| | |
Summary: Abstract--This paper presents a framework based on
Gaussian Processes for assessing cross channel consensus in
Body Sensor Network (BSN) data. Cross channel consensus can
be observed by measuring the prediction error of one channel
given the others, which could help in predicting missing data,
correcting for noisy channels, or learning relationships between
sensor channels over time. The method is evaluated with
activities of daily living experiments with sensing data including
heart rate, respiration and activity levels. The acquired
prediction rates indicate the potential practical value of the
technique for home-monitoring of chronically ill patients.
I. INTRODUCTION
ecent developments in body sensor networks (BSNs)
have enabled continuous sensing of a range of
conditions under normal physiological loading of the
patient [1]. This has transformed the traditional snap-shot
nature of patient monitoring, allowing many chronic
conditions to be assessed continuously in normal home
environments. Since many chronic conditions, such as COPD
(Chronic Obstructive Pulmonary Disease), cause a reduction
|
