Titlebook: Body Area Networks. Smart IoT and Big Data for Intelligent Health; 15th EAI Internation Muhammad Mahtab Alam,Matti Hämäläinen,Yannick Le M

Confess

颠簸下上

Wolf Bors,Christa Michel,Manfred Saranation. Experimental results from 10 participants wearing the smart glasses running our application achieved average step detection error of 2.6% demonstrating the feasibility of our salience-based algorithm for performing pedometry on smart glasses.

繁重

安抚

UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithmhe inclusion positioned at . and . radians, respectively. The ideal image is then used as a reference image to compare the artefact removal methods employing a novel Image Quality Index, calculated using a weighted combination of image quality metrics. The Summed Symmetric Differential method performed very well in our simulations.

呼吸

Model-Based Analysis of Secure and Patient-Dependent Pacemaker Monitoring Systemgeneral health condition, acting environment, remote reporting and others. We demonstrate that including these factors in analysis can provide drastically different results compared to that of average approximating estimates.

botany

Amplitude Modulation in a Molecular Communication Testbed with Superparamagnetic Iron Oxide Nanopartt the receiver to differentiate between six different amplitude levels and grey code is used to reduce bit errors. With AM and the designed coding scheme, the achievable effective data rate was doubled to 4.45 bit s..

ASTER

Activity Monitoring Using Smart Glasses: Exploring the Feasibility of Pedometry on Head Mounted Dispation. Experimental results from 10 participants wearing the smart glasses running our application achieved average step detection error of 2.6% demonstrating the feasibility of our salience-based algorithm for performing pedometry on smart glasses.

搜寻

Anxiety Detection Leveraging Mobile Passive Sensing an individual’s device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features.

Atheroma

degradation