Internet of things for sensing: a case study in healthcare system

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Shah, Syed Aziz ; Ren, Aifeng ; Fan, Dou ; Zhang, Zhiya ; Zhao, Nan ; Yang, Xiaodong ; Luo, Ming ; Wang, Weigang ; Hu, Fangming ; Ur Rehman, Masood ; Badarneh, Osamah S. ; Abbasi, Qammer Hussain (2018)
  • Publisher: MDPI
  • Journal: Applied Sciences (issn: 2076-3417)
  • Related identifiers: doi: 10.3390/app8040508
  • Subject: Chemistry | QD1-999 | Engineering (General). Civil engineering (General) | Technology | QH301-705.5 | TA1-2040 | Internet of Things | T | S-band sensing | Physics | smart devices | QC1-999 | Biology (General)

Medical 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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