Bidirectional Hetero-Associative Memory Network With Flexible Sensors and Cloud Computing for Blood Leakage Detection in Intravenous and Dialysis Therapy

Abstract

Infiltration and blood leakage are serious life-threatening complications during intravenous administration and dialysis therapy. Serious infiltrations, such as drips and drugs, may cause inflection, sepsis, and extent of tissue damage. Blood leakage and blood loss are serious complications, as it takes only a few minutes to lose over 40% of adult blood volume, potentially resulting in mortality. Therefore, we propose integrating the flexible sensors and bidirectional hetero-associative memory (BHAM) network into a cloud computing-based warning tool for the detection of infiltration and blood leakage. The flexible sensors are arranged in two array patterns for liquid leakage detection and are fabricated on a soft substrate via a screen-printing technique. The BHAM network constructs a virtual alarm unit in an embedded system or tablet PC. This early warning unit can be employed to identify risk levels via a wireless network and cloud computing, and then indicates warning information on a remote monitor device or a mobile device. The proposed detection model has been validated experimentally, and the experimental results demonstrate its feasibility.