Abstract
Air pollutants and allergens are the main stimuli that have considerable effects on asthmatic patients’ health. Seamless monitoring of patients’ conditions and the surrounding environment, limiting their exposure to allergens and irritants, and reducing the exacerbation of symptoms can aid patients to deal with asthma better. In this context, ubiquitous healthcare monitoring systems can provide any service to any user everywhere and every time through any device and network. In this regard, this research established a GIS-based outdoor asthma monitoring framework in light of ubiquitous systems. The proposed multifaceted model was designed in three layers: (1) pre-processing, for cleaning and interpolating data, (2) reasoning, for deducing knowledge and extract contextual information from data, and (3) prediction, for estimating the asthmatic conditions of patients ubiquitously. The effectiveness of the proposed model is assessed by applying it on a real dataset that comprised of internal context information including patients’ personal information (age, gender, height, medical history), patients’ locations, and their peak expiratory flow (PEF) values, as well as external context information including air pollutant data (O3, SO2, NO2, CO, PM10), meteorological data (temperature, pressure, humidity), and geographic information related to the city of Tehran, Iran. With more than 92% and 93% accuracies in reasoning and estimation mechanism, respectively, the proposed method showed remarkably effective in asthma monitoring and management.
Similar content being viewed by others
References
Afify HA (2011) Evaluation of change detection techniques for monitoring land-cover changes: a case study in new Burg El-Arab area. Alexandria Engineering Journal 50:187–195. https://doi.org/10.1016/j.aej.2011.06.001
Akdis C, Agache I (2013) Global Atlas of Asthma/European Academy of Allergy and Clinical Immunology http://www.eaaci.org/GlobalAtlas/Global_Atlas_of_Asthma.pdf. Accessed 10 Oct 2018
Al-Qerem WA, Ling J, Pullen R, McGarry K (2016) Reported prevalence of allergy and asthma in children from urban and rural Egypt. Air Quality, Atmosphere & Health 9:613–620. https://doi.org/10.1007/s11869-015-0372-1
Ali FM, Lee SW, Bien Z, Mokhtari M (2008) Combined fuzzy state Q-learning algorithm to predict context aware user activity under uncertainty in assistive environment. In: Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 6–8 Aug. 2008. 57–62 https://doi.org/10.1109/SNPD.2008.13
Altuğ H, Gaga EO, Döğeroğlu T, Özden Ö, Örnektekin S, Brunekreef B, Meliefste K, Hoek G, van Doorn W (2013) Effects of air pollution on lung function and symptoms of asthma, rhinitis and eczema in primary school children. Environmental Science and Pollution Research 20:6455–6467. https://doi.org/10.1007/s11356-013-1674-1
Auger F, Hilairet M, Guerrero JM, Monmasson E, Orlowska-Kowalska T, Katsura S (2013) Industrial applications of the Kalman filter: a review. IEEE Transactions on Industrial Electronics 60:5458–5471. https://doi.org/10.1109/TIE.2012.2236994
Bedolla-Barajas M, Morales-Romero J, Robles-Figueroa M, Fregoso-Fregoso M (2013) Asthma in late adolescents of Western Mexico: prevalence and associated factors. Archivos de Bronconeumología (English Edition) 49:47–53. https://doi.org/10.1016/j.arbr.2012.09.010
Chen SY (2012) Kalman filter for robot vision: a survey. IEEE Transactions on Industrial Electronics 59:4409–4420. https://doi.org/10.1109/TIE.2011.2162714
Chu H-T, Huang C-C, Lian Z-H, Tsai JJP (2006)A ubiquitous warning system for asthma-inducement. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06), 5–7 June 2006 186–191 https://doi.org/10.1109/SUTC.2006.20
Cortes C, Vapnik V (1995) Support-vector networks. machine learning 20:273–297. https://doi.org/10.1023/a:1022627411411
Elgazzar K, Aboelfotoh M, Martin P, Hassanein HS (2012) Ubiquitous health monitoring using mobile web services. Procedia Computer Science 10:332–339. https://doi.org/10.1016/j.procs.2012.06.044
Ghaffari HR, Aval HE, Alahabadi A, Mokammel A, Khamirchi R, Yousefzadeh S, Ahmadi E, Rahmani-Sani A, Estaji M, Ghanbarnejad A, Gholizadeh A, Taghavi M, Miri M (2017) Asthma disease as cause of admission to hospitals due to exposure to ambient oxidants in Mashhad, Iran. Environmental Science and Pollution Research 24:27402–27408. https://doi.org/10.1007/s11356-017-0226-5
Ghozikali MG, Ansarin K, Naddafi K, Nodehi RN, Yaghmaeian K, Hassanvand MS, Yunesian M (2018) Prevalence of asthma and associated factors among male late adolescents in Tabriz, Iran. Environmental Science and Pollution Research 25:2184–2193. https://doi.org/10.1007/s11356-017-0553-6
Gregg I, Nunn AJ (1973) Peak expiratory flow in normal subjects. The British Medical Journal 3:282–284
Habibi R, Alesheikh A, Mohammadinia A, Sharif M (2017) An assessment of spatial pattern characterization of air pollution: a case study of CO and PM2. 5 in Tehran, Iran. ISPRS International Journal of Geo-Information 6:270
Intille SS (2004) A new research challenge: persuasive technology to motivate healthy aging. IEEE Transactions on Information Technology in Biomedicine 8:235–237. https://doi.org/10.1109/TITB.2004.835531
Ishihara JY, Terra MH, Campos JCT (2006) Robust Kalman filter for descriptor systems. IEEE Transactions on Automatic Control 51:1354–1354 doi:https://doi.org/10.1109/TAC.2006.878741
Kaffash-Charandabi N, Alesheikh A (2017) Context inference and prediction modeling in ubiquitous health GIS. Transactions in GIS 21:1098–1114 https://doi.org/10.1111/tgis.12263
Kalman RE (1960) A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82:35–45. https://doi.org/10.1115/1.3662552
Khasha R, Sepehri MM, Mahdaviani SA, Khatibi T (2018) Mobile GIS-based monitoring asthma attacks based on environmental factors. Journal of Cleaner Production 179:417–428. https://doi.org/10.1016/j.jclepro.2018.01.046
Krumm J (2010) Ubiquitous computing fundamentals. Chapman & Hall, CRC Press, Boca Raton
Laine M, Latva-Pukkila N, Kyrölä E (2014) Analysing time-varying trends in stratospheric ozone time series using the state space approach. Atmospheric Chemistry and Physics 14:9707–9725
Lee C-H, Chen JC-Y, Tseng VS (2011) A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring. Computer Methods and Programs in Biomedicine 101:44–61. https://doi.org/10.1016/j.cmpb.2010.04.016
Lo C-C, Chen C-H, Cheng D-Y, Kung H-Y (2011) Ubiquitous healthcare service system with context-awareness capability: design and implementation. Expert Systems with Applications 38:4416–4436. https://doi.org/10.1016/j.eswa.2010.09.111
Mahboub V, Ebrahimzadeh S, Saadatseresht M, Faramarzi M (2018) On robust constrained Kalman filter for dynamic errors-in-variables model. Survey Review:1–8. https://doi.org/10.1080/00396265.2018.1547863
Martinez FD (2007) Genes, environments, development and asthma: a reappraisal. European Respiratory Journal 29:179
Nathan RA, Sorkness CA, Kosinski M, Schatz M, Li JT, Marcus P, Murray JJ, Pendergraft TB (2004) Development of the asthma control test. Journal of Allergy and Clinical Immunology 113:59–65. https://doi.org/10.1016/j.jaci.2003.09.008
Park M, Luo S, Kwon J, Stock TH, Delclos G, Kim H, Yun-Chul H (2013) Effects of air pollution on asthma hospitalization rates in different age groups in metropolitan cities of Korea. Air Quality, Atmosphere & Health 6:543–551. https://doi.org/10.1007/s11869-013-0195-x
Pearson JK (2007) A comparative business site-location feasibility analysis using geographic information systems and the gravity model. Volume 9, Papers in resource analysis. 10 pp. Saint Mary’s University of Minnesota Central Services Press, Winona, MN, USA
Raad MW, Sheltami T, Shakshuki E (2015) Ubiquitous tele-health system for elderly patients with Alzheimer’s. Procedia Computer Science 52:685–689. https://doi.org/10.1016/j.procs.2015.05.075
Rohr AC, Habre R, Godbold J, Moshier E, Schachter N, Kattan M, Grunin A, Nath A, Coull B, Koutrakis P (2014) Asthma exacerbation is associated with particulate matter source factors in children in New York city. Air Quality, Atmosphere & Health 7:239–250. https://doi.org/10.1007/s11869-013-0230-y
Savenije OEM, Kerkhof M, Koppelman GH, Postma DS (2012) Predicting who will have asthma at school age among preschool children. Journal of Allergy and Clinical Immunology 130:325–331. https://doi.org/10.1016/j.jaci.2012.05.007
Schachter EN, Moshier E, Habre R, Rohr A, Godbold J, Nath A, Grunin A, Coull B, Koutrakis P, Kattan M (2016) Outdoor air pollution and health effects in urban children with moderate to severe asthma. Air Quality, Atmosphere & Health 9:251–263. https://doi.org/10.1007/s11869-015-0335-6
Sharif M, Alesheikh AA (2017) Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing 54:426–452. https://doi.org/10.1080/15481603.2017.1278644
Sharif M, Alesheikh AA (2018) Context-aware movement analytics: implications, taxonomy, and design framework. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8:e1233 https://doi.org/10.1002/widm.1233
Sharif M, Sadeghi-Niaraki A (2017) Ubiquitous sensor network simulation and emulation environments: a survey. Journal of Network and Computer Applications 93:150–181. https://doi.org/10.1016/j.jnca.2017.05.009
Sharker MH, Karimi HA (2014) Computing least air pollution exposure routes. International Journal of Geographical Information Science 28:343–362. https://doi.org/10.1080/13658816.2013.841317
Tashayo B, Alimohammadi A (2016) Modeling urban air pollution with optimized hierarchical fuzzy inference system. Environmental Science and Pollution Research 23:19417–19431. https://doi.org/10.1007/s11356-016-7059-5
Tashayo B, Alimohammadi A, Sharif M (2017) A hybrid fuzzy inference system based on dispersion model for quantitative environmental health impact assessment of urban transportation planning. Sustainability 9:134
Tofallis C (2015) A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society 66:1352–1362. https://doi.org/10.1057/jors.2014.103
Vapnik V (1963) Pattern recognition using generalized portrait method. Automation and remote control 24:774–780
Weiser M (1991) The Computer for the 21st Century. Scientific American 265:94–105
Weiser M (1993) Hot topics-ubiquitous computing. Computer 26:71–72. https://doi.org/10.1109/2.237456
WHO factsheet 206 (2010) www.who.int/mediacentre/factsheets/fs206/en. Accessed 20 April 2018
Wolpert DM, Ghahramani Z (2000) Computational principles of movement neuroscience. Nature Neuroscience 3:1212–1217. https://doi.org/10.1038/81497
Yuan B, Herbert J (2012) Fuzzy CARA - a fuzzy-based context reasoning system for pervasive healthcare. Procedia Computer Science 10:357–365. https://doi.org/10.1016/j.procs.2012.06.047
Yue P, Baumann P, Bugbee K, Jiang L (2015) Towards intelligent GIServices. Earth Science Informatics 8:463–481. https://doi.org/10.1007/s12145-015-0229-z
Yun T-J (2012) Using ubiquitous communication technology to improve pediatric asthma management. PhD Thesis, Georgia Institute of Technology, USA. https://smartech.gatech.edu/bitstream/handle/1853/44794/yun_tae-jung_201208_phd.pdf?sequence=1&isAllowed=y. Accessed 10 Oct 2018
Zainal Abidin E, Semple S, Rasdi I, Ismail SNS, Ayres JG (2014) The relationship between air pollution and asthma in Malaysian schoolchildren. Air Quality, Atmosphere & Health 7:421–432. https://doi.org/10.1007/s11869-014-0252-0
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Philippe Garrigues
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kaffash-Charandabi, N., Alesheikh, A.A. & Sharif, M. A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts. Environ Sci Pollut Res 26, 7525–7539 (2019). https://doi.org/10.1007/s11356-019-04185-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-019-04185-3