A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective.
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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 1; pp. 231 - 245 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133800691&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133800691 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2019 vid: 57 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133800691 133800691 NLM30083806 10.1007/s11517-018-1877-1 NLM30083806 133800691 ppf: 231 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective. aug: au: Verma, Prabal Sood, Sandeep K affil: Computer Science and Engineering, Guru Nanak Dev University, 143521, Amritsar, India sug: subj: Internet Students Psychosocial Factors Stress, Psychological Diagnosis Telemedicine Decision Making Time Factors Probability Models, Theoretical Algorithms ab: 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. Graphical Abstract Student stress monitoring in IoT-Fog Environment. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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