Skip to main content

Advertisement

Log in

A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Over the last few years, Internet of Things (IoT) has opened the doors to innovations that facilitate interactions among things and humans. Focusing on healthcare domain, IoT devices such as medical sensors, visual sensors, cameras, and wireless sensor network are leading this evolutionary trend. In this direction, the paper proposes a novel, IoT-aware student-centric stress monitoring framework to predict student stress index at a particular context. Bayesian Belief Network (BBN) is used to classify the stress event as normal or abnormal using physiological readings collected from medical sensors at fog layer. Abnormal temporal structural data which is time-enriched dataset sequence is analyzed for various stress-related parameters at cloud layer. To compute the student stress index, a two-stage Temporal Dynamic Bayesian Network (TDBN) model is formed. This model computes stress based on four parameters, namely, leaf node evidences, workload, context, and student health trait. After computing the stress index of the student, decisions are taken in the form of alert generation mechanism with the deliverance of time-sensitive information to caretaker or responder. Experiments are conducted both at fog and cloud layer which hold evidence for the utility and accuracy of the BBN classifier and TDBN predictive model in our proposed system.

Student stress monitoring in IoT-Fog Environment

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): A vision, architectural elements, and future directions. Fut Gener Comput Syst 29(7):1645–1660. https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  2. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tuts 17(4):2347–2376. https://doi.org/10.1109/COMST.2015.2444095

    Article  Google Scholar 

  3. Zheng J, Ha C, Zhang Z (2017) Design and evaluation of a ubiquitous chest-worn cardiopulmonary monitoring system for healthcare application: a pilot study. Med Biol Eng Comput 55(2):283–294. https://doi.org/10.1007/s11517-016-1518-5

    Article  Google Scholar 

  4. Dastjerdi AV, Buyya R (2016) Fog computing: Helping the Internet of Things realize its potential. Computer 49(8):112–116. https://doi.org/10.1109/MC.2016.245

    Article  Google Scholar 

  5. Verma P, Sood SK (2018) Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet Things J 5(3):1789–1796. https://doi.org/10.1109/JIOT.2018.2803201

    Article  Google Scholar 

  6. Jovanov E, Lords AD, Raskovic D, Cox PG, Adhami R, Andrasik F (2003) Stress monitoring using a distributed wireless intelligent sensor system. IEEE Eng Med Biol 22(3):49–55. https://doi.org/10.1109/MEMB.2003.1213626

    Article  Google Scholar 

  7. Suzuki S, Matsui T, Imuta H, Uenoyama M, Yura H, Ishihara M, Kawakami M (2008) A novel autonomic activation measurement method for stress monitoring: non-contact measurement of heart rate variability using a compact microwave radar. Med Biol Eng Comput 46(7):709–714. https://doi.org/10.1007/s11517-007-0298-3

    Article  Google Scholar 

  8. Ayzenberg Y, Rivera JH, Picard R (2012) FEEL: frequent EDA and event logging -a mobile social interaction stress monitoring system. In: CHI’12 Extended Abstracts on Human Factors in Computing Systems. ACM, pp 2357–2362)

  9. Shen YH, Zheng JW, Zhang ZB, Li CM (2012) Design and implementation of a wearable, multiparameter physiological monitoring system for the study of human heat stress, cold stress, and thermal comfort. Instrum Sci Technol 40(4):290–304. https://doi.org/10.1080/10739149.2012.673193

    Article  CAS  Google Scholar 

  10. Tartarisco G, Baldus G, Corda D, Raso R, Arnao A, Ferro M, Gaggioli A, Pioggia G (2012) Personal health system architecture for stress monitoring and support to clinical decisions. Comput Commun 35 (11):1296–1305. https://doi.org/10.1016/j.comcom.2011.11.015

    Article  Google Scholar 

  11. Yoon S, Sim JK, Cho YH (2014) On-chip flexible multi-layer sensors for human stress monitoring. In: IEEE conference Sensors, pp 851–854. https://doi.org/10.1109/ICSENS.2014.6985133

  12. Sheng Z, Yang S, Yu Y, Vasilakos A, Mccann J, Leung K (2013) A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. IEEE Wirel Commun 20(6):91–98. https://doi.org/10.1109/MWC.2013.6704479

    Article  Google Scholar 

  13. Zhou J, Cao Z, Dong X, Xiong N, Vasilakos AV (2014) 4S: A secure and privacy-preserving key management scheme for cloud-assisted wireless body area network in m-healthcare social networks. Inf Sci 331:255-276. https://doi.org/10.1016/j.ins.2014.09.003

    Google Scholar 

  14. Tsai CW, Lai CF, Vasilakos AV (2014) Future Internet of Things: open issues and challenges. Wireless Netw 20(8):2201–2217. https://doi.org/10.1007/s11276-014-0731-0

    Article  Google Scholar 

  15. Fortino G, Di Fatta G, Pathan M, Vasilakos AV (2014) Cloud-assisted body area networks: state-of-the-art and future challenges. Wireless Netw 20(7):1925–1938. https://doi.org/10.1007/s11276-014-0714-1

    Article  Google Scholar 

  16. Chouvarda IG, Goulis DG, Lambrinoudaki I, Maglaveras N (2015) Connected health and integrated care: Toward new models for chronic disease management. Maturitas 82(1):22–27. https://doi.org/10.1016/j.maturitas.2015.03.015

    Article  Google Scholar 

  17. Qin Y, Sheng QZ, Falkner NJ, Dustdar S, Wang H, Vasilakos AV (2016) When things matter: a survey on data-centric internet of things. J Netw Comput Appl 64:137–153. https://doi.org/10.1016/j.jnca.2015.12.016

    Article  Google Scholar 

  18. Zhang D, He Z, Qian Y, Wan J, Li D, Zhao S (2016) Revisiting unknown RFID tag identification in large-scale internet of things. IEEE Wirel Commun 23(5):24–29. https://doi.org/10.1109/MWC.2016.7721738

    Article  Google Scholar 

  19. Amadeo M, Campolo C, Quevedo J, Corujo D, Molinaro A, Iera A, Aguiar RL, Vasilakos AV (2016) Information-centric networking for the internet of things: challenges and opportunities. IEEE Netw 30 (2):92–100. https://doi.org/10.1109/MNET.2016.7437030

    Article  Google Scholar 

  20. Wan J, Tang S, Shu Z, Li D, Wang S, Imran M, Vasilakos AV (2016) Software-defined industrial internet of things in the context of industry 4.0. IEEE Sens J 16(20):7373–7380. https://doi.org/10.1109/JSEN.2016.2565621

    Article  Google Scholar 

  21. Azimi I, Rahmani AM, Liljeberg P, Tenhunen H (2017) Internet of things for remote elderly monitoring: a study from user-centered perspective. J Ambient Intell Humaniz Comput 8(2):273–289. https://doi.org/10.1007/s12652-016-0387-y

    Article  Google Scholar 

  22. Ghanavati S, Abawajy JH, Izadi D, Alelaiwi AA (2017) Cloud-assisted IoT-based health status monitoring framework. Cluster Comput 20(2):1843–1853. https://doi.org/10.1007/s10586-017-0847-y

    Article  Google Scholar 

  23. Yang Z, Zhou Q, Lei L, Zheng K, Xiang W (2016) An IoT-cloud based wearable ECG monitoring system for smart healthcare. J Med Syst 40(12):286. https://doi.org/10.1007/s10916-016-0644-9

    Article  Google Scholar 

  24. Wu T, Wu F, Redouté J M, Yuce MR (2017) An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5:11413–11422. https://doi.org/10.1109/ACCESS.2017.2716344

    Article  Google Scholar 

  25. Ahmad M, Amin MB, Hussain S, Kang BH, Cheong T, Lee S (2016) Health Fog: a novel framework for health and wellness applications. J Supercomput 72(10):3677–3695. https://doi.org/10.1007/s11227-016-1634-x

    Article  Google Scholar 

  26. Vu THN, Park N, Lee YK, Lee Y, Lee JY, Ryu KH (2010) Online discovery of Heart rate variability patterns in mobile healthcare services. J Syst Softw 83(10):1930–1940. https://doi.org/10.1016/j.jss.2010.05.074

    Article  Google Scholar 

  27. Alberdi A, Aztiria A, Basarab A (2016) Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J Biomed Inform 59:49–75. https://doi.org/10.1016/j.jbi.2015.11.007

    Article  Google Scholar 

  28. Van Breda W, Hoogendoorn M, Eiben AE, Berking M (2017) Assessment of temporal predictive models for health care using a formal method. Comput Biol Med 87:347–357. https://doi.org/10.1016/j.compbiomed.2017.06.014

    Article  Google Scholar 

  29. Forkan ARM, Khalil I, Atiquzzaman M (2017) Visibid: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data. Comput Netw 113:244–257. https://doi.org/10.1016/j.comnet.2016.12.019

    Article  Google Scholar 

  30. Karumbaya A, Satheesh G (2015) Iot empowered real time environment monitoring system. Int J Comput Appl 129(5):30–32. https://doi.org/10.5120/ijca2015906917

    Google Scholar 

  31. Zhu Z, Ji Q (2005) Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Comput Vis Image Underst 98(1):124–154. https://doi.org/10.1016/j.cviu.2004.07.012

    Article  Google Scholar 

  32. Koldijk S, Sappelli M, Verberne S, Neerincx MA, Kraaij W (2014) The SWELL knowledge work dataset for stress and user modeling research. In: 16th International Conference on Multimodal Interaction, pp 291–298. https://doi.org/10.1145/2663204.2663257

  33. Lauría E J, Duchessi PJ (2006) A Bayesian belief network for IT implementation decision support. Decis Support Syst 42(3):1573–1588. https://doi.org/10.1016/j.dss.2006.01.003

    Article  Google Scholar 

  34. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012

    Article  Google Scholar 

  35. Mukaka MM (2012) A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24(3):69–71

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Sacchi L, Larizza C, Combi C, Bellazzi R (2007) Data mining with temporal abstractions: learning rules from time series. Data Min Knowl Discov 15(2):217–247. https://doi.org/10.1007/s10618-007-0077-7

    Article  Google Scholar 

  37. Wang L, Tao J, Ranjan R, Marten H, Streit A, Chen J, Chen D (2013) G-hadoop: Mapreduce across distributed data centers for data-intensive computing. Future Gener Comput Syst 29(3):739–750. https://doi.org/10.1016/j.future.2012.09.001

    Article  Google Scholar 

  38. Liu Y, Xu L, Li M (2017) The parallelization of back propagation neural network in MapReduce and Spark. Int J Parallel Prog 45(4):760–779. https://doi.org/10.1007/s10766-016-0401-1

    Article  Google Scholar 

  39. Wang JG, Sung E (2002) Study on eye gaze estimation. IEEE Trans Syst Man Cybern B Cybern 32(3):332–350. https://doi.org/10.1109/TSMCB.2002.999809

    Article  Google Scholar 

  40. Morimoto CH, Mimica MR (2005) Eye gaze tracking techniques for interactive applications. Comput Vis Image Underst 98(1):4–24. https://doi.org/10.1016/j.cviu.2004.07.010

    Article  Google Scholar 

  41. Zhu Z, Ji Q (2004) Real time 3d face pose tracking from an uncalibrated camera. In: IEEE Computer Vision and Pattern Recognition Workshop CVPRW’04, pp 73–73. https://doi.org/10.1109/CVPR.2004.424

  42. Tian Y, Kanade T, Cohn JF (2011) Facial expression recognition. In: Handbook of face recognition. https://doi.org/10.1007/978-0-387-73003-5_98. Springer, London, pp 487–519

  43. Ma L, Khorasani K (2004) Facial expression recognition using constructive feedforward neural networks. IEEE Trans Syst Man Cybern B Cybern 34(3):1588–1595. https://doi.org/10.1109/TSMCB.2004.825930

    Article  CAS  Google Scholar 

  44. Plaut DC, Vande Velde AK (2017) Statistical learning of parts and wholes: a neural network approach. J Exp Psychol Gen 146(3):318–336. https://doi.org/10.1037/xge0000262

    Article  Google Scholar 

  45. Verma P, Sood SK, Kalra S (2017) Cloud-centric IoT based student healthcare monitoring framework. J Ambient Intell Human Comput:1-17. https://doi.org/10.1007/s12652-017-0520-6

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prabal Verma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Verma, P., Sood, S.K. A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective. Med Biol Eng Comput 57, 231–244 (2019). https://doi.org/10.1007/s11517-018-1877-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1877-1

Keywords

Navigation