  Accurate assessment of patient actions plays a crucial role in healthcare as
it contributes significantly to disease progression monitoring and treatment
effectiveness. However, traditional approaches to assess patient actions often
rely on manual observation and scoring, which are subjective and
time-consuming. In this paper, we propose an automated approach for patient
action assessment using a Multi-Residual Spatio Temporal Graph Network
(MR-STGN) that incorporates both angular and positional 3D skeletons. The
MR-STGN is specifically designed to capture the spatio-temporal dynamics of
patient actions. It achieves this by integrating information from multiple
residual layers, with each layer extracting features at distinct levels of
abstraction. Furthermore, we integrate an attention fusion mechanism into the
network, which facilitates the adaptive weighting of various features. This
empowers the model to concentrate on the most pertinent aspects of the
patient's movements, offering precise instructions regarding specific body
parts or movements that require attention. Ablation studies are conducted to
analyze the impact of individual components within the proposed model. We
evaluate our model on the UI-PRMD dataset demonstrating its performance in
accurately predicting real-time patient action scores, surpassing
state-of-the-art methods.
