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dc.contributor.authorShah, Syed Azizen
dc.contributor.authorRen, Aifengen
dc.contributor.authorFan, Douen
dc.contributor.authorZhang, Zhiyaen
dc.contributor.authorZhao, Nanen
dc.contributor.authorYang, Xiaodongen
dc.contributor.authorLuo, Mingen
dc.contributor.authorWang, Weigangen
dc.contributor.authorHu, Fangmingen
dc.contributor.authorUr-Rehman, Masooden
dc.contributor.authorBadarneh, Osameh S.en
dc.contributor.authorAbbasi, Qammer Hussainen
dc.date.accessioned2019-12-17T13:41:09Z
dc.date.available2019-12-17T13:41:09Z
dc.date.issued2018-03-27
dc.identifier.citationShah S, Ren A, Fan D, Zhang Z, Zhao N, Yang X, Luo M, Wang W, Hu F, Ur Rehman M, Badarneh O, Abbasi Q, (2018) 'Internet of things for sensing: a case study in the healthcare system', Applied Sciences (Switzerland), 8 (4), pp.508-.en
dc.identifier.doi10.3390/app8040508
dc.identifier.urihttp://hdl.handle.net/10547/623641
dc.description.abstractMedical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.
dc.language.isoenen
dc.publisherMDPIen
dc.relation.urlhttps://www.mdpi.com/2076-3417/8/4/508en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectsmart devicesen
dc.subjectS-band sensingen
dc.subjectInternet of Thingsen
dc.subjectG920 Others in Computing Sciencesen
dc.titleInternet of things for sensing: a case study in the healthcare systemen
dc.typeArticleen
dc.identifier.eissn2076-3417
dc.contributor.departmentXidian University, Chinaen
dc.contributor.departmentNorthwest Women’s and Children’s Hospital, Chinaen
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentUniversity of Tabuk, Saudi Arabiaen
dc.contributor.departmentUniversity of Glasgowen
dc.identifier.journalApplied Sciences (Switzerland)en
dc.date.updated2019-12-17T13:37:19Z
dc.description.noteopen access article with cc licence
html.description.abstractMedical healthcare is one of the fascinating applications using Internet of Things (IoTs). The pervasive smart environment in IoTs has the potential to monitor various human activities by deploying smart devices. In our pilot study, we look at narcolepsy, a disorder in which individuals lose the ability to regulate their sleep-wake cycle. An imbalance in the brain chemical called orexin makes the sleep pattern irregular. This sleep disorder in patients suffering from narcolepsy results in them experience irrepressible sleep episodes while performing daily routine activities. This study presents a novel method for detecting sleep attacks or sleepiness due to immune system attacks and affecting daily activities measured using the S-band sensing technique. The S-Band sensing technique is channel sensing based on frequency spectrum sensing using the orthogonal frequency division multiplexing transmission at a 2 to 4 GHz frequency range leveraging amplitude and calibrated phase information of different frequencies obtained using wireless devices such as card, and omni-directional antenna. Each human behavior induces a unique channel information (CI) signature contained in amplitude and phase information. By linearly transforming raw phase measurements into calibrated phase information, we ascertain phase coherence. Classification and validation of various human activities such as walking, sitting on a chair, push-ups, and narcolepsy sleep episodes are done using support vector machine, K-nearest neighbor, and random forest algorithms. The measurement and evaluation were carried out several times with classification values of accuracy, precision, recall, specificity, Kappa, and F-measure of more than 90% that were achieved when delineating sleep attacks.


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