Systematic Review of Real-time Remote Health Monitoring System in Triage and Priority-Based Sensor Technology: Taxonomy, Open Challenges, Motivation and Recommendations

  • O. S. Albahri
  • A. S. Albahri
  • K. I. Mohammed
  • A. A. ZaidanEmail author
  • B. B. Zaidan
  • M. Hashim
  • Omar H. Salman
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement


The new and ground-breaking real-time remote monitoring in triage and priority-based sensor technology used in telemedicine have significantly bounded and dispersed communication components. To examine these technologies and provide researchers with a clear vision of this area, we must first be aware of the utilised approaches and existing limitations in this line of research. To this end, an extensive search was conducted to find articles dealing with (a) telemedicine, (b) triage, (c) priority and (d) sensor; (e) comprehensively review related applications and establish the coherent taxonomy of these articles. ScienceDirect, IEEE Xplore and Web of Science databases were checked for articles on triage and priority-based sensor technology in telemedicine. The retrieved articles were filtered according to the type of telemedicine technology explored. A total of 150 articles were selected and classified into two categories. The first category includes reviews and surveys of triage and priority-based sensor technology in telemedicine. The second category includes articles on the three-tiered architecture of telemedicine. Tier 1 represents the users. Sensors acquire the vital signs of the users and send them to Tier 2, which is the personal gateway that uses local area network protocols or wireless body area network. Medical data are sent from Tier 2 to Tier 3, which is the healthcare provider in medical institutes. Then, the motivation for using triage and priority-based sensor technology in telemedicine, the issues related to the obstruction of its application and the development and utilisation of telemedicine are examined on the basis of the findings presented in the literature.


Telemedicine Telehealth Healthcare Services Real-time remote monitoring Triage Priority Sensor 



This study was funded by UPSI grant No: 2017–0179–109-01.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Raikhelkar, J., and Raikhelkar, J. K., The impact of telemedicine in cardiac critical care. Crit. Care Clin. 31(2):305–317, 2015.PubMedCrossRefGoogle Scholar
  2. 2.
    Doumbouya, M. B., Kamsu-Foguem, B., Kenfack, H., and Foguem, C., A framework for decision making on teleexpertise with traceability of the reasoning. IRBM 36(1):40–51, 2015.CrossRefGoogle Scholar
  3. 3.
    Hamdi, O., Chalouf, M. A., Ouattara, D., and Krief, F., eHealth: Survey on research projects, comparative study of telemonitoring architectures and main issues. J. Netw. Comput. Appl. 46:100–112, 2014.CrossRefGoogle Scholar
  4. 4.
    Salman, O. H., Rasid, M. F. A., Saripan, M. I., and Subramaniam, S. K., Multi-sources data fusion framework for remote triage prioritization in telehealth. J. Med. Syst. 38(9):103, 2014.PubMedCrossRefGoogle Scholar
  5. 5.
    Sene, A., Kamsu-foguem, B., and Rumeau, P., Telemedicine framework using case-based. Comput. Methods Prog. Biomed. 121(1):21–35, 2015.CrossRefGoogle Scholar
  6. 6.
    Rajan, S. P., Review and investigations on future research directions of mobile based telecare system for cardiac surveillance. J. Appl. Res. Technol. Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico 13(4):454–460, 2015.CrossRefGoogle Scholar
  7. 7.
    Negra, R., Jemili, I., and Belghith, A., Wireless body area networks: Applications and technologies. Procedia Comput. Sci. 83:1274–1281, 2016.CrossRefGoogle Scholar
  8. 8.
    Niswar, M., et al. The design of wearable medical device for triaging disaster casualties in developing countries. In: 2015 5th International Conference on Digital Information Processing and Communications, ICDIPC 2015, 2015, pp. 207–212.Google Scholar
  9. 9.
    Moreno, S., Quintero, A., Ochoa, C., Bonfante, M., Villareal, R., and Pestana, J., Remote monitoring system of vital signs for triage and detection of anomalous patient states in the emergency room. In: 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), 2016, pp. 1–5.Google Scholar
  10. 10.
    Dos Santos, J. R. B., Blard, G., Oliveira, A. S. R., and De Carvalho, N. B., Wireless sensor tag and network for improved clinical triage. In: 2015 Euromicro Conference on Digital System Design, 2015, pp. 399–406.Google Scholar
  11. 11.
    Gambhir, S., and Kathuria, M., DWBAN: Dynamic priority based WBAN architecture for healthcare system. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 3380–3386.Google Scholar
  12. 12.
    Yuan, X., Li, C., Song, Y., Yang, L., and Ullah, S., On energy-saving in e-healthcare: a directional MAC protocol for WBAN. In: 2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings, 2015.Google Scholar
  13. 13.
    de la Piedra, A., Braeken, A., Touhafi, A., and Wouters, K., Secure event logging in sensor networks. Comput. Math Appl. 65(5):762–773, 2013.CrossRefGoogle Scholar
  14. 14.
    Rodrigues, E. M. G., Godina, R., Cabrita, C. M. P., and Catalão, J. P. S., Biomedical signal processing and control experimental low cost reflective type oximeter for wearable health systems. Biomed. Signal Process. 31:419–433, 2017.CrossRefGoogle Scholar
  15. 15.
    Ahmadi-javid, A., Seyedi, P., and Syam, S. S., Computers & operations research a survey of healthcare facility location. Comput. Oper. Res. 79:223–263, 2017.CrossRefGoogle Scholar
  16. 16.
    Alaiad, A., and Zhou, L., The determinants of home healthcare robots adoption: An empirical investigation. Int. J. Med. Inform. 83(11):825–840, 2014.PubMedCrossRefGoogle Scholar
  17. 17.
    Carayon, P., Kianfar, S., Li, Y., Xie, A., Alyousef, B., and Wooldridge, A., Review article a systematic review of mixed methods research on human factors and ergonomics in health care. Appl. Ergon. 51:291–321, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Ejehiohen, G., Herselman, M., and Botha, A., Digital health innovation ecosystems: From systematic literature review to conceptual framework. Procedia - Procedia Comput. Sci. 100:244–252, 2016.CrossRefGoogle Scholar
  19. 19.
    Point, C., Accreditation, E., and Benton, D., Health care delivery. J. Nurs. Regul. 7(4):S12–S16, 2017.CrossRefGoogle Scholar
  20. 20.
    Aggidis, A. G. A., Newman, J. D., and Aggidis, G. A., Investigating pipeline and state of the art blood glucose biosensors to formulate next steps. Biosens. Bioelectron. 74:243–262, 2015.PubMedCrossRefGoogle Scholar
  21. 21.
    Alberdi, A., Aztiria, A., and Basarab, A., Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. J. Biomed. Inform. Elsevier Inc 59:49–75, 2016.PubMedCrossRefGoogle Scholar
  22. 22.
    Albert, N. M., A systematic review of transitional-care strategies to reduce rehospitalization in patients with heart failure. Hear. Lung. J. Acute Crit. Care 45(2):100–113, 2016.CrossRefGoogle Scholar
  23. 23.
    Brian, R. M., and Ben-Zeev, D., Mobile health (mHealth) for mental health in Asia: Objectives, strategies, and limitations. Asian J. Psychiatr. Elsevier B.V 10(2014):96–100, 2014.PubMedCrossRefGoogle Scholar
  24. 24.
    Finet, P., Le Bouquin Jeannès, R., Dameron, O., and Gibaud, B., Review of current telemedicine applications for chronic diseases. Toward a more integrated system? IRBM 36(3):133–157, 2015.CrossRefGoogle Scholar
  25. 25.
    Hu, J. et al., Portable microfluidic and smartphone-based devices for monitoring of cardiovascular diseases at the point of care. Biotechnol. Adv. 34(3):305–320, 2016.PubMedCrossRefGoogle Scholar
  26. 26.
    Shore, J. H., Aldag, M., McVeigh, F. L., Hoover, R. L., Ciulla, R., and Fisher, A., Review of mobile health technology for military mental health. Mil. Med. 179(8):865–878, 2014.PubMedCrossRefGoogle Scholar
  27. 27.
    Wade, V., and Stocks, N., The use of telehealth to reduce inequalities in cardiovascular outcomes in Australia and New Zealand: A critical review. Hear. Lung. Circ. 26(4):331–337, 2017.CrossRefGoogle Scholar
  28. 28.
    Tripathi, K. M., Kim, T., Losic, D., and Tung, T. T., Recent advances in engineered graphene and composites for detection of volatile organic compounds (VOCs) and non-invasive diseases diagnosis. Carbon N Y 110:97–129, 2016.CrossRefGoogle Scholar
  29. 29.
    Schulmeister, L., Technology and the transformation of oncology care. Semin. Oncol. Nurs. 32(2):99–109, 2016.PubMedCrossRefGoogle Scholar
  30. 30.
    Renard, E., Cobelli, C., and Kovatchev, B. P., Closed loop developments to improve glucose control at home. Diabetes Res. Clin. Pract. 102(2):79–85, 2013.PubMedCrossRefGoogle Scholar
  31. 31.
    Gross, T. et al., New technologies in emergency medical services for children. Clin. Pediatr. Emerg. Med. 15(1):67–78, 2014.CrossRefGoogle Scholar
  32. 32.
    Sakuragui, R. R. M., Rebelo, M. S., Gutierrez, M. A., Näslund, M., and Håkansson, P., Mobile health in emerging countries: A survey of research initiatives in Brazil. Int. J. Med. Inform. 82(5):283–298, 2013.PubMedCrossRefGoogle Scholar
  33. 33.
    Li, H., Zhang, T., Chi, H., Chen, Y., Li, Y., and Wang, J., Mobile health in China: Current status and future development. Asian J. Psychiatr. 10(2014):101–104, 2014.PubMedCrossRefGoogle Scholar
  34. 34.
    Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre Díez, I., López-Coronado, M., and Saleem, K., Mobile-health: A review of current state in 2015. J. Biomed. Inform. 56:265–272, 2015.PubMedCrossRefGoogle Scholar
  35. 35.
    Adams, Z. W., McClure, E. A., Gray, K. M., Danielson, C. K., Treiber, F. A., and Ruggiero, K. J., Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. J. Psychiatr. Res. 85:1–14, 2017.CrossRefGoogle Scholar
  36. 36.
    Liu, L., Stroulia, E., Nikolaidis, I., Miguel-cruz, A., and Rios, A., Smart homes and home health monitoring technologies for older adults: A systematic review. Int. J. Med. Inform. 91:44–59, 2016.PubMedCrossRefGoogle Scholar
  37. 37.
    Obi, T., Ishmatova, D., and Iwasaki, N., Promoting ICT innovations for the ageing population in Japan. Int. J. Med. Inform. 82(4):e47–e62, 2013.PubMedCrossRefGoogle Scholar
  38. 38.
    Rantz, M. J. et al., Sensor technology to support aging in place. J. Am. Med. Dir. Assoc. 14(6):386–391, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Woznowski, P., Kaleshi, D., Oikonomou, G., and Craddock, I., Classification and suitability of sensing technologies for activity recognition. Comput. Commun. 89–90:34–50, 2016.CrossRefGoogle Scholar
  40. 40.
    Reeder, B., Meyer, E., Lazar, A., Chaudhuri, S., Thompson, H. J., and Demiris, G., Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: A systematic review. Int. J. Med. Inform. 82(7):565–579, 2013.PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Phillips, S. A., Martino, S., and Arena, R., Research opportunities and challenges in the era of healthy living medicine: Unlocking the potential. Prog. Cardiovasc. Dis. 59(5):498–505, 2017.PubMedCrossRefGoogle Scholar
  42. 42.
    Bradai, N., Charfi, E., Fourati, L. C., and Kamoun, L., Priority consideration in inter-WBAN data scheduling and aggregation for monitoring systems. Trans. Emerg. Telecommun. Technol. 27(4):589–600, 2016.CrossRefGoogle Scholar
  43. 43.
    Villalonga, C., Pomares, H., Rojas, I., and Banos, O., MIMU-wear: Ontology-based sensor selection for real-world wearable activity recognition. Neurocomputing 250(2017):76–100, 2017.CrossRefGoogle Scholar
  44. 44.
    Ghanavati, S., Abawaji, J., and Izadi, D., A congestion control scheme based on fuzzy logic in wireless body area networks. In: 2015 I.E. 14th International Symposium on Network Computing and Applications, 2015, pp. 235–242.Google Scholar
  45. 45.
    Ghanavati, S., Abawajy, J., and Izadi, D., ECG rate control scheme in pervasive health care monitoring system. In: 2016 I.E. International Conference on Fuzzy Systems (FUZZ-IEEE), 2016, pp. 2265–2270.Google Scholar
  46. 46.
    Haque, S. A., and Aziz, S. M., False alarm detection in cyber-physical systems for healthcare applications. AASRI Procedia 5:54–61, 2013.CrossRefGoogle Scholar
  47. 47.
    Almadani, B., Saeed, B., and Alroubaiy, A., Healthcare systems integration using Real Time Publish Subscribe (RTPS) middleware. Comput. Electr. Eng. 50:67–78, 2016.CrossRefGoogle Scholar
  48. 48.
    Ben Elhadj, H., Elias, J., Chaari, L., and Kamoun, L., Multi-attribute decision making handover algorithm for wireless body area networks. Comput. Commun. 81:97–108, 2016.CrossRefGoogle Scholar
  49. 49.
    Bradai, N., Fourati, L. C., and Kamoun, L., Ad hoc networks WBAN data scheduling and aggregation under WBAN / WLAN healthcare network. 25:251–262, 2015.Google Scholar
  50. 50.
    Gündoğdu, K., and Çalhan, A., An implementation of wireless body area networks for improving priority data transmission delay. J. Med. Syst. 40(3):75, 2016.PubMedCrossRefGoogle Scholar
  51. 51.
    Kim, R. H., Kim, P. S., and Kim, J. G., An effect of delay reduced MAC protocol for WBAN based medical signal monitoring. In: 2015 I.E. Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2015, vol. 2015–Novem, pp. 434–437.Google Scholar
  52. 52.
    Zhao, Y., and Kerkhoff, H. G., Design of an embedded health monitoring infrastructure for accessing multi-processor SoC degradation. In: 2014 17th Euromicro Conference on Digital System Design, 2014, pp. 154–160.Google Scholar
  53. 53.
    Shah, M. A., Kim, J., Khadra, M. H., and Feng, D., Home area network for optimizing telehealth services- empirical simulation analysis. 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2014, pp. 1370–1373.Google Scholar
  54. 54.
    Rezvani, S., and Ghorashi, S. A., Context aware and channel-based resource allocation for wireless body area networks. IET Wirel. Sens. Syst. 3(1):16–25, 2013.CrossRefGoogle Scholar
  55. 55.
    Ren, J., Wu, G., Li, X., Pirozmand, P., and Obaidat, M. S., Probabilistic response-time analysis for real-time systems in body area sensor networks. 2145–2166, 2015.Google Scholar
  56. 56.
    Rezaee, A. A., Yaghmaee, M. H., and Rahmani, A. M., Optimized congestion management protocol for healthcare wireless sensor networks. Wirel. Pers. Commun. 75(1):11–34, 2014.CrossRefGoogle Scholar
  57. 57.
    Rezaee, A. A., Yaghmaee, M. H., Rahmani, A. M., and Mohajerzadeh, A. H., HOCA: Healthcare aware optimized congestion avoidance and control protocol for wireless sensor networks. J. Netw. Comput. Appl. 37(1):216–228, 2014.CrossRefGoogle Scholar
  58. 58.
    Kaur, J., Saini, K. S., and Grewal, R., Priority based congestion avoidance hybrid scheme for wireless sensor network. In: 2015 1st International Conference on Next Generation Computing Technologies (NGCT), 2015, pp. 158–165.Google Scholar
  59. 59.
    Al Mamoon, I., Muzahidul-Islam, A. K. M., Baharun, S., Komaki, S., and Ahmed, A., Architecture and communication protocols for cognitive radio network enabled hospital. In: 2015 9th International Symposium on Medical Information and Communication Technology (ISMICT), 2015, vol. 2015–May, pp. 170–174.Google Scholar
  60. 60.
    Lin, D., Labeau, F., Yao, Y., Vasilakos, A. V., and Tang, Y., Admission control over internet of vehicles attached with medical sensors for ubiquitous healthcare applications. IEEE J. Biomed. Heal. Informatics 20(4):1195–1204, 2016.CrossRefGoogle Scholar
  61. 61.
    Li, C., Yuan, X., Yang, L., and Song, Y., A hybrid lifetime extended directional approach for WBANs. Sensors 15(12):28005–28030, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Hwang, T. H., Kim, D. S., and Kim, J. G., An on-time power-aware scheduling scheme for medical sensor SoC-based WBAN systems. Sensors (Switzerland) 13(1):375–392, 2013.CrossRefGoogle Scholar
  63. 63.
    Li, N., Lin, K., Yong, S., Chen, X., Wang, X., and Zhang, X., Design and implementation of a MAC protocol for a wearable monitoring system on human body. In: 2015 I.E. 11th International Conference on ASIC (ASICON), 2015, pp. 1–4.Google Scholar
  64. 64.
    Sudha, G. F., Karthik, S., and Kumar, N. S., Activity aware energy efficient priority based multi patient monitoring adaptive system for body sensor networks. Technol. Health Care 22(2):167–177, 2014.PubMedGoogle Scholar
  65. 65.
    Kong, R., Chen, C., Yu, W., Yang, B., and Guan, X., Data priority based slot allocation for wireless body area networks. In: 2013 International Conference on Wireless Communications and Signal Processing, 2013, pp. 1–6.Google Scholar
  66. 66.
    Misra, S., and Sarkar, S., Priority-based time-slot allocation in wireless body area networks during medical emergency situations: An evolutionary game-theoretic perspective. IEEE J. Biomed. Heal. Informatics 19(2):541–548, 2015.CrossRefGoogle Scholar
  67. 67.
    Puri, T., Challa, R. K., and Sehgal, N. K., Energy efficient QoS aware MAC layer time slot allocation scheme for WBASN. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015, pp. 966–972.Google Scholar
  68. 68.
    Chiang, H., Lai, C., and Huang, Y., A green cloud-assisted health monitoring service on wireless body area networks. Inf. Sci. (Ny) 284:118–129, 2014.CrossRefGoogle Scholar
  69. 69.
    Ben Elhadj, H., Elias, J., Chaari, L., and Kamoun, L., A priority based cross layer routing protocol for healthcare applications. Ad Hoc Netw. 42:1–18, 2016.CrossRefGoogle Scholar
  70. 70.
    Hu, L., Zhang, Y., Feng, D., Hassan, M. M., Alelaiwi, A., and Alamri, A., Design of QoS-aware multi-level MAC-layer for wireless body area network. J. Med. Syst. 39(12):192, 2015.PubMedCrossRefGoogle Scholar
  71. 71.
    Iftikhar, M., and Ahmad, I., A novel analytical model for provisioning QoS in body area sensor networks. Procedia Comput. Sci. 32:900–907, 2014.CrossRefGoogle Scholar
  72. 72.
    Iftikhar, M., Al Elaiwi, N., and Aksoy, M. S., Performance analysis of priority queuing model for low power Wireless Body Area Networks (WBANs). Procedia Comput. Sci. 34:518–525, 2014.CrossRefGoogle Scholar
  73. 73.
    Sevin, A., Bayilmis, C., and Kirbas, I., Design and implementation of a new quality of service-aware cross-layer medium access protocol for wireless body area networks. Comput. Electr. Eng. 56:145–156, 2016.CrossRefGoogle Scholar
  74. 74.
    Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D., Sensing as a service model for smart cities supported by internet of things. Trans. Emerg. Telecommun. Technol. 25(1):81–93, 2014.CrossRefGoogle Scholar
  75. 75.
    Baehr, D., McKinney, S., Quirk, A., and Harfoush, K., On the practicality of elliptic curve cryptography for medical sensor networks. In: 2014 11th Annual High Capacity Optical Networks and Emerging/Enabling Technologies (Photonics for Energy), 2014, pp. 41–45.Google Scholar
  76. 76.
    Hedin, D. S., Kollmann, D. T., Gibson, P. L., Riehle, T. H., and Seifert, G. J., Distance bounded energy detecting ultra-wideband impulse radio secure protocol. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, vol. 2014, pp. 6619–6622.Google Scholar
  77. 77.
    Rubio, Ó. J., Trigo, J. D., Alesanco, Á., Serrano, L., and García, J., Analysis of ISO/IEEE 11073 built-in security and its potential IHE-based extensibility. J. Biomed. Inform. 60:270–285, 2016.PubMedCrossRefGoogle Scholar
  78. 78.
    Benmansour, T., Ahmed, T., and Moussaoui, S., Performance evaluation of IEEE 802.15.6 MAC in monitoring of a cardiac patient. In: 2016 I.E. 41st Conference on Local Computer Networks Workshops (LCN Workshops), 2016, pp. 241–247.Google Scholar
  79. 79.
    Fourati, H., Idoudi, H., Val, T., Van Den Bossche, A., and Saidane, L. A., Performance evaluation of IEEE 802.15.6 CSMA/CA-based CANet WBAN. In: 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), 2015, pp. 1–7.Google Scholar
  80. 80.
    Radhakrishnan, S., Duvvuru, A., and Kamarthi, S. V., Investigating discrete event simulation method to assess the effectiveness of wearable health monitoring devices. Procedia Econ. Financ. 11(14):838–856, 2014.CrossRefGoogle Scholar
  81. 81.
    Meizoso, J. P. et al., Evaluation of miniature wireless vital signs monitor in a trauma intensive care unit. Mil. Med. 181(5S):199–204, 2016.PubMedCrossRefGoogle Scholar
  82. 82.
    Alnanih, R., Ormandjieva, O., and Radhakrishnan, T., Context-based and rule-based adaptation of mobile user interfaces in mHealth. Procedia Comput. Sci. 21:390–397, 2013.CrossRefGoogle Scholar
  83. 83.
    Fratini, A., and Caleffi, M., Medical emergency alarm dissemination in urban environments. Telemat. Informatics 31(3):511–517, 2014.CrossRefGoogle Scholar
  84. 84.
    Boursalie, O., Samavi, R., and Doyle, T. E., M4CVD: Mobile machine learning model for monitoring cardiovascular disease. Procedia Comput. Sci 63(Icth):384–391, 2015.CrossRefGoogle Scholar
  85. 85.
    Fezari, M., Rasras, R., and El Emary, I. M. M., Ambulatory health monitoring system using wireless sensors node. Procedia Comput. Sci 65(Iccmit):86–94, 2015.CrossRefGoogle Scholar
  86. 86.
    Villarreal, V., Fontecha, J., Hervas, R., and Bravo, J., Mobile and ubiquitous architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes. Futur. Gener. Comput. Syst. 34:161–175, 2014.CrossRefGoogle Scholar
  87. 87.
    Sebillo, M., Tortora, G., Tucci, M., Vitiello, G., Ginige, A., and Di Giovanni, P., Combining personal diaries with territorial intelligence to empower diabetic patients. J. Vis. Lang. Comput. 29:1–14, 2015.CrossRefGoogle Scholar
  88. 88.
    Katib, A., Rao, D., Rao, P., Williams, K., and Grant, J., A prototype of a novel cell phone application for tracking the vaccination coverage of children in rural communities. Comput. Methods Prog. Biomed. 122(2):215–228, 2015.CrossRefGoogle Scholar
  89. 89.
    Lwin, M. O. et al., A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication. Acta Trop. 130:100–107, 2014.PubMedCrossRefGoogle Scholar
  90. 90.
    Bresó, A., Martínez-Miranda, J., Fuster-García, E., and García-Gómez, J. M., A novel approach to improve the planning of adaptive and interactive sessions for the treatment of major depression. Int. J. Hum. Comput. Stud. 87:80–91, 2016.CrossRefGoogle Scholar
  91. 91.
    Chakraborty, S., Ghosh, S. K., Jamthe, A., and Agrawal, D. P., Detecting mobility for monitoring patients with parkinson’s disease at home using RSSI in a wireless sensor network. Procedia Comput. Sci. 19:956–961, 2013.CrossRefGoogle Scholar
  92. 92.
    Hermens, H., op den Akker, H., Tabak, M., Wijsman, J., and Vollenbroek, M., Personalized coaching systems to support healthy behavior in people with chronic conditions. J. Electromyogr. Kinesiol. 24(6):815–826, 2014.PubMedCrossRefGoogle Scholar
  93. 93.
    Adibi, S., A mobile health network disaster management system. In: 2015 Seventh International Conference on Ubiquitous and Future Networks, 2015, pp. 424–428.Google Scholar
  94. 94.
    Beck, C., and Georgiou, J., A wearable, multimodal, vitals acquisition unit for intelligent field triage. In: 2016 I.E. International Symposium on Circuits and Systems (ISCAS), 2016, vol. 2016–July, pp. 1530–1533.Google Scholar
  95. 95.
    Besaleva, L. I., and Weaver, A. C., Mobile electronic triaging for emergency response improvement through crowdsourced and sensor-detected information. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM ‘13, 2013, pp. 1092–1093.Google Scholar
  96. 96.
    Ganz, A., Schafer, J. M., Tang, J., Yang, Z., Yi, J., and Ciottone, G., Urban search and rescue situational awareness using DIORAMA disaster management system. Procedia Eng. 107:349–356, 2015.CrossRefGoogle Scholar
  97. 97.
    Gunasekaran, S., and Suresh, M., A novel control of disaster protection (NCDP) for pilgrims by pan technology. In: 2014 I.E. 8th International Conference on Intelligent Systems and Control (ISCO), 2014, pp. 103–107.Google Scholar
  98. 98.
    Renner, A., et al., RIPPLE: Scalable medical telemetry system for supporting combat rescue. In: NAECON 2014 - IEEE National Aerospace and Electronics Conference, 2014, vol. 2015–Febru, pp. 228–232.Google Scholar
  99. 99.
    Ali, R. et al., GUDM: Automatic generation of unified datasets for learning and reasoning in healthcare. Sensors 15(12):15772–15798, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Gaynor, M., and Waterman, J., Design framework for sensors and RFID tags with healthcare applications. Heal. Policy Technol. 5(4):357–369, 2016.CrossRefGoogle Scholar
  101. 101.
    Kim, H.-K., Convergence agent model for developing u-healthcare systems. Futur. Gener. Comput. Syst. 35:39–48, 2014.CrossRefGoogle Scholar
  102. 102.
    Misra, S., and Chatterjee, S., Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization. Inf. Sci. (Ny) 284:95–117, 2014.CrossRefGoogle Scholar
  103. 103.
    Zhang, K., Liang, X., Baura, M., Lu, R., and Shen, (Sherman) X., PHDA: A priority based health data aggregation with privacy preservation for cloud assisted WBANs. Inf. Sci. (Ny). 284:130–141, 2014.Google Scholar
  104. 104.
    Yi, C., Zhao, Z., Cai, J., Lobato de Faria, R., and Zhang, (Michael) G., Priority-aware pricing-based capacity sharing scheme for beyond-wireless body area networks. Comput. Netw. 98:29–43, 2016.Google Scholar
  105. 105.
    Yi, C., Alfa, A. S., and Cai, J., An incentive-compatible mechanism for transmission scheduling of delay-sensitive medical packets in e-Health networks. IEEE Trans. Mob. Comput. 15(10):2424–2436, 2016.CrossRefGoogle Scholar
  106. 106.
    Yi, Z., et al., Emergency treatment in smart terminal-based e-healthcare networks. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), 2015, no. Iccsnt, pp. 1178–1181.Google Scholar
  107. 107.
    Sneha, S., and Varshney, U., A framework for enabling patient monitoring via mobile ad hoc network. Decis. Support. Syst. 55(1):218–234, 2013.CrossRefGoogle Scholar
  108. 108.
    Bouakaz, S. et al., CIRDO: Smart companion for helping elderly to live at home for longer. IRBM 35(2):100–108, 2014.CrossRefGoogle Scholar
  109. 109.
    De Backere, F., Bonte, P., Verstichel, S., Ongenae, F., and De Turck, F., The OCarePlatform: A context-aware system to support independent living. Comput. Methods Prog. Biomed. 140:111–120, 2017.CrossRefGoogle Scholar
  110. 110.
    Kormanyos, B., and Pataki, B., Multilevel simulation of daily activities: Why and how? In: 2013 I.E. International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2013, pp. 1–6.Google Scholar
  111. 111.
    Rahmani, A. M. et al., Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Futur. Gener. Comput. Syst. 78:641–658, 2017.CrossRefGoogle Scholar
  112. 112.
    Varshney, U., A model for improving quality of decisions in mobile health. Decis. Support. Syst. 62:66–77, 2014.CrossRefGoogle Scholar
  113. 113.
    Minutolo, A., Esposito, M., and De Pietro, G., Design and validation of a light-weight reasoning system to support remote health monitoring applications. Eng. Appl. Artif. Intell. 41:232–248, 2015.CrossRefGoogle Scholar
  114. 114.
    Sakr, S., and Elgammal, A., Towards a comprehensive data analytics framework for smart healthcare services. Big Data Res. 4:44–58, 2016.CrossRefGoogle Scholar
  115. 115.
    Moutacalli, M. T., Marmen, V., Bouzouane, A., and Bouchard, B., Activity pattern mining using temporal relationships in a smart home. In: 2013 I.E. Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2013, pp. 83–87.Google Scholar
  116. 116.
    Bharatula, S., and Meenakshi, M., Design of cognitive radio network for hospital management system. Wirel. Pers. Commun. 90(2):1021–1038, 2016.CrossRefGoogle Scholar
  117. 117.
    Vaidehi, V., Vardhini, M., Yogeshwaran, H., Inbasagar, G., Bhargavi, R., and Hemalatha, C. S., Agent based health monitoring of elderly people in indoor environments using wireless sensor networks. Procedia Comput. Sci 19(Ant):64–71, 2013.CrossRefGoogle Scholar
  118. 118.
    Ben Othman, S., Zgaya, H., Hammadi, S., Quilliot, A., Martinot, A., and Renard, J., Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling. J. Biomed. Inform. 64:25–43, 2016.PubMedCrossRefGoogle Scholar
  119. 119.
    Peleg, M. et al., Assessment of a personalized and distributed patient guidance system. Int. J. Med. Inform. 101:108–130, 2017.PubMedCrossRefGoogle Scholar
  120. 120.
    Tawfik, H., and Anya, O., Evaluating practice-centered awareness in cross-boundary telehealth decision support systems. Telemat. Informatics 32(3):486–503, 2015.CrossRefGoogle Scholar
  121. 121.
    Tamura, T. et al., Assessment of participant compliance with a Web-based home healthcare system for promoting specific health checkups. Biocybern. Biomed. Eng. 34(1):63–69, 2014.CrossRefGoogle Scholar
  122. 122.
    Lounis, A., Hadjidj, A., Bouabdallah, A., and Challal, Y., Healing on the cloud: Secure cloud architecture for medical wireless sensor networks. Futur. Gener. Comput. Syst. 55:266–277, 2016.CrossRefGoogle Scholar
  123. 123.
    Saleem, K., Derhab, A., Al-Muhtadi, J., and Shahzad, B., Human-oriented design of secure machine-to-machine communication system for e-Healthcare society. Comput. Hum. Behav. 51:977–985, 2015.CrossRefGoogle Scholar
  124. 124.
    Wang, J., Qiu, M., and Guo, B., Enabling real-time information service on telehealth system over cloud-based big data platform. J. Syst. Archit. 72:69–79, 2017.CrossRefGoogle Scholar
  125. 125.
    Nageba, E., Rubel, P., and Fayn, J., Towards an intelligent exploitation of heterogeneous and distributed resources in cooperative environments of eHealth. IRBM 34(1):79–85, 2013.CrossRefGoogle Scholar
  126. 126.
    Doumbouya, M. B., Kamsu-Foguem, B., Kenfack, H., and Foguem, C., Telemedicine using mobile telecommunication: Towards syntactic interoperability in teleexpertise. Telemat. Informatics 31(4):648–659, 2014.CrossRefGoogle Scholar
  127. 127.
    Urovi, V., Jimenez-del-Toro, O., Dubosson, F., Ruiz Torres, A., and Schumacher, M. I., COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines. Comput. Biol. Med. 81:24–31, 2017.PubMedCrossRefGoogle Scholar
  128. 128.
    Tegegne, T., and van der Weide, (Theo) T. P., Enriching queries with user preferences in healthcare. Inf. Process. Manag.. 50(4):599–620, 2014.Google Scholar
  129. 129.
    Ganapathy, K., Vaidehi, V., Kannan, B., and Murugan, H., Hierarchical particle swarm optimization with ortho-cyclic circles. Expert Syst. Appl. 41(7):3460–3476, 2014.CrossRefGoogle Scholar
  130. 130.
    Hindia, M. N., Rahman, T. A., Ojukwu, H., Hanafi, E. B., and Fattouh, A., Enabling remote health-caring utilizing iot concept over LTE-femtocell networks. PLoS One 11(5):e0155077, 2016.PubMedPubMedCentralCrossRefGoogle Scholar
  131. 131.
    Niswar, M., et al., Performance evaluation of ZigBee-based wireless sensor network for monitoring patients’ pulse status. In: 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 2013, pp. 291–294.Google Scholar
  132. 132.
    Paulus, A., Meisen, P., Meisen, T., Jeschke, S., Czaplik, M., and Hirsch, F., AUDIME: Augmented disaster medicine. In: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), 2015, pp. 342–345.Google Scholar
  133. 133.
    Ullah, F., Khelil, A., Sheikh, A. A., Felemban, E., and Bojan, H. M. A., Towards automated self-tagging in emergency health cases. In: 2013 I.E. 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), 2013, no. Healthcom, pp. 658–663.Google Scholar
  134. 134.
    Rodriguez, D., Heuer, S., Guerra, A., Stork, W., Weber, B., and Eichler, M., Towards automatic sensor-based triage for individual remote monitoring during mass casualty incidents. In: 2014 I.E. International Conference on Bioinformatics and Biomedicine (BIBM), 2014, pp. 544–551.Google Scholar
  135. 135.
    Gómez, J., Oviedo, B., and Zhuma, E., Patient monitoring system based on internet of things. Procedia Comput. Sci 83(Ant):90–97, 2016.CrossRefGoogle Scholar
  136. 136.
    Hussain, A., Wenbi, R., da Silva, A. L., Nadher, M., and Mudhish, M., Health and emergency-care platform for the elderly and disabled people in the Smart City. J. Syst. Softw. 110:253–263, 2015.CrossRefGoogle Scholar
  137. 137.
    Lamprinakos, G. C. et al., An integrated remote monitoring platform towards Telehealth and Telecare services interoperability. Inf. Sci. (Ny) 308:23–37, 2015.CrossRefGoogle Scholar
  138. 138.
    Sung, W., and Chang, K., Health parameter monitoring via a novel wireless system. Appl. Soft Comput. 22:667–680, 2014.CrossRefGoogle Scholar
  139. 139.
    Mendes, J., Simões, H., Rosa, P., Costa, N., Rabadão, C., and Pereira, A., Secure low-cost solution for elder’s eCardio surveillance. Procedia Comput. Sci 27(Dsai 2013):46–56, 2014.CrossRefGoogle Scholar
  140. 140.
    Zanjal, S. V., and Talmale, G. R., Medicine reminder and monitoring system for secure health using IOT. Procedia Comput. Sci. 78(3):471–476, 2016.CrossRefGoogle Scholar
  141. 141.
    Ganapathy, K., Priya, B., Priya, B., Dhivya, Prashanth, V., and Vaidehi, V., SOA Framework for geriatric remote health care using wireless sensor network. Procedia Comput. Sci 19(Fams):1012–1019, 2013.CrossRefGoogle Scholar
  142. 142.
    Miah, S. J., Hasan, J., and Gammack, J. G., On-cloud healthcare clinic: An e-health consultancy approach for remote communities in a developing country. Telemat. Informatics 34(1):311–322, 2017.CrossRefGoogle Scholar
  143. 143.
    Moore, P., Thomas, A., Qassem, T., Bessis, N., and Hu, B., Monitoring patients with mental disorders. In: 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2015, pp. 65–70.Google Scholar
  144. 144.
    Traverso, G. et al., Physiologic status monitoring via the gastrointestinal tract. PLoS One 10(11):e0141666, 2015.PubMedPubMedCentralCrossRefGoogle Scholar
  145. 145.
    Kovalchuk, S. V., Krotov, E., Smirnov, P. A., Nasonov, D. A., and Yakovlev, A. N., Distributed data-driven platform for urgent decision making in cardiological ambulance control. Futur. Gener. Comput. Syst. 79:144–154, 2016.CrossRefGoogle Scholar
  146. 146.
    Rajkumar, R., and Sriman Narayana Iyengar, N. C., Dynamic integration of mobile JXTA with cloud computing for emergency rural public health care. Osong Public Heal Res Perspect 4(5):255–264, 2013.CrossRefGoogle Scholar
  147. 147.
    Kumar, N., Kaur, K., Jindal, A., and Rodrigues, J. J. P. C., Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment. Digit Commun. Networks 1(3):191–203, 2015.CrossRefGoogle Scholar
  148. 148.
    Moretti, S., Cicalò, S., Mazzotti, M., Tralli, V., and Chiani, M., Content/context-aware multiple camera selection and video adaptation for the support of m-Health services. Procedia Comput. Sci. 40:206–213, 2014.CrossRefGoogle Scholar
  149. 149.
    Calyam, P. et al., Synchronous big data analytics for personalized and remote physical therapy. Pervasive Mob. Comput. 28:3–20, 2016.CrossRefGoogle Scholar
  150. 150.
    Teijeiro, T., Félix, P., Presedo, J., and Zamarrón, C., An open platform for the protocolization of home medical supervision. Expert Syst. Appl. 40(7):2607–2614, 2013.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • O. S. Albahri
    • 1
  • A. S. Albahri
    • 1
  • K. I. Mohammed
    • 1
  • A. A. Zaidan
    • 1
    Email author
  • B. B. Zaidan
    • 1
  • M. Hashim
    • 1
  • Omar H. Salman
    • 2
  1. 1.Department of ComputingUniversiti Pendidikan Sultan IdrisTanjong MalimMalaysia
  2. 2.Al-Iraqia UniversityBaghdadIraq

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