StrokeChange:  computer vision ML-based detection of stroke related facial patterns: towards an in-situ patient recovery/status monitoring system.

Lynne Grewe; Soujanya Ravindra Nayak

doi:10.1117/12.3013118

7 June 2024 StrokeChange: computer vision ML-based detection of stroke related facial patterns: towards an in-situ patient recovery/status monitoring system.

Lynne Grewe, Soujanya Ravindra Nayak

Author Affiliations +

Proceedings Volume 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII; 130570M (2024) https://doi.org/10.1117/12.3013118
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

StrokeChange is a proof-of-concept system that uses computer vision and machine learning to monitor stroke patients in-situ towards the goal of monitoring patient progress and changes. The system uses advanced algorithms and deep learning techniques to analyze and interpret the patient's facial data in real-time. StrokeChange is designed to assist healthcare professionals in remotely monitoring the patient's condition and intervening promptly if necessary. This system has the potential to revolutionize stroke care by affordably improving patient outcomes, while also providing patients with greater independence and reducing the burden on healthcare systems. This innovative system can detect changes in a patient's facial expressions, which may indicate changes in their condition, such as the onset of new symptoms or the worsening of existing ones. Various approaches including detection, classification, and regression to solve this problem are implemented and compared. A dataset was curated for training and testing of StrokeChange. When evaluated on test data, StrokeChange achieved best results on a detection and regression combination, achieving an 98% accuracy on facial area detection (eye/mouth detector), a 1.255 mean average loss, and a “perceived accuracy” of 80.3% on regression of eye patterns, and an 83.3% on regression of mouth patterns. The StrokeChange model is deployed into an Android Application for proof of concept. Results including testing and application outcomes are demonstrated as well as challenging results, problems, and areas of future work.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lynne Grewe and Soujanya Ravindra Nayak "StrokeChange: computer vision ML-based detection of stroke related facial patterns: towards an in-situ patient recovery/status monitoring system.", Proc. SPIE 13057, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII, 130570M (7 June 2024); https://doi.org/10.1117/12.3013118

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