University of Virginia Patent Foundation

Yan Zhuang

Gustavo Kunde Rohde

Andrew M Southerland

Omar Uribe

Mark McDonald

Chad M Aldridge

Mohamed Abul Hassan

An automated and quantitative facial weakness screening framework that utilizes a Bi-LSTM network to model the temporal dynamics among the shape and appearance features. The technique is beneficial to assist the paramedics or other users to identify the facial weakness in the field or, more importantly, whenever expertise in neurology is not available either for emergency patient triage (e.g., pre-hospital stroke care) or chronic disease management (e.g., Bell's palsy rehabilitation screen), leading to increased coverage and earlier treatment. The technique provides visualizable and interpretable results to increase its transparency and interpretability. The technique provides for inexpensive solutions that can be used in areas underserved by non-neurologists to more readily identify neurological deficits such as facial weakness in the field or other environment.

System, method and computer readable medium for video-based facial weakness analysis for detecting neurological deficits

The disclosed embodiments provide systems and methods for predicting presence of one or more neurological deficits. The system may include a microphone, a camera, one or more memory devices storing instructions, and one or more processors configured to execute the instructions to extract audio information including a period density entropy coefficient and a mel frequency cepstral coefficient from an audio feed received from the microphone. Additionally, the instructions may cause the processor to determine position and depth information of eye movement from a video feed received from the camera and detect features of interest including facial landmarks, spatial orientation of limbs, and positional information of limb movements from the video feed. The one or more processors may further extract the features of interest from the video feed and process the extracted features of interest by aligning the extracted features of interest to a common reference.

System and method for automated detection of neurological deficits

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Charlottesville, VA, United States of America

Yan Zhuang