Titlebook: Body Area Networks. Smart IoT and Big Data for Intelligent Health Management; 16th EAI Internation Masood Ur Rehman,Ahmed Zoha Conference p

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书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management影响因子(影响力)




书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management影响因子(影响力)学科排名




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书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management网络公开度学科排名




书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management被引频次




书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management被引频次学科排名




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书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management年度引用学科排名




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书目名称Body Area Networks. Smart IoT and Big Data for Intelligent Health Management读者反馈学科排名

Abduct

myriad

Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8 GHz Frequency Mod is to monitor activities of daily living using the publicly available dataset recorded in nine different geometrical locations for ninety-nine volunteers including young and older adults (65+) using 5.8 GHz Frequency Modulated Continuous Wave (FMCW) radar. In this work, we experimented with discret

项目

Increment

Wireless Sensing for Human Activity Recognition Using USRPincluding intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sens

outset

极微小

Nerve-Block

Detecting Alzheimer’s Disease Using Machine Learning Methods dementia with Alzheimer’s disease is expected to increase rapidly in the next few years. Currently, healthcare systems require an accurate detection of the disease for its treatment and prevention. Therefore, it has become essential to develop a framework for early detection of Alzheimer’s disease

Mortal

FPGA-Based Realtime Detection of Freezing of Gait of Parkinson Patientso compare our results with state of the art solutions we used the well-known open dataset Daphnet. Our most important findings are even though we used a tool to map the trained model to the FPGA we can detect FoG in less than a millisecond which will give us sufficient time to trigger cueing and by

Gleason-score

Received WiFi Signal Strength Monitoring for Contactless Body Temperature Classificationeading. This work explores an alternative way of classifying temperature using the ubiquitous WiFi waveform. By merely observing the change in the received signal strength indicator (RSSI), body temperature can be classified as below normal, normal, or warm. Using a smartphone as the receiver and a