Objective: Facial weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing facial weakness still remains as a challenge, because it requires experience and neurological training.
Methods: We propose a framework for facial weakness detection, which models the temporal dynamics of both shape and appearance-based features of each target frame through a bi-directional long short-term memory network (Bi-LSTM). The system is evaluated on a "in-the-wild"video dataset that is verified by three board-certified neurologists. In addition, three emergency medical services (EMS) personnel and three upper level residents rated the dataset. We compare the evaluation of the proposed algorithm with other comparison methods as well as the human raters.
Results: Experimental evaluation demonstrates that: (1) the proposed algorithm achieves the accuracy, sensitivity, and specificity of 94.3%, 91.4%, and 95.7%, which outperforms other comparison methods and achieves the equal performance to paramedics; (2) the framework can provide visualizable and interpretable results that increases model transparency and interpretability; (3) a prototype is implemented as a proof-of-concept showcase to show the feasibility of an inexpensive solution for facial weakness detection.
Conclusion: The experiment results suggest that the proposed framework can identify facial weakness effectively.
Significance: We provide a proof-of-concept study, showing that such technology could be used by non-neurologists to more readily identify facial weakness in the field, leading to increasing coverage and earlier treatment.