Sage Journals: Discover world-class research

Abstract

Activity and behaviour monitoring of inhabitants play an essential role in an ambient environment. Different researchers proposed many promising solutions; this discipline, however, needs more accurate results. The main reason for this insufficiency is the imprecision data generated by the intelligent algorithms due to irregularities and unpredictability involved in users action and behaviour. The success of today’s system primarily ends on controlled and well-defined activities and conduct. However, in the real-world scenario, this is difficult to accomplish and requires more sophisticated strategies. Self-learning systems, in most cases, used to develop state-of-the-art in smart and cognitive environments. In this paper, we present a comprehensive review of different activity and behaviour analysis methods to identify seventeen critical challenges allied to ambient assisted living systems (AALs). Our primary objective is to offer a comprehensive guide to select the best approach to determine activity and human behaviour in the smart environment. Moreover, the study will present a better understanding of existing problems and a direction for future research.

Keywords

Activity recognition ambient intelligence assisted living anomaly detection smart environment

Get full access to this article

View all access options for this article.

References

Abtoy,

Touhafi,

Tahiri et al., Ambient assisted living system’s models and architectures: A survey of the state of the art, Journal of King Saud University-Computer and Information Sciences (2018).

M.J.

Akhlaghinia,

Lotfi,

Langensiepen and

Sherkat, Occupant behaviour prediction in ambient intelligence computing environment, Journal of Uncertain Systems 2(2) (2008), 85–100.

Alemdar,

Tunca and

Ersoy, Daily life behaviour monitoring for health assessment using machine learning: Bridging the gap between domains, Personal and Ubiquitous Computing 19(2) (2015), 303–315. doi:10.1007/s00779-014-0823-y.

Amoretti,

Copelli,

Wientapper,

Furfari,

Lenzi and

Chessa, Sensor data fusion for activity monitoring in the persona ambient assisted living project, Journal of Ambient Intelligence and Humanized Computing 4(1) (2013), 67–84. doi:10.1007/s12652-011-0095-6.

Andò,

Baglio,

C.O.

Lombardo and

Marletta, Smart multisensor strategies for indoor localization, in: Mobility of Visually Impaired People, Springer, 2018, pp. 585–595. doi:10.1007/978-3-319-54446-5_18.

Atallah and

G.-Z.

Yang, The use of pervasive sensing for behaviour profiling – a survey, Pervasive and Mobile Computing 5(5) (2009), 447–464. doi:10.1016/j.pmcj.2009.06.009.

Augello and

Gaglio, Detection of user activities in intelligent environments, in: Advances onto the Internet of Things, Springer, 2014, pp. 19–32. doi:10.1007/978-3-319-03992-3_2.

J.C.

Augusto, Past, present and future of ambient intelligence and smart environments, in: International Conference on Agents and Artificial Intelligence, Springer, 2009, pp. 3–15.

Aumayr,

Bleier and

Sturm, Transformation and new topics in health care: A short overview on mHealth and AAL with example projects, DigitCult-Scientific Journal on Digital Cultures 2(2) (2018), 25–36.

10.

I.A.

Awada,

Cramariuc,

Mocanu,

Seceleanu,

Kunnappilly and

A.M.

Florea, An end-user perspective on the CAMI ambient and assisted living project, in: 12th Annual International Technology, Education and Development Conference INTED 2018, 05 Mar 2018, Valencia, Spain, 2018.

11.

Aztiria,

J.C.

Augusto,

Basagoiti,

Izaguirre and

D.J.

Cook, Learning frequent behaviors of the users in intelligent environments, IEEE Transactions on Systems, Man, and Cybernetics: Systems 43(6) (2013), 1265–1278. doi:10.1109/TSMC.2013.2252892.

12.

Aztiria,

Izaguirre,

Basagoiti and

J.C.

Augusto, Learning about preferences and common behaviours of the user in an intelligent environment, in: Behaviour Monitoring and Interpretation (BMI) Book, Vol. 3, 2009, pp. 289–315.

13.

Aztiria,

Izaguirre,

Basagoiti and

J.C.

Augusto, Learning about preferences and common behaviours of the user in an intelligent environment, in: BMI Book, 2009, pp. 289–315.

14.

U.A.B.U.A.

Bakar,

Ghayvat,

S.F.

Hasanm and

S.C.

Mukhopadhyay, Activity and anomaly detection in smart home: A survey, in: Next Generation Sensors and Systems, Springer, 2016, pp. 191–220.

15.

Bamis,

Lymberopoulos,

Teixeira and

Savvides, The BehaviorScope framework for enabling ambient assisted living, Personal and Ubiquitous Computing 14(6) (2010), 473–487. doi:10.1007/s00779-010-0282-z.

16.

Barsocchi,

M.G.C.A.

Cimino,

Ferro,

Lazzeri,

Palumbo and

Vaglini, Monitoring elderly behavior via indoor position-based stigmergy, Pervasive and Mobile Computing 23 (2015), 26–42. doi:10.1016/j.pmcj.2015.04.003.

17.

Bellagente,

Crema,

Depari,

Ferrari,

Flammini,

Lanfranchi,

Lenzi,

Maddiona,

Rinaldi,

Sisinni et al., Remote and non-invasive monitoring of elderly in a smart city context, in: Sensors Applications Symposium (SAS), 2018 IEEE, IEEE, 2018, pp. 1–6.

18.

Berrouiguet,

M.M.

Perez-Rodriguez,

Larsen,

Baca-García,

Courtet and

Oquendo, From eHealth to iHealth: Transition to participatory and personalized medicine in mental health, Journal of Medical Internet Research 20(1) (2018). doi:10.2196/jmir.8338.

19.

Bouchard,

Hao,

Bouchard,

Gaboury,

M.T.

Moutacalli,

Gouin-Vallerand,

H.K.

Ngankam,

Pigot and

Giroux, The cornerstones of smart home research for healthcare, in: Advanced Data Analytics in Health, Springer, 2018, pp. 185–200. doi:10.1007/978-3-319-77911-9_11.

20.

S.T.M.

Bourobou and

Yoo, User activity recognition in smart homes using pattern clustering applied to temporal ANN algorithm, Sensors 15(5) (2015), 11953–11971. doi:10.3390/s150511953.

21.

Buettner,

Prasad,

Philipose and

Wetherall, Recognizing daily activities with RFID-based sensors, in: Proceedings of the 11th International Conference on Ubiquitous Computing, ACM, 2009, pp. 51–60.

22.

M.M.

Bugge,

Coenen and

Branstad, Governing socio-technical change: Orchestrating demand for assisted living in ageing societies, Science and Public Policy (2018).

23.

Calvaresi,

Cesarini,

Sernani,

Marinoni,

A.F.

Dragoni and

Sturm, Exploring the ambient assisted living domain: A systematic review, Journal of Ambient Intelligence and Humanized Computing 8(2) (2017), 239–257. doi:10.1007/s12652-016-0374-3.

24.

Cardinaux,

Brownsell,

Hawley and

Bradley, Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance, in: Iberoamerican Congress on Pattern Recognition, Springer, 2008, pp. 243–251.

25.

B.D.

Carolis,

Ferilli and

Redavid, Incremental learning of daily routines as workflows in a smart home environment, ACM Transactions on Interactive Intelligent Systems (TiiS) 4(4) (2015), 20.

26.

J.C.

Castillo,

Á.

Castro-González,

Alonso-Martín,

Fernández-Caballero and

M.Á.

Salichs, Emotion detection and regulation from personal assistant robot in smart environment, in: Personal Assistants: Emerging Computational Technologies, Springer, 2018, pp. 179–195.

27.

Cesta,

Cortellessa,

Fracasso,

Orlandini and

Turno, User needs and preferences on AAL systems that support older adults and their carers, Journal of Ambient Intelligence and Smart Environments 10(1) (2018), 49–70. doi:10.3233/AIS-170471.

28.

K.-C.

Chang,

H.H.

Lin and

X.-Y.

Tsai, Implementing intelligent behavior tracking technique for elderly home care services, in: Consumer Electronics-Taiwan (ICCE-TW), 2015 IEEE International Conference on, IEEE, 2015, pp. 278–279. doi:10.1109/ICCE-TW.2015.7216897.

29.

Chen,

C.D.

Nugent and

Wang, A knowledge-driven approach to activity recognition in smart homes, IEEE Transactions on Knowledge and Data Engineering 24(6) (2012), 961–974. doi:10.1109/TKDE.2011.51.

30.

T.-C.T.

Chen, Advanced ambient intelligence system informatics, Journal of Ambient Intelligence and Humanized Computing 9(2) (2018), 211–213. doi:10.1007/s12652-017-0629-7.

31.

Chernbumroong,

Cang,

Atkins and

Yu, Elderly activities recognition and classification for applications in assisted living, Expert Systems with Applications 40(5) (2013), 1662–1674. doi:10.1016/j.eswa.2012.09.004.

32.

D.J.

Cook, Learning setting-generalized activity models for smart spaces, IEEE Intelligent Systems 27(1) (2012), 32–38. doi:10.1109/MIS.2010.112.

33.

D.J.

Cook and

Schmitter-Edgecombe, Assessing the quality of activities in a smart environment, Methods of Information in Medicine 48(5) (2009), 480. doi:10.3414/ME0592.

34.

Cuddihy,

Weisenberg,

Graichen and

Ganesh, Algorithm to automatically detect abnormally long periods of inactivity in a home, in: Proceedings of the 1st ACM SIGMOBILE International Workshop on Systems and Networking Support for Healthcare and Assisted Living Environments, ACM, 2007, pp. 89–94.

35.

P.S.

Curtiss, Neural network modeling and control applications in building mechanical systems, in: Proc. Int’l Conf. Chartered Inst. of Building Services Engineers and Am. Soc. Heating Refrigeration and Air-Conditioning Engineers, 2001.

36.

De,

Chatterjee and

Rakshit, Recognition of human behavior for assisted living using dictionary learning approach, IEEE Sensors Journal 18(6) (2017), 2434–2441. doi:10.1109/JSEN.2017.2787616.

37.

Delnevo,

Monti,

Foschini and

Santonastasi, On enhancing accessible smart buildings using IoT, in: Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual, IEEE, 2018, pp. 1–6.

38.

Dernbach,

Das,

N.C.

Krishnan,

B.L.

Thomas and

D.J.

Cook, Simple and complex activity recognition through smart phones, in: Intelligent Environments (IE), 2012 8th International Conference on, IEEE, 2012, pp. 214–221. doi:10.1109/IE.2012.39.

39.

A.K.

Dey,

G.D.

Abowd et al., The context toolkit: Aiding the development of context-aware applications, in: Workshop on Software Engineering for Wearable and Pervasive Computing, 2000, pp. 431–441.

40.

Diethe,

Twomey,

Kull,

Flach and

Craddock, Probabilistic sensor fusion for ambient assisted living, 2017, arXiv preprint, arXiv:1702.01209.

41.

Doctor,

Hagras and

Callaghan, A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 35(1) (2005), 55. doi:10.1109/TSMCA.2004.838488.

42.

M.-A.

Dragan and

Mocanu, Human activity recognition in smart environments, in: Control Systems and Computer Science (CSCS), 2013 19th International Conference on, IEEE, 2013, pp. 495–502. doi:10.1109/CSCS.2013.78.

43.

Droghini,

Principi,

Squartini,

Olivetti and

Piazza, Human fall detection by using an innovative floor acoustic sensor, in: Multidisciplinary Approaches to Neural Computing, Springer, 2018, pp. 97–107. doi:10.1007/978-3-319-56904-8_10.

44.

T.V.

Duong,

H.H.

Bui,

D.Q.

Phung and

Venkatesh, Activity recognition and abnormality detection with the switching hidden semi-Markov model, in: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, IEEE, 2005, pp. 838–845.

45.

Espinilla,

Medina,

Hallberg and

Nugent, A new approach based on temporal sub-windows for online sensor-based activity recognition, Journal of Ambient Intelligence and Humanized Computing (2018), 1–13.

46.

Esposito,

De Santis,

Tortora,

Chang and

K.-K.R.

Choo, Blockchain: A panacea for healthcare cloud-based data security and privacy?, IEEE Cloud Computing 5(1) (2018), 31–37. doi:10.1109/MCC.2018.011791712.

47.

L.G.

Fahad and

Rajarajan, Anomalies detection in smart-home activities, in: Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on, IEEE, 2015, pp. 419–422.

48.

L.G.

Fahad,

S.F.

Tahir and

Rajarajan, Activity recognition in smart homes using clustering based classification, in: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE, 2014, pp. 1348–1353.

49.

L.G.

Fahad,

S.F.

Tahir and

Rajarajan, Feature selection and data balancing for activity recognition in smart homes, in: Communications (ICC), 2015 IEEE International Conference on, IEEE, 2015, pp. 512–517.

50.

Ferilli and

Esposito, A logic framework for incremental learning of process models, Fundamenta Informaticae 128(4) (2013), 413–443.

51.

A.R.M.

Forkan,

Khalil,

Tari,

Foufou and

Bouras, A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living, Pattern Recognition 48(3) (2015), 628–641. doi:10.1016/j.patcog.2014.07.007.

52.

Fuchsberger, Ambient assisted living: Elderly people’s needs and how to face them, in: Proceedings of the 1st ACM International Workshop on Semantic Ambient Media Experiences, ACM, 2008, pp. 21–24. doi:10.1145/1461912.1461917.

53.

Garcia-Ceja and

R.F.

Brena, An improved three-stage classifier for activity recognition, International Journal of Pattern Recognition and Artificial Intelligence 32(01) (2018), 1860003. doi:10.1142/S0218001418600030.

54.

K.S.

Gayathri,

Elias and

K.S.

Easwarakumar, Assistive dementia care system through smart home, in: Proceedings of First International Conference on Smart System, Innovations and Computing, Springer, 2018, pp. 455–467. doi:10.1007/978-981-10-5828-8_43.

55.

Giannella,

Han,

Pei,

Yan and

P.S.

Yu, Mining frequent patterns in data streams at multiple time granularities, Next Generation Data Mining 212 (2003), 191–212.

56.

Global health and aging report, world health organization (who), accessed: 2018-09-25, 2018.

57.

Gu,

Wang,

Wu,

Tao and

Lu, A pattern mining approach to sensor-based human activity recognition, IEEE Transactions on Knowledge and Data Engineering 23(9) (2011), 1359–1372. doi:10.1109/TKDE.2010.184.

58.

Gu,

Wu,

Tao,

H.K.

Pung and

Lu, Epsicar: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition, in: Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on, IEEE, 2009, pp. 1–9.

59.

Guralnik and

K.Z.

Haigh, Learning models of human behaviour with sequential patterns, in: Proceedings of the AAAI-02 Workshop Automation as Caregiver, 2002, pp. 24–30.

60.

Hagras, Embedding computational intelligence in pervasive spaces, IEEE Pervasive Computing 6(3) (2007). doi:10.1109/MPRV.2007.54.

61.

Helaoui,

Niepert and

Stuckenschmidt, Recognizing interleaved and concurrent activities: A statistical-relational approach, in: Pervasive Computing and Communications (PerCom), 2011 IEEE International Conference on, IEEE, 2011, pp. 1–9.

62.

Helaoui,

Riboni,

Niepert,

Bettini and

Stuckenschmidt, Activity Context Representation: Techniques and Languages AAAI Technical Report WS-12-05, 12:05, 2012.

63.

Helaoui,

Riboni and

Stuckenschmidt, A probabilistic ontological framework for the recognition of multilevel human activities, in: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2013, pp. 345–354.

64.

Heras,

Palanca and

C.I.

Chesñevar, Argumentation-based personal assistants for ambient assisted living, in: Personal Assistants: Emerging Computational Technologies, Springer, 2018, pp. 19–36. doi:10.1007/978-3-319-62530-0_2.

65.

Y.-J.

Hong,

I.-J.

Kim,

S.C.

Ahn and

H.-G.

Kim, Mobile health monitoring system based on activity recognition using accelerometer, Simulation Modelling Practice and Theory 18(4) (2010), 446–455. doi:10.1016/j.simpat.2009.09.002.

66.

H.-H.

Hsu and

C.-C.

Chen, Rfid-based human behavior modeling and anomaly detection for elderly care, Mobile Information Systems 6(4) (2010), 341–354. doi:10.1155/2010/460103.

67.

P.-C.

Huang,

S.-S.

Lee,

Y.-H.

Kuo and

K.-R.

Lee, A flexible sequence alignment approach on pattern mining and matching for human activity recognition, Expert Systems with Applications 37(1) (2010), 298–306. doi:10.1016/j.eswa.2009.05.057.

68.

Jakkula,

D.J.

Cook et al., Anomaly detection using temporal data mining in a smart home environment, Methods of Information in Medicine 47(1) (2008), 70–75. doi:10.3414/ME9103.

69.

A.J.

Jara,

M.A.

Zamora and

A.F.

Skarmeta, An Internet of things-based personal device for diabetes therapy management in ambient assisted living (AAL), Personal and Ubiquitous Computing 15(4) (2011), 431–440. doi:10.1007/s00779-010-0353-1.

70.

Jaschinski and

S.B.

Allouch, An extended view on benefits and barriers of ambient assisted living solutions, Int. J. Adv. Life Sci. 7(2) (2015), 40–53.

71.

Kaluza and

Gams, An approach to analysis of daily living dynamics, in: Proceedings of the World Congress on Engineering and Computer Science, Vol. 1, 2010, pp. 485–490.

72.

Kanagarajan and

Ramakrishnan, Ubiquitous and ambient intelligence assisted learning environment infrastructures development – a review, Education and Information Technologies 23(1) (2018), 569–598. doi:10.1007/s10639-017-9618-x.

73.

Kikhia,

T.G.

Stavropoulos,

Meditskos,

Kompatsiaris,

Hallberg,

Sävenstedt and

Melander, Utilizing ambient and wearable sensors to monitor sleep and stress for people with BPSD in nursing homes, Journal of Ambient Intelligence and Humanized Computing 9(2) (2018), 261–273. doi:10.1007/s12652-015-0331-6.

74.

Kim,

Helal and

Cook, Human activity recognition and pattern discovery, IEEE Pervasive Computing/IEEE Computer Society [and] IEEE Communications Society 9(1) (2010), 48. doi:10.1109/MPRV.2010.7.

75.

Kivimaki, Technologies for ambient assisted living: Ambient communication and indoor positioning, Publication 1295, Tampere University of Technology, 2015.

76.

Kleinberger,

Becker,

Ras,

Holzinger and

Müller, Ambient intelligence in assisted living: Enable elderly people to handle future interfaces, in: International Conference on Universal Access in Human–Computer Interaction, Springer, 2007, pp. 103–112.

77.

C.R.

Kothari, Research Methodology: Methods and Techniques, New Age International, 2004.

78.

La Tona,

Petitti,

Lorusso,

Colella,

Milella and

Attolico, Modular multimodal user interface for distributed ambient intelligence architectures, Internet Technology Letters 1(2) (2018), e23. doi:10.1002/itl2.23.

79.

Li,

Zhang and

Duan, A novel one-pass neural network approach for activities recognition in intelligent environments, in: Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, IEEE, 2008, pp. 50–54.

80.

Liming and

Nugent, Ontology-based activity recognition in intelligent pervasive environments, International Journal of Web Information Systems 5(4) (2009), 410–430. doi:10.1108/17440080911006199.

81.

Liu,

Nie,

Liu and

D.S.

Rosenblum, From action to activity: Sensor-based activity recognition, Neurocomputing 181 (2016), 108–115. doi:10.1016/j.neucom.2015.08.096.

82.

Lopez-Iturri,

Aguirre,

J.D.

Trigo,

J.J.

Astrain,

Azpilicueta,

Serrano,

Villadangos and

Falcone, Implementation and operational analysis of an interactive intensive care unit within a smart health context, Sensors 18(2) (2018), 389. doi:10.3390/s18020389.

83.

Lotfi,

Langensiepen,

S.M.

Mahmoud and

M.J.

Akhlaghinia, Smart homes for the elderly dementia sufferers: Identification and prediction of abnormal behaviour, Journal of Ambient Intelligence and Humanized Computing 3(3) (2012), 205–218. doi:10.1007/s12652-010-0043-x.

84.

Luhr,

Venkatesh,

West and

H.H.

Bui, Duration Abnormality Detection in Sequences of Human Activity, Curtin University of Technology, Perth, 2004.

85.

Lühr,

West and

Venkatesh, Recognition of emergent human behaviour in a smart home: A data mining approach, Pervasive and Mobile Computing 3(2) (2007), 95–116. doi:10.1016/j.pmcj.2006.08.002.

86.

Mahmoud,

Lotfi and

Langensiepen, Behavioural pattern identification and prediction in intelligent environments, Applied Soft Computing 13(4) (2013), 1813–1822. doi:10.1016/j.asoc.2012.12.012.

87.

Makhlouf,

Boudouane,

Saadia and

A.R.

Cherif, Ambient assistance service for fall and heart problem detection, Journal of Ambient Intelligence and Humanized Computing (2018), 1–20.

88.

K.F.

Mares,

Baltazar,

M.Á.

Casillas,

Zamudio and

Lemus, A proposal to classify ways of walking patterns using spiking neural networks, in: Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, Springer, 2018, pp. 89–98.

89.

F.E.

Martínez-Pérez,

J.Á.

González-Fraga,

J.C.

Cuevas-Tello and

M.D.

Rodríguez, Activity inference for ambient intelligence through handling artifacts in a healthcare environment, Sensors 12(1) (2012), 1072–1099. doi:10.3390/s120101072.

90.

H.C.

Mayr,

Al Machot,

Michael,

Morak,

Ranasinghe,

Shekhovtsov and

Steinberger, Hcm-l: Domain-specific modeling for active and assisted living, in: Domain-Specific Conceptual Modeling, Springer, 2016, pp. 527–552. doi:10.1007/978-3-319-39417-6_24.

91.

Medjahed,

Istrate,

Boudy and

Dorizzi, A Fuzzy Logic System for Home Elderly People Monitoring (Emutem), ACM, 2009.

92.

Memon,

S.R.

Wagner,

C.F.

Pedersen,

F.H.A.

Beevi and

F.O.

Hansen, Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes, Sensors 14(3) (2014), 4312–4341. doi:10.3390/s140304312.

93.

Mihovska and

Sarkar, Smart connectivity for Internet of things (IoT) applications, in: New Advances in the Internet of Things, Springer, 2018, pp. 105–118. doi:10.1007/978-3-319-58190-3_7.

94.

D.N.

Monekosso and

Remagnino, Anomalous behavior detection: Supporting independent living, in: Intelligent Environments, 2009, pp. 33–48. doi:10.1007/978-1-84800-346-0_3.

95.

Mori,

Ishino,

Noguchi,

Shimosaka and

Sato, Anomaly detection and life pattern estimation for the elderly based on categorization of accumulated data, in: AIP Conference Proceedings, Vol. 1371, AIP, 2011, pp. 297–306.

96.

Mshali,

Lemlouma and

Magoni, Adaptive monitoring system for e-health smart homes, Pervasive and Mobile Computing 43 (2018), 1–19. doi:10.1016/j.pmcj.2017.11.001.

97.

Nazerfard and

D.J.

Cook, Crafft: An activity prediction model based on Bayesian networks, Journal of Ambient Intelligence and Humanized Computing 6(2) (2015), 193–205. doi:10.1007/s12652-014-0219-x.

98.

Nazerfard,

Rashidi and

D.J.

Cook, Discovering temporal features and relations of activity patterns, in: Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, IEEE, 2010, pp. 1069–1075.

99.

Nisar,

Asri and

Ibrahim, A smart home model using Android application, in: International Conference on Kansei Engineering & Emotion Research, Springer, 2018, pp. 3–10.

100.

M.H.M.

Noor,

Salcic,

Kevin and

Wang, Ontology-based sensor fusion activity recognition, Journal of Ambient Intelligence and Humanized Computing (2018), 1–15.

101.

C.L.

Oguego,

J.C.

Augusto,

Muñoz and

Springett, Using argumentation to manage users’ preferences, Future Generation Computer Systems 81 (2018), 235–243. doi:10.1016/j.future.2017.09.040.

102.

R.N.

Oliveira,

Roth,

A.F.

Henzen,

J.M.

Simao,

Nohama and

E.C.G.

Wille, Notification oriented paradigm applied to ambient assisted living tool, IEEE Latin America Transactions 16(2) (2018), 647–653. doi:10.1109/TLA.2018.8327425.

103.

F.J.

Ordóñez and

Roggen, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors 16(1) (2016), 115. doi:10.3390/s16010115.

104.

Palanca,

del Val,

Garcia-Fornes,

Billhardt,

J.M.

Corchado and

Julián, Designing a goal-oriented smart-home environment, Information Systems Frontiers 20(1) (2018), 125–142. doi:10.1007/s10796-016-9670-x.

105.

Palumbo, Ambient intelligence in assisted living environments, PhD thesis, Universita Degli Studi di Pisa, 2016.

106.

Philipose,

K.P.

Fishkin,

Perkowitz,

D.J.

Patterson,

Fox,

Kautz and

Hahnel, Inferring activities from interactions with objects, IEEE Pervasive Computing 3(4) (2004), 50–57. doi:10.1109/MPRV.2004.7.

107.

Rafferty,

C.D.

Nugent,

Liu and

Chen, From activity recognition to intention recognition for assisted living within smart homes, IEEE Transactions on Human–Machine Systems 47(3) (2017), 368–379. doi:10.1109/THMS.2016.2641388.

108.

Rashidi and

Cook, An adaptive sensor mining model for pervasive computing applications, in: Knowledge Discovery from Sensor Data, (Sensor-KDD 2008). 2nd International Workshop on, 2004.

109.

Rashidi and

D.J.

Cook, Keeping the resident in the loop: Adapting the smart home to the user, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 39(5) (2009), 949–959. doi:10.1109/TSMCA.2009.2025137.

110.

Rashidi and

D.J.

Cook, Mining sensor streams for discovering human activity patterns over time, in: 2010 IEEE International Conference on Data Mining, IEEE, 2010, pp. 431–440. doi:10.1109/ICDM.2010.40.

111.

Rashidi,

D.J.

Cook,

L.B.

Holder and

Schmitter-Edgecombe, Discovering activities to recognize and track in a smart environment, IEEE Transactions on Knowledge and Data Engineering 23(4) (2011), 527–539. doi:10.1109/TKDE.2010.148.

112.

Rashidi and

Mihailidis, A survey on ambient-assisted living tools for older adults, IEEE Journal of Biomedical and Health Informatics 17(3) (2013), 579–590. doi:10.1109/JBHI.2012.2234129.

113.

Rivera-Illingworth,

Callaghan and

Hagras, Towards the detection of temporal behavioural patterns in intelligent environments, in: 2nd IET International Conference on Intelligent Environments (IE 06), Vol. 2006, IET, 2006, pp. 119–125.

114.

Rivera-Illingworth,

Callaghan and

Hagras, Detection of normal and novel behaviours in ubiquitous domestic environments, The Computer Journal 53(2) (2007), 142–151. doi:10.1093/comjnl/bxm078.

115.

Rudary,

Singh and

M.E.

Pollack, Adaptive cognitive orthotics: Combining reinforcement learning and constraint-based temporal reasoning, in: Proceedings of the Twenty-First International Conference on Machine Learning, ACM, 2004, p. 91.

116.

Sadek and

Mohktari, Nonintrusive remote monitoring of sleep in home-based situation, Journal of Medical Systems 42(4) (2018), 64. doi:10.1007/s10916-018-0917-6.

117.

Sahni,

Cao and

Shen, Challenges and opportunities in designing smart spaces, in: Internet of Everything, Springer, 2018, pp. 131–152. doi:10.1007/978-981-10-5861-5_6.

118.

Schneider and

Becker, Runtime models for self-adaptation in the ambient assisted living domain, Technical Report COMP COMP-005-2008, Lancaster University, 2008.

119.

J.H.

Shah and

A.D.

Patel, Ambient assisted living system: The scope of research and development, in: International Conference on EECCMC, Manuscript accepted, 2018.

120.

C.A.

Siebra,

B.A.

Sá,

T.B.

Gouveia,

F.Q.

Silva and

A.L.M.

Santos, A neural network based application for remote monitoring of human behaviour, in: Computer Vision and Image Analysis Applications (ICCVIA), 2015 International Conference on, IEEE, 2015, pp. 1–6.

121.

Singh,

Merdivan,

Psychoula,

Kropf,

Hanke,

Geist and

Holzinger, Human activity recognition using recurrent neural networks, in: International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer, 2017, pp. 267–274.

122.

Soulas, Activity monitoring through home automation devices, PhD thesis, Télécom Bretagne; Université de Bretagne Occidentale, 2016.

123.

Stefan,

C.L.

Aldea and

C.S.

Nechifor, Web platform architecture for ambient assisted living, Journal of Ambient Intelligence and Smart Environments 10(1) (2018), 35–47. doi:10.3233/AIS-170470.

124.

N.K.

Suryadevara and

S.C.

Mukhopadhyay, Determining wellness through an ambient assisted living environment, IEEE Intelligent Systems 29(3) (2014), 30–37. doi:10.1109/MIS.2014.16.

125.

E.M.

Tapia,

S.S.

Intille and

Larson, Activity recognition in the home using simple and ubiquitous sensors, in: International Conference on Pervasive Computing, Springer, 2004, pp. 158–175.

126.

Twomey,

Diethe,

Kull,

Song,

Camplani,

Hannuna,

Fafoutis,

Zhu,

Woznowski,

Flach et al., The sphere challenge: Activity recognition with multimodal sensor data, 2016, arXiv preprint arXiv:1603.00797.

127.

United nation’s world population prospectus, accessed: 2018-01-05, 2018.

128.

van de Garde-Perik, Ambient intelligence & personalization: People’s perspectives on information privacy, PhD thesis, Eindhoven University of Technology, 2008.

129.

van Kasteren and

Krose, Bayesian activity recognition in residence for elders, in: 3rd IET International Conference on Intelligent Environments (IE 07), IET, 2007, pp. 209–212.

130.

Van Kasteren,

Noulas,

Englebienne and

Kröse, Accurate activity recognition in a home setting, in: Proceedings of the 10th International Conference on Ubiquitous Computing, ACM, 2008, pp. 1–9.

131.

Vincze,

Bajones,

Suchi,

Wolf,

Lammer,

Weiss and

Fischinger, User experience results of setting free a service robot for older adults at home, in: Service Robots, InTech, 2018.

132.

Wyatt,

Philipose and

Choudhury, Unsupervised activity recognition using automatically mined common sense, in: AAAI, Vol. 5, 2005, pp. 21–27.

133.

G.M.

Youngblood,

D.J.

Cook and

L.B.

Holder, Managing adaptive versatile environments, Pervasive and Mobile Computing 1(4) (2005), 373–403. doi:10.1016/j.pmcj.2005.08.004.

134.

Zhang,

S.I.

McClean and

B.W.

Scotney, Probabilistic learning from incomplete data for recognition of activities of daily living in smart homes, IEEE Transactions on Information Technology in Biomedicine 16(3) (2012), 454–462. doi:10.1109/TITB.2012.2188534.

135.

Zhu and

Sheng, Motion-and location-based online human daily activity recognition, Pervasive and Mobile Computing 7(2) (2011), 256–269. doi:10.1016/j.pmcj.2010.11.004.

136.

Zouba,

Bremond and

Thonnat, An activity monitoring system for real elderly at home: Validation study, in: Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on, IEEE, 2010, pp. 278–285. doi:10.1109/AVSS.2010.83.

Sensor-based activity recognition in the context of ambient assisted living systems: A review

Abstract

Keywords

Get full access to this article

References