The respiratory system can be significantly affected by thoracic injuries, which can lead to complications such as lung dysfunction. Therefore, immediate diagnosis, along with the precise location of these injuries is crucial, as it allows targeted medical interventions, reduces unnecessary treatments, and accelerates patient recovery. Respiratory function is dependent on the diaphragm and intercostal muscles, which work in sync to produce individual breathing motion. These motions are highly individual and can be influenced by injuries and respiratory therapy. In this paper, we employ an embedded sensor network to record human breathing patterns and present a novel approach utilizing Temporal Graph Neural Networks (TGCNs) to develop radiation-free, non-invasive techniques for breathing motion monitoring. We modeled the sensor network into a graph structure in which the nodes represent the sensors and the edges represent the correlation among them, enabling our method to effectively capture both spatial and temporal relationships between sensor measurements. We simulate a network of standard sensors to monitor human breathing movements, generating a synthetic dataset of different breathing motions using real data for oversampling, including the degree of abnormalities such as mild, moderate, and severe. We perform node-level classification using the Graph Convolutional Gated Recurrent Unit (GConvGRU) model to identify the abnormal breathing pattern along with the location and severity level of the injuries. Our results demonstrate that TGCNs can accurately localize breathing abnormalities through a graphical sensor network representation, facilitating the location of the potential severity of the injury, and improving remote diagnosis, particularly in post-injury rehabilitation.