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FriendCare-AAL: a robust social IoT based alert generation system for ambient assisted living

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Abstract

The use of advanced communication technologies such as Internet of Things (IoT) in the domain of Ambient Assisted Living (AAL) tends to promote the quality of living for elderly staying independently. However, the state of the art IoT based solutions for AAL systems have not fully expressed the importance of building social connections between smart devices. This paper attempts to study the significance of deploying socially enabled IoT systems in AAL environment by proposing a robust Social IoT based AAL system for elderly named FriendCare-AAL. In addition, it presents a schematic approach to establish a partnership among smart devices and introduces the concept of responsibility offloading between devices. The proposed system is capable of providing assistance to the elderly staying in smart home environment. In case of emergency, the system automatically generates alerts intimating about the situation to the concerned entities. To experimentally evaluate the system’s performance, a smart home AAL environment for an elderly person is simulated using human activity simulator namely ‘Home Sensor Simulator’ and person’s routine dataset is generated. Further, two machine learning models; Naive Bayes (NB) and Random Forest (RF) are employed to analyze the data in order to predict the well being of the elderly person. The performance of the two classifiers is assessed using metrics such as sensitivity, specificity, detection rate and accuracy. Experimental results revealed that RF classifier outperforms NB classifier in terms of overall accuracy, detection rate and balanced accuracy. The overall accuracy is observed to be 89.2% for RF and 83.9% for NB classifier. Furthermore, a performance comparison of the proposed model is performed with two baseline approaches. A system prototype is also developed using Node-Red simulation tool to determine the performance of the proposed system in real-world and failure-prone environments. It turns out that the system performs well in critical situations with a tolerable response time of less than 1.2 s for a high failure rate of upto 50%.

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References

  • Abdelgawad A, Yelamarthi K, Khattab A (2016) IoT-based health monitoring system for active and assisted living. In: International Conference on Smart Objects and Technologies for Social Good (pp. 11–20). Springer, Cham

  • Aftab H, Gilani K, Lee J, Nkenyereye L, Jeong S, Song J (2020) Analysis of identifiers in IoT platforms. Digit Commun Netw 6(3):333–340

    Article  Google Scholar 

  • Amoretti M, Copelli S, Wientapper F, Furfari F, Lenzi S, Chessa S (2013) Sensor data fusion for activity monitoring in the PERSONA ambient assisted living project. J Ambient Intell Hum Comput 4(1):67–84

    Article  Google Scholar 

  • Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2013) A public domain dataset for human activity recognition using smartphones. In Esann (Vol. 3, p. 3)

  • Atzori L, Iera A, Morabito G (2011) Siot: Giving a social structure to the internet of things. IEEE Commun Lett 15(11):1193–1195

    Article  Google Scholar 

  • Atzori L, Iera A, Morabito G, Nitti M (2012) The social internet of things (siot)–when social networks meet the internet of things: Concept, architecture and network characterization. Comput Netw 56(16):3594–3608

    Article  Google Scholar 

  • Augusto JC, Nakashima H, Aghajan H (2010) Ambient intelligence and smart environments: a state of the art. In: Handbook of ambient intelligence and smart environments (pp. 3–31). Springer, Boston, MA

  • Azimi I, Rahmani AM, Liljeberg P, Tenhunen H (2017) Internet of things for remote elderly monitoring: a study from user-centered perspective. J Ambient Intell Hum Comput 8(2):273–289

    Article  Google Scholar 

  • Borelli E, Paolini G, Antoniazzi F, Barbiroli M, Benassi F, Chesani F et al (2019) HABITAT: an IoT solution for independent elderly. Sensors 19(5):1258

    Article  Google Scholar 

  • Calvaresi D, Cesarini D, Sernani P, Marinoni M, Dragoni AF, Sturm A (2017) Exploring the ambient assisted living domain: a systematic review. J Ambient Intell Hum Comput 8(2):239–257

    Article  Google Scholar 

  • Catarinucci L, de Donno D, Mainetti L, Palano L, Patrono L, Stefanizzi ML, Tarricone L (2015) An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 2(6):515–526

    Article  Google Scholar 

  • Chernbumroong S, Cang S, Atkins A, Yu H (2013) Elderly activities recognition and classification for applications in assisted living. Expert Syst Appl 40(5):1662–1674

    Article  Google Scholar 

  • Corno F, De Russis L, Roffarello AM (2016) A healthcare support system for assisted living facilities: An iot solution. In 2016 IEEE 40th annual computer software and applications conference (COMPSAC), IEEE, Vol 1, pp 344–352

  • Coronato A (2012) Uranus: A middleware architecture for dependable AAL and vital signs monitoring applications. Sensors 12(3):3145–3161

    Article  Google Scholar 

  • Coskun V, Ozdenizci B, Ok K (2013) A survey on near field communication (NFC) technology. Wireless Pers Commun 71(3):2259–2294

    Article  Google Scholar 

  • Cumin J, Lefebvre G, Ramparany F, Crowley JL (2017) A dataset of routine daily activities in an instrumented home. In: International Conference on Ubiquitous Computing and Ambient Intelligence, Springer, Cham, pp 413–425

  • Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923

    Article  Google Scholar 

  • Esnaola U, Smithers T (2006) Whistling to machines. In Ambient intelligence in everyday life (pp. 198–226). Springer, Berlin, Heidelberg

  • Fawcett T (2004) ROC graphs: notes and practical considerations for researchers. Mach Learn 31(1):1–38

    MathSciNet  Google Scholar 

  • Figueiredo CP, Gama ÓS, Pereira CM, Mendes PM, Silva S, Domingues L, Hoffmann KP (2010) Autonomy suitability of wireless modules for ambient assisted living applications: Wifi, zigbee, and proprietary devices. In 2010 Fourth international conference on sensor technologies and applications (pp 169–172), IEEE

  • Fiske AP (1992) The four elementary forms of sociality: framework for a unified theory of social relations. Psychol Rev 99(4):689

    Article  Google Scholar 

  • Grguric A (2012) ICT towards elderly independent living. Research and Development Centre, Ericsson Nikola Tesla

    Google Scholar 

  • Gupta M, Gao J, Aggarwal CC, Han J (2013) Outlier detection for temporal data: a survey. IEEE Trans Knowl Data Eng 26(9):2250–2267

    Article  Google Scholar 

  • Halfacree G, Upton E (2012) Raspberry Pi user guide. John Wiley & Sons

    Google Scholar 

  • Happ D, Karowski N, Menzel T, Handziski V, Wolisz A (2017) Meeting IoT platform requirements with open pub/sub solutions. Ann Telecommun 72(1–2):41–52

    Article  Google Scholar 

  • Hooda D, Rani R (2020) Ontology driven human activity recognition in heterogeneous sensor measurements. J Ambient Intell Hum Comput 11:1–14

    Article  Google Scholar 

  • Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: a semantic web rule language combining OWL and RuleML. W3C Mem Submiss 21(79):1–31

    Google Scholar 

  • Hosseinzadeh, M., Koohpayehzadeh, J., Ghafour, M. Y., Ahmed, A. M., Asghari, P., Souri, A. et al (2020). An elderly health monitoring system based on biological and behavioral indicators in internet of things. Journal of Ambient Intelligence and Humanized Computing, 1–11. http://www.dspguide.com/CH28.PDF. https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Report.pdf

  • Istepanian RS (2011) The potential of Internet of Things (IoT) for assisted living applications. In IET Seminar on Assisted Living 2011 (pp. 1–40). IET

  • Jara AJ, López P, Fern'ndez D, Úbeda B, Zamora MA, Skarmeta AF (2012) Interaction of patients with breathing problems through NFC in Ambient Assisted Living environments. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (pp. 892–897) IEEE

  • Jara AJ, Zamora MA, Skarmeta AF (2011) An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL). Pers Ubiquit Comput 15(4):431–440

    Article  Google Scholar 

  • Javed A (2016) Building Arduino Projects for the Internet of Things. Apress Media, LLC, Experiments with Real-World Applications. United States of America, pp 15–34

    Google Scholar 

  • Karagiannis V, Chatzimisios P, Vazquez-Gallego F, Alonso-Zarate J (2015) A survey on application layer protocols for the internet of things. Trans IoT Cloud Comput 3(1):11–17

    Google Scholar 

  • Kim J, Lee JW (2014) OpenIoT: an open service framework for the Internet of Things. In 2014 IEEE world forum on internet of things (WF-IoT) (pp. 89–93). IEEE

  • Kormányos B, Pataki B (2013) Multilevel simulation of daily activities: Why and how?. In: 2013 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA) (pp 1–6). IEEE

  • Kumar DP, Amgoth T, Annavarapu CSR (2019) Machine learning algorithms for wireless sensor networks: a survey. Inf Fusion 49:1–25

    Article  Google Scholar 

  • Leutheuser H, Schuldhaus D, Eskofier BM (2013) Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset. PLoS ONE 8(10):e75196

    Article  Google Scholar 

  • Lloret J, Canovas A, Sendra S, Parra L (2015) A smart communication architecture for ambient assisted living. IEEE Commun Mag 53(1):26–33

    Article  Google Scholar 

  • Mainetti L, Patrono L, Secco A, Sergi I (2016) An IoT-aware AAL system for elderly people. In: 2016 International multidisciplinary conference on computer and energy science (SpliTech) (pp 1–6), IEEE

  • Memon M, Wagner SR, Pedersen CF, Beevi FHA, Hansen FO (2014) Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14(3):4312–4341

    Article  Google Scholar 

  • Mojarad R, Attal F, Chibani A, Amirat Y (2020) A hybrid context-aware framework to detect abnormal human daily living behavior. In: 2020 International joint conference on neural networks (IJCNN), pp 1–8. IEEE

  • Nitti M, Atzori L, Cvijikj IP (2014) Friendship selection in the social internet of things: challenges and possible strategies. IEEE Internet Things J 2(3):240–247

    Article  Google Scholar 

  • Node Application Metrics (2019) Available at: https://www.npmjs.com/package/appmetrics

  • Node-Red node (2019) Available at: https://www.npmjs.com/package/node-red-node-smooth

  • Novák M, Jakab F, Lain L (2013) Anomaly detection in user daily patterns in smart-home environment. J Sel Areas Health Inform 3(6):1–11

    Google Scholar 

  • O’Brien E (1991) Murphy J. Tyndall A. Atkins N, Mee F. McCarthy G, Staessen J. Cox J, O’Malley K. Twenty-four-hour ambulatory blood pressure in men and women aged 17 to 80 years: the Allied Irish Bank Study. J Hypertens 9:355–360

    Article  Google Scholar 

  • Ortiz AM, Hussein D, Park S, Han SN, Crespi N (2014) The cluster between internet of things and social networks: Review and research challenges. IEEE Internet Things J 1(3):206–215

    Article  Google Scholar 

  • Park K, Park J, Lee J (2017) An IoT system for remote monitoring of patients at home. Appl Sci 7(3):260

    Article  Google Scholar 

  • Seeger C, Van Laerhoven K, Buchmann A (2014) MyHealthAssistant: an event-driven middleware for multiple medical applications on a smartphone-mediated body sensor network. IEEE J Biomed Health Inform 19(2):752–760

    Article  Google Scholar 

  • Shin JH, Lee B, Park KS (2011) Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Trans Inf Technol Biomed 15(3):438–448

    Article  Google Scholar 

  • Sirin E, Parsia B, Grau BC, Kalyanpur A, Katz Y (2007) Pellet: a practical owl-dl reasoner. J Web Semant 5(2):51–53

    Article  Google Scholar 

  • Smith SW (1997) The scientist and engineer's guide to digital signal processing

  • Stavrotheodoros S, Kaklanis N, Votis K, Tzovaras D (2018) A smart-home IoT infrastructure for the support of independent living of older adults. In: IFIP international conference on artificial intelligence applications and innovations (pp 238–249). Springer, Cham

  • Turcu CE, Turcu CO (2017) Social Internet of things in healthcare: from things to social things in Internet of things. In: The internet of things: breakthroughs in research and practice (pp 88–111). IGI Global

  • Vora J, Tanwar S, Tyagi S, Kumar N, Rodrigues JJ (2017) FAAL: fog computing-based patient monitoring system for ambient assisted living. In: 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom) (pp 1–6). IEEE

  • Warriach EU, Kaldeli E, Lazovik A, Aiello M (2013) An interplatform service-oriented middleware for the smart home. Int J Smart Home 7(1):115–141

    Google Scholar 

  • World Population Ageing Highlights (2019) United Nation Publications, UN 2020

  • Yang G, Xie L, Mäntysalo M, Zhou X, Pang Z, Da Xu L, Chen Q, Zheng LR (2014) A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans Ind Inf 10(4):2180–2191

    Article  Google Scholar 

  • Zgheib R, Conchon E, Bastide R (2019) Semantic middleware architectures for IoT healthcare applications. In: Enhanced Living Environments (pp 263–294), Springer, Cham

  • Zgheib, R., Kristiansen, S., Conchon, E., Plageman, T., Goebel, V., & Bastide, R. (2020). A scalable semantic framework for IoT healthcare applications. J Ambient Intell Hum Comput 1–19

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Acknowledgments

The authors wish to acknowledge UGC, New Delhi, India for SRF scholarship.

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  1. Department of CSE, GNDU, Regional Campus, Jalandhar, India

    Nancy Gulati & Pankaj Deep Kaur

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  1. Nancy Gulati
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Correspondence to Nancy Gulati.

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Gulati, N., Kaur, P.D. FriendCare-AAL: a robust social IoT based alert generation system for ambient assisted living. J Ambient Intell Human Comput 13, 1735–1762 (2022). https://doi.org/10.1007/s12652-021-03236-3

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