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.