitude: an empirical study. Comput Hum Behav 58:12–24

    Article  Google Scholar 

  • Rodríguez ND, Cuéllar MP, Lilius J, Calvo-Flores MD (2014) A survey on ontologies for human behavior recognition. ACM Comput Surv 46(4):43

    Article  Google Scholar 

  • Ronao CA, Cho S-B (2017) Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models. Int J Distrib Sens Netw 13(1):1550147716683687

    Article  Google Scholar 

  • Salisu Wada Y, Ahmad L, Mufti M (2019) A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl Soft Comput 83:105613

    Article  Google Scholar 

  • Sanjari MJ, Karami H, Gooi HB (2017) Analytical rule-based approach to online optimal control of smart residential energy system. IEEE Trans Ind Inform 13(4):1586–1597

    Article  Google Scholar 

  • Suryadevara NK, Mukhopadhyay SC, Wang R, Rayudu R (2013) Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng Appl Artif Intell 26(10):2641–2652

    Article  Google Scholar 

  • Ta Minh T, Nguyen Huu T, Ngoc-Tu H (2018) Key-value based data hiding method for NoSQL database. In: 10th International Conference on Knowledge and Systems Engineering (KSE), pp 193–197

  • Thanos GS, Efstratios K, Nick B, John A, Antonis B, Dimitris V, Ioannis V (2015) Rule-based approaches for energy savings in an ambient intelligence environment. Pervasive Mob Comput 19:1–23

    Article  Google Scholar 

  • United Nations (2019) World Population Ageing 2019, highlights [online]. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf. Accessed 22 Sept 2019

  • Valentina B, Marco B, Gianfranco L, Paolo F, Monica M, Ilaria DM (2019) IoT wearable sensor and deep learning: an integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet Thing J 6(5):8553–8562

    Article  Google Scholar 

  • Wang C, Xu Y, Zhang J, Yu W (2016) SW-HMM: a method for evaluating confidence of smartphone-based activity recognition. In: Proceedings of the 2016 IEEE Trustcom/BigDataSE/ISPA, pp 2086–2091

  • Wemlinger ZE, Holder LB (2018) Cross-environment activity recognition using a shared semantic vocabulary. Pervasive Mob Comput 51:150–159

    Article  Google Scholar 

  • Wen J, Wang Z (2017) Learning general model for activity recognition with limited labelled data. Expert Syst Appl 74:19–28

    Article  Google Scholar 

  • Wickramasinghe A, Torres RLS, Ranasinghe DC (2017) Recognition of falls using dense sensing in an ambient assisted living environment. Pervasive Mob Comput 34:14–24

    Article  Google Scholar 

  • Wu E, Zhang P, Lu T, Gu H, Gu N (2016) Behavior prediction using an improved hidden Markov model to support people with disabilities in smart homes. In: Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp 560–565

  • Ye J, Stevenson G, Dobson S (2015) USMART: an unsupervised semantic mining activity recognition technique. ACM Trans Interact Intell Syst 4(4):16

    Article  Google Scholar 

  • Yiyan L, Fang Z, Wenhua S, Haiyong L (2016) An hidden Markov model based complex walking pattern recognition algorithm. In: Proceedings of the 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS), pp 223–229

  • Yu S, Chen H, Brown RA (2018) Hidden Markov model-based fall detection with motion sensor orientation calibration: a case for real-life home monitoring. IEEE J Biomed Health Inform 22(6):1847–1853

    Article  Google Scholar 

  • Yuan B, Herbert J (2014) Context-aware hybrid reasoning framework for pervasive healthcare. Pers Ubiquitous Comput 18(4):865–881

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their thanks to all the team of the project “e-Health Monitoring Open Data project”.

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Correspondence to Zaineb Liouane.

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Liouane, Z., Lemlouma, T., Roose, P. et al. An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space. J Ambient Intell Human Comput 11, 6059–6075 (2020). https://doi.org/10.1007/s12652-020-01876-5

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