LBCAM: A Channel Attention Embedded Sensor Fusion Architecture & Its
Applications in Fetal Movement Monitoring
Abstract
The article introduces a novel channel attention architecture embedded
within a sensor fusion framework for fetal movement monitoring. Our
proprietary multi-sensory device recorded the training dataset,
comprising accelerometric sensor data collected from forty-four pregnant
mothers. The channel attention architecture, LBCAM (LSTM Based Channel
Attention Map) can learn important information by observing the
evolution of each sensor channel with time. Notably, it outperforms
existing state-of-the-art models, showcasing its superior performance in
fetal movement monitoring.
We believe that the demonstrated accuracy and efficiency of our model,
as outlined in the manuscript, will significantly contribute to
advancements in not only in fetal health monitoring but also in
introducing a model that brings contextual modifications to robust
models that are already in use in computer vision. The integration of
novel channel attention module and sensor fusion has aided this
introduced model to surpasses current methodologies.