Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519225
Title: Towards reconstruction quality of biosignals from its compressive measurements for wban based health monitoring environment a deep learning approach
Researcher: Evangelin,D
Guide(s): Rajakumar,G
Keywords: Autoencoder
Compressed Sensing
Information And Communication Engineering
University: Anna University
Completed Date: 2023
Abstract: iii newlineABSTRACT newlineMiniaturized sensors equipped with wireless communication and newlinesensing technologies are becoming more common due to the rapid newlineimprovements in wireless communication and micro-electromechanical newlinesystem-based sensor technology. Due to this mechanism, health care newlineorganization is highly benefitted from this technology. The electrical impulses newlineof a specific human organ can be detected by this miniature sensor, which can newlinethen communicate the information to another device. So, the elderly and newlinechronically ill can be treated instantly and can get health tip at their doorsteps newlinethrough sensor based wearable system. Wireless wearable device plays an newlineimportant role in sensor based wearable technology as it is responsible for newlinecollecting, processing and transmitting to the remote clinician throughout the newlineday. Since the wearables are miniaturized in nature, its battery has been newlineconstrained to the energy level. So, this prolonged monitoring may decline newlinebattery life of wearable device. newline
Pagination: xxii, 133p.
URI: http://hdl.handle.net/10603/519225
Appears in Departments:Faculty of Information and Communication Engineering

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02_prelim_pages.pdf690.65 kBAdobe PDFView/Open
03_content.pdf19.29 kBAdobe PDFView/Open
04_abstract.pdf129.88 kBAdobe PDFView/Open
05_chapter 1.pdf787.88 kBAdobe PDFView/Open
06_chapter 2.pdf1.02 MBAdobe PDFView/Open
07_chapter 3.pdf1.75 MBAdobe PDFView/Open
08_chapter4.pdf1.18 MBAdobe PDFView/Open
10_annexures.pdf371.62 kBAdobe PDFView/Open
80_recommendation.pdf183.04 kBAdobe PDFView/Open
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