Review on application of location tracking to monitor people in health field against COVID-19

• Autorzy: Syamsudin H. , Mohammed M. N. , Aljibori H. S. S. , Al-Tamimi Adnan N. Jameel , Alfiras M. , Yusuf Eddy , Abdullah Oday I.

Identyfikatory

DOI

10.29354/diag/174144

• Języki publikacji: EN

Abstrakty

EN

Location tracking stands for technologies that physically identify and electronically register and monitor the tracking of individuals or objects. This technology is widely implemented in the health field for tracking clinicians and patient locations. The history of visited location data is used for many purposes and is mainly applied to monitor and assess the patient’s movement to provide helpful knowledge. This data is obtained using the Global positioning system (GPS), Bluetooth, and Radio-frequency identification (RFID) built into the device such as smartphones, smart watches, or wearable devices. In some Research, other technologies like Google Location History (GLH) provide the history of visited locations made by the Google Account. Location tracking can be an alternative and potentially help monitor and track Covid-19 infectee to prevent wider diffusion. This paper reviews location tracking applications in healthcare based on how the location data is obtained and analyzed. The application of location tracking was differentiated and reviewed based on the applied methods in acquiring knowledgeable data. Furthermore, the data analysis used to track location was also discussed to know what knowledge that obtained from the location history data. Getting the location and assessing the data for specific purposes was also highlighted.

Słowa kluczowe

EN

location tracking; GPS; RFID; WLAN; bluetooth; Covid-19

PL

WLAN; RFID; bluetooth; Covid-19; GPS

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 4
• Strony: art. no. 2023408
• Opis fizyczny: Bibliogr. 42 poz., rys., tab.

Twórcy

autor

Syamsudin H.

• Faculty of Information Sciences and Engineering, Jakarta Global University, Depok 16412, West Java, Indonesia

autor

Mohammed M. N.

• Mechanical Engineering Department, College of Engineering, Gulf University, Kingdom of Bahrain

• https://orcid.org/0000-0002-1472-8401

autor

Aljibori H. S. S.

• Al-Warith Center for Crowd Engineering and Management Research, University of Warith Al-Anbiyaa

• https://orcid.org/0000-0002-5815-8148

autor

Al-Tamimi Adnan N. Jameel

• College of Technical Engineering, Al-Farahidi University, 47024 Baghdad

• https://orcid.org/0009-0004-3078-9126

autor

Alfiras M.

• Electrical and Electronic Engineering Department, College of Engineering, Gulf University, Sanad, Kingdom of Bahrain

• https://orcid.org/0000-0001-9398-7737

autor

Yusuf Eddy

• Faculty of Pharmacy, Jakarta Global University, Depok 16412, West Java, Indonesia

• https://orcid.org/0000-0001-9604-0901

autor

Abdullah Oday I.

• Department of Energy Engineering, College of Engineering, University of Baghdad, 47024 Baghdad, Iraq
• Mechanical Engineering Department, College of Engineering, Gulf University, Kingdom of Bahrain

• https://orcid.org/0000-0001-5450-021X

Bibliografia

• 1. Gholamhosseini L, Sadoughi F, Safaei A. Hospital real-time location system (A practical approach in healthcare): A narrative review article. Iranian Journal of Public Health 2019; 48(4): 593-602. https://doi.org/10.18502/ijph.v48i4.980.
• 2. Mohammed MN, Hazairin NA, Syamsudin H, AlZubaid S, A.K S, Mustapha S, i in. 2019 NOVEL CORONAVIRUS DISEASE (COVID-19): DETECTION AND DIAGNOSIS SYSTEM USING IOT BASED SAMART GLASSES. International Journal of Advanced Science and Technology 2020.
• 3. Hardy J, Veinot TC, Yan X, Berrocal VJ, Clarke P, Goodspeed R, Gomez-Lopez IN, Romero D, Vvydiswaran VGV. User acceptance of locationtracking technologies in health research: Implications for study design and data quality. Journal of Biomedical Informatics 2018; 79: 7-19 https://doir.org/10.1016/j.jbi.2018.01.003.
• 4. Ruktanonchai NW, Ruktanonchai CW, Floyd JR, Tatem AJ. Using Google Location History data to quantify fine-scale human mobility. International Journal of Health Geographics 2018; 17(1): 1-13. https://doi.org/10.1186/s12942-018-0150-z.
• 5. Yang SY, Hsu CL. A location-based services and Google maps-based information master system for tour guiding. Computers and Electrical Engineering 2016; 54: 87-105. https://doi.org/10.1016/j.compeleceng.2015.11.020.
• 6. Zhang R. Applying RFID and GPS tracker for signal processing in a cargo security system. 2013 IEEE International Conference on Signal Processing, Communications and Computing ISCPCC 2013; https://doi.org/10.1109/ICSPCC.2013.6664053.
• 7. Macarulla Rodriguez A, Tiberius C, Bree R, Geradts Z. Google timeline accuracy assessment and error prediction. Forensic Sciences Research 2018; 3(3): 240-55. https://doi.org/10.1080/20961790.2018.1509187.
• 8. Yao W, Chu CH, Li Z. The use of RFID in healthcare: Benefits and barriers. Proceedings of 2010 IEEE International Conference on RFID-Technology and Applications, RFID-TA 2010 2010; (June): :128-34. https://doi.org/10.1109/RFID-TA.2010.5529874.
• 9. Homdee N, Alam R, Wolfe S, Lach J. Mind the gaps: Edge-based monitoring for continuous room-level location tracking. Smart Health 2018; 9-10: 297-306. https://doi.org/10.1016/j.smhl.2018.07.020.
• 10. Surian D, Kim V, Menon R, Dunn AG, Sintchenko V, Coiera E. Tracking a moving user in indoor environments using Bluetooth low energy beacons. Journal of Biomedical Informatics 2019; 98: 103288. https://doi.org/10.1016/j.jbi.2019.103288.
• 11. Goodspeed R, Yan X, Hardy J, Vydiswaran VV, Berrocal VJ, Clarke P. Comparing the data quality of global positioning system devices and mobile phones for assessing relationships between place, mobility, and health: Field study. JMIR mHealth and uHealth 2018; 6(8). https://doi.org/10.2196/mhealth.9771.
• 12. Belciug FG, Turcu CE. Hybrid agent based algorithm for an efficient RFID-based health information system. 2017 E-Health and Bioengineering Conference, EHB 2017; 277-80. https://doi.org/10.1109/EHB.2017.7995415.
• 13. Sardianos C, Varlamis I, Bouras G. Extracting user habits from google maps history logs. 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 690-697. https://doi.org/10.1109/ASONAM.2018.8508442.
• 14. Cumulative confirmed COVID-19 cases by world region. Available: https://ourworldindata.org/grapher/cumulative-covidcases-region.
• 15. Cumulative confirmed COVID-19 deaths by world region. Available: https://ourworldindata.org/grapher/cumulative-coviddeaths-region.
• 16. WHO. WHO Covid-19 Weekly Operational Update 2020. Available: https://www.who.int/emergencies/diseases/novelcoronavirus-2019/strategies-and-plans.
• 17. Kamel Boulos MN, Geraghty EM. Geographical tracking and mapping of coronavirus disease COVID19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbr. International Journal of Health Geographics 2020; 19(1): 1-12. https://doi.org/10.1186/s12942-020-00202-8.
• 18. Mohammed MN, Hazairin NA, Al-Zubaid S, A.K S, Mustapha S, Yusuf E. Toward a novel design for coronavirus detection and diagnosis system using IOT based drone technology. International Journal of Psychosocial Rehabilitation 2020; 24(7): 2287-95. https://doi.org/10.37200/IJPR/V24I7/PR270220.
• 19. Mohammed MN, Hazairin NA, Syamsudin H, AlZubaidi S, Sairah AK, Mustapha S, Yusuf E. 2019 novel coronavirus disease (COVID-19): detection and diagnosis system using IOT based smart glasses. International Journal of Advanced Science and Technology 2020; 29(7): 954-960.
• 20. Mohammed MN, Syamsudin H, Al-Zubaidi S, A.K S, Ramli R, Yusuf E. Novel COVID-19 detection and diagnosis system using IOT based smart helmet. International Journal of Psychosocial Rehabilitation 2020; 24(7): 2296-303. https://doi.org/10.37200/IJPR/V24I7/PR270221.
• 21. Cantelmo G, Vitello P, Toader B, Antoniou, Viti F. Inferring urban mobility and habits from user location history. Transportation Research Procedia 2020; 47: 283-90. https://doi.org/10.1016/j.trpro.2020.03.100.
• 22. Kestens Y, Thierry B, Chaix B. Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data. Health and Place 2016; 40: 29-33. https://doi.org/10.1016/j.healthplace.2016.04.004.
• 23. Wojtusiak J, Mogharab Nia R. Location prediction using GPS trackers: Can machine learning help locate the missing people with dementia? Internet of Things 2019; 100035. https://doi.org/10.1016/j.iot.2019.01.002.
• 24. Wu YC, Chen PF, Hu ZA, Chang CH, Lee GC, Yu WC. A mobile health monitoring system using RFID ring-type pulse sensor. 8th IEEE International Symposium on Dependable, Autonomic and Secure Computing, DASC 2009; 317-22. https://doi.org/10.1109/DASC.2009.136.
• 25. Razak NA, Arshad NHM, Adnan R, Misnan MF, Thamrin NM, Mahmud SF. A real-time angle deviation detection and measurement technique for straight line quadrocopter navigation using accelorometer. Proceedings - 2013 IEEE Conference on Systems, Process and Control, ICSPC 2013; 295-300. https://doi.org/10.1109/SPC.2013.6735150.
• 26. Bucher D, Mangili F, Cellina F, Bonesana C, Jonietz D, Raubal M. From location tracking to personalized eco-feedback: A framework for geographic information collection, processing and visualization to promote sustainable mobility behaviors. Travel Behaviour and Society 2019; 14: 43-56. https://doi.org/10.1016/j.tbs.2018.09.005.
• 27. Yu X, Stuart AL, Liu Y, Ivey CE, Russell AG, Kan H. On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. Environmental Pollution 2019; 252: 924-30. https://doi.org/10.1016/j.envpol.2019.05.081.
• 28. Zenk SN, Schulz AJ, Matthews SA, Odoms-Young A, Wilbur JE, Wegrzyn L. Activity space environment and dietary and physical activity behaviors: A pilot study. Health and Place 2011; 17(5): 1150-61. https://doi.org/10.1016/j.healthplace.2011.05.001.
• 29. Coustasse A, Cunningham B, Deslich S, Willson E, Meadows P. Benefits and barriers of implementation and utilization of radio-frequency identification (RFID) systems in transfusion medicine. Perspectives in health information management 2015; 12.
• 30. Dusseljee-Peute LW, Togt R, Jansen B, Jaspers MW. The value of radio frequency identification in quality management of the blood transfusion chain in an academic hospital setting. JMIR Medical Informatics 2019; 7(3). https://doi.org/10.2196/medinform.9510.
• 31. Martínez Pérez M, Cabrero-Canosa M, Vizoso Hermida J, Carrajo García L, Llamas Gómez D, Vázquez González G. Application of RFID technology in patient tracking and medication traceability in emergency care. Journal of Medical Systems 2012; 36(6): 3983-93. https://doi.org/10.1007/s10916-012-9871-x.
• 32. Madanian S. The use of e-health technology in healthcare environment: The role of RFID technology. 10th International Conference on e-Commerce in Developing Countries: With Focus on e-Tourism, ECDC 2016; 1-5. https://doi.org/10.1109/ECDC.2016.7492974.
• 33. Baji AM, Salini M, Sandra S, Thomas SR, Ravikumar A. Track&Go: A location prediction web application. 2017 International Conference on Networks and Advances in Computational Technologies, NetACT 2017; 52-5. https://doi.org/10.1109/NETACT.2017.8076740.
• 34. Alfiras M, Yassin AA, Bojiah J. Present and the Future Role of the Internet of Things in Higher Education Institutions. Journal of Positive Psychology and Wellbeing 2022; 6(1): 167-175.
• 35. Burdzik R. Epidemic Risk Analysis and Assessment in Transport Services: COVID-19 and Other Viruses. CRC Press 2021.
• 36. Budzynski M, Luczkiewicz A, Szmaglinski J. Assessing the risk in urban public transport for epidemiologic factors. Energies 2021; 14(15): 4513.
• 37. Alanzi T. A review of mobile applications available in the app and google play stores used during the COVID-19 outbreak. Journal of multidisciplinary healthcare 2021; 45-57.
• 38. Hu T, Wang S, She B, Zhang M, Huang X, Cui Y, Li Z. Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges. International Journal of Digital Earth 2021; 14(9): 1126-1147. https://doi.org/10.1080/17538947.2021.1952324.
• 39. Nomura S, Tanoue Y, Yoneoka D, Gilmour S, Kawashima T, Eguchi A, Miyata H. Mobility patterns in different age groups in Japan during the COVID-19 pandemic: a small area time series analysis through March 2021. Journal of Urban Health 2021; 98(5): 635-641. https://doi.org/10.1007/s11524-021-00566-7.
• 40. Vecino-Ortiz AI, Villanueva Congote J, Zapata Bedoya S, Cucunuba ZM. Impact of contact tracing on COVID-19 mortality: An impact evaluation using surveillance data from Colombia. PLoS One 2021; 16(3). https://doi.org/10.1371/journal.pone.0246987.
• 41. Felipe SGB, Parreira Batista P, da Silva CCR, de Melo RC, de Assumpção D, Perracini MR. Impact of COVID‐19 pandemic on mobility of older adults: A scoping review. International journal of older people nursing 2023; 18(1). https://doi.org/10.1111/opn.12496.
• 42. Al-shami M, Abdulghafor R, Aborujilah A, Yagoub A, Turaev S, Ali MA. COVID-19 Tracking Applications Acceptance among General Populace: An Overview in Malaysia. Sustainability 2023; 15(5): 4060. https://doi.org/10.3390/su15054060.