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Application on Pervasive Computing in Healthcare – A Review

Affiliations

  • Calcutta Institute of Engineering and Management, Kolkata–700040 , West Bengal, India
  • National Institute of Technology, Patna–800005, Bihar, India

Abstract


Background/Objectives: Application of pervasive computation in healthcare is an interdisciplinary research domain for both the medical and computer domains. Such systems provide support to remote patients and to disaster affected people. Methods/Statistical Analysis: This paper calculates percentage of contribution of various methodologies which have been described in this whole paper. Along with this it has presented comparative analysis of the surveyed algorithms based on their important features. Findings: The literature studies in this field are found to concentrate on specific applications of pervasive healthcare, such as remote patient monitoring, fall detection, etc. In this paper, we exhibit a descriptive study of different features of pervasive healthcare in recent years. Application/Improvements: The pervasive healthcare has proved to be much useful in case of elderly people living alone or patients undergoing post-operative recovery phase. Finally, a comparative analysis table of the respective techniques has been presented.

Keywords

Access Control, Classification, Clustering, Daily Activities, Decision Making, Healthcare, Pervasive, Prediction, Remote Patient Monitoring.

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References


  • Luhr S, West G, Venkatesh S. Recognition of emergent human behaviour in a smart home: A data mining approach. Pervasive and Mobile Computing. 2007; 3(2):95–116.
  • Giri S, Berges M, Rowe A. Towards automated appliance recognition using an EMF sensor in NILM platforms. Advanced Engineering Informatics. 2013; 27(4):477–85.
  • Orwat C, Andreas G, Timm F. Towards pervasive computing in health care – A literature review. BMC Medical Informatics and Decision Making. 2008; 8: 26.
  • Oguz K, Bayraktar C, Gumuşkaya H, Karlik B. Diagnosing diabetes using neural networks on small mobile devices. Expert Systems with Applications. 2012; 39(1):54–60.
  • Palaniappan S, Awang R. Intelligent heart disease prediction system using data mining techniques. IEEE/ACS International Conference on Computer Systems and Applications. AICCSA 2008; 2008.
  • Atallah L, Lo B, Ali R, King R, Yang GZ. Real-time activity classification using ambient and wearable sensors. IEEE Transactions on Information Technology in Biomedicine. 2009; 13(6):1031–9.
  • Tapia EM, Intille SS, Haskell W, Larson K, Wright J, King A, Friedman R. Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. 11th IEEE International Symposium on Wearable Computers; 2007. p. 37–40.
  • Plotz T, Hammerla NY, Olivier P. Feature learning for activity recognition in ubiquitous computing. IJCAI Proceedings - International Joint Conference on Artificial Intelligence. 2011 Jul; 22(1):1729.
  • Chernbumroong S, Cang S, Atkins A, Yu H. Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications. 2013; 40(5):1662–74.
  • Miikka E, et al. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Transactions on Information Technology in Biomedicine. 2008; 12(1):20–6.
  • Parkka J, Ermes M, Korpipaa P, Mantyjarvi J, Peltola J. Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine. 2006 Jan; 10(1):119–28.
  • Zhao Z, Chen Y, Wang S, Chen Z. FallAlarm: Smart phone based fall detecting and positioning system. Procedia Computer Science. 2012; 10:617–24.
  • Pansiot J, Stoyanov D, McIlwraith D, Lo BP, Yang GZ. Ambient and wearable sensor fusion for activity recognition in healthcare monitoring systems. 4th International Workshop on Wearable and Implantable Body Sensor Networks; Heidelberg, Springer Berlin. 2007. P. 208–12.
  • Jeen Y, Park J, Park P. Design and implementation of the smart healthcare frame based on pervasive computing technology. The 9th International Conference on Advanced Communication Technology. 2007; 1:349–52.
  • Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks. 2008; 21(2):427–36.
  • Yuan B, Herbert J. Context-aware hybrid reasoning framework for pervasive healthcare. Personal and Ubiquitous Computing. 2014; 18(4):865–81.
  • Yuan B, Herbert J. Fuzzy cara - A fuzzy-based context reasoning system for pervasive healthcare. Procedia Computer Science. 2012; 10:357–65.
  • Mazilu S, Hardegger M., Zhu Z, Roggen D, Troster G, Plotnik M, Hausdorff JM. Online detection of freezing of gait with smartphones and machine learning techniques. 6th International Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health) and Workshops; 2012. p. 123–30.
  • Ravi N, Dandekar N, Mysore P, Littman ML. Activity recognition from accelerometer data. AAAI. 2005; 5:1541–6.
  • Medjahed H, Istrate D, Boudy J, Baldinger JL, Dorizzi B. A pervasive multi-sensor data fusion for smart home healthcare monitoring. 2011 IEEE International Conference on Fuzzy Systems (FUZZ); 2011. p. 1466–73.
  • Lo B, Atallah L, Aziz O, El ElHew M, Darzi A, Yang GZ. Real-time pervasive monitoring for postoperative care. 4th international workshop on wearable and implantable body sensor networks (BSN 2007); Springer Berlin, Heidelberg. 2007. p. 122–7.
  • Diane CJ. Learning setting-generalized activity models for smart spaces. IEEE Intelligent System. 2012; 27(1):32–8.
  • Ling B, Stephen SI. Activity recognition from user-annotated acceleration data. A. Ferscha and F. Mattern, editors. Pervasive 2004, LNCS 3001; 2004. p. 1–17.
  • Kim E, Helal S, Cook D. Human activity recognition and pattern discovery. Pervasive Computing. IEEE. 2010; 9(1):48–53.
  • Subbalakshmi G, Ramesh K, Chinna Rao M. Decision support in heart disease prediction system using Naive Bayes. IJCSE. 2011 Apr-May; 2(2):170–6.
  • Edward SS. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE Sensor Journal.2012 May; 12(5):1240–8.
  • Sun Y, Kamel MS, Wong AK, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition. 2007; 40(12):3358–78.
  • Dadashi F, Millet GP, Aminian K. A Bayesian approach for pervasive estimation of breaststroke velocity using a wearable IMU. Unpublished. .Pervasive and Mobile Computing. Elsevier; 2014,
  • Bachlin M, Plotnik M, Roggen D, Maidan I, Hausdorff JM, Giladi N, Troster G. Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Transactions on Information Technology in Biomedicine. 2010; 14(2):436–46.
  • ElSayed, M, Alsebai A, Salaheldin A, El Gayar N, ElHelw M. Ambient and wearable sensing for gait classification in pervasive healthcare environments. 2010 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom); 2010. p. 240–5.
  • Hauptmann AG, Gao J, Yan R, Qi Y, Yang J, Wactlar HD. Automated analysis of nursing home observations. IEEE Pervasive Computing. 2004; 3(2): 15–21.
  • Atallah L, ElHelw M, Pansiot J, Stoyanov D, Wang L, Lo B, Yang GH. Behaviour profiling with ambient and wearable sensing. 4th International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2007); Springer Berlin, Heidelberg. 2007. p. 133–8.
  • Enamul H, Stankovic J. AALO: Activity recognition in smart homes using active learning in the presence of overlapped activities. IEEE 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth); 2012. p. 139–46.
  • Lapalu J, Bouchard K, Bouzouane A, Bouchard B, Giroux B. Unsupervised mining of activities for smart home prediction. ANT-2013, SEIT-2013. Procedia Computer Science. 2013; 19:503–10.
  • Stone EE, Skubic M. Passive, in-home gait measurement using an inexpensive depth camera: Initial results. IEEE 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth); 2012. p. 183–6.
  • Yao L, Lin C, Kong X, Xia F, Wu G. A clustering-based location privacy protection scheme for pervasive computing. Proceedings of the ACM International Conference on Green Computing and Communications and Int’l Conference on Cyber, Physical and Social Computing. IEEE Computer Society; 2010. p. 719–26.
  • Wyatt D, Philipose M, Choudhury T. Unsupervised activity recognition using automatically mined common sense. Proceedings of 20th National Conference of Artificial Intelligence, AAAI. 2005; 5:21–7.
  • Ye N, Wang R. A sensor network-based data stream clustering algorithm for pervasive computing. Chinese Journal of Electronics. 2009; 18(2):255–8.
  • Sigg S, Haseloff S, David K. A novel approach to context prediction in ubicomp environments. IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications; 2006. p. 1–5.
  • Aljumah AA, Ahamad MG, Siddiqui MK. Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University-Computer and Information Sciences. 2013; 25(2):127–36.
  • Agarwal S. Weighted support vector regression approach for remote healthcare monitoring. 2011 IEEE International Conference on Recent Trends in Information Technology (ICRTIT); 2011. p. 969–74.
  • Bastiani E, Librelotto GR, Freitas LO, Pereira R, Brasil MB. An approach for pervasive homecare environments focused on care of patients with dementia. Procedia Technology. 2013; 9:921–9.
  • Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M. SWRL: A Semantic Web Rule Language Combining OWL and RuleML. DARPA DAML Program; 2004.
  • Freitas LO, Pereira RT, Pereira HG, Martini RG, Mozzaquatro B, Kasper J, Librelotto GR. A methodology for an architecture of pervasive systems to homecare environments. Procedia Technology. 2012; 5:820–9.
  • Chen H, Finin T, Joshi A. An ontology for context-aware pervasive computing environments. The Knowledge Engineering Review. 2003; 18(03):197–207.
  • Chen L, Nugent C. Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems. 2009; 5(4):410–30.
  • Rodriguez DN, Cuellar MP, Lilius J, Delgado Calvo-Flores M. A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowledge-Based Systems. 2014; 66: 46–60.
  • Roy N, Gu T, Das SK. Supporting pervasive computing applications with active context fusion and semantic context delivery. Pervasive and Mobile Computing. 2010; 6(1):21–42.
  • Henricksen K, Livingstone S, Indulska J. Towards a hybrid approach to context modelling, reasoning and interoperation. Proceedings of the First International Workshop on Advanced Context Modelling, Reasoning and Management, in conjunction with UbiComp; 2004. p. 54–61.
  • Garg N, Lather JS, Dhurandher SK. Smart applications of context services using automatic adaptive module and making users profiles. Procedia Technology. 2012; 6:324–33.
  • Choi JH, Jang H, Eom YI. CA-RBAC: Context aware RBAC scheme in ubiquitous computing environments. Journal of Information Science and Engineering. 2010; 26(5):1801–16.
  • Kulkarni D, Tripathi A. Context-aware role-based access control in pervasive computing systems. IProceedings of the 13th ACM Symposium on Access Control Models and Technologies; 2008. p. 113–22.
  • Emami SS, Amini M, Zokaei S. A context-aware access control model for pervasive computing environments. IEEE International Conference on Intelligent Pervasive Computing, IPC 2007; 2007. p. 51–6.
  • Toninelli A, Montanari R, Kagal L, Lassila O. A semantic context-aware access control framework for secure collaborations in pervasive computing environments. The Semantic Web-ISWC 2006; Springer Berlin Heidelberg. 2006. p. 473–86.
  • Yao L, Kong X, Wu G, Fan Q, Lin C. A privacy-preserving authentication scheme using biometrics for pervasive computing environments. Journal of Electronics (China). 2010; 27(1):68–78.
  • Cherukuri S, Venkatasubramanian KK, Gupta SK. BioSec: A biometric based approach for securing communication in wireless networks of biosensors implanted in the human body. Proceedings of IEEE International Conference on Parallel Processing Workshops; 2003; p. 432–9.
  • Popel DV, Popel EI. BIOGLYPH: Biometric identification in pervasive environments. Proceedings of the Seventh IEEE International Symposium on Multimedia (ISM’05); 2005. p. 713–8.
  • Doukas C, Maglogiannis I. Bringing IoT and cloud computing towards pervasive healthcare. Sixth IEEE International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS); 2012. p. 922–6.
  • Cubo J, Nieto A, Pimentel E. A cloud-based Internet of Things platform for ambient assisted living. Article in Sensors. 2014; 14:14070–105. DOI: 10.3390/s140814070, 2014.
  • Hashizume K, Rosado DG, Fernandez-Medina E, Fernandez EB. An analysis of security issues for cloud computing. Journal of Internet Services and Applications. 2013. Available from: http://www.jisajournal.com/content/4/1/5

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