{"id":477,"date":"2025-03-26T02:38:25","date_gmt":"2025-03-26T02:38:25","guid":{"rendered":"https:\/\/coeng.uaa.alaska.edu\/AI\/?page_id=477"},"modified":"2025-03-26T02:38:26","modified_gmt":"2025-03-26T02:38:26","slug":"raise-freezing-of-gait-detection","status":"publish","type":"page","link":"https:\/\/coeng.uaa.alaska.edu\/AI\/raise-freezing-of-gait-detection\/","title":{"rendered":"RAISE – Freezing of Gait Detection"},"content":{"rendered":"
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\nUAA College of Engineering\n<\/div>

\nAdvancing Freezing of Gait Detection in Parkinson's Disease\n<\/h1>
\nA project led by Masoumeh Heidari Kapourchali\n<\/div>\n<\/div>
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\"UAA<\/div>\n<\/section>
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\n \n \n\t\n\n\n\n\"Parkinson\n<\/div>
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\nAdvancing Freezing of Gait Detection\n<\/h1>
\nWearable Sensors and Machine Learning Enhance Patient Monitoring\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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\nFreezing of gait (FoG) is a debilitating symptom affecting 38-65% of Parkinson's disease patients, significantly impacting their mobility and independence. This project led by Masoumeh Heidari Kapourchali has made significant strides in improving FoG detection using wearable sensors and machine learning techniques.

The project utilizes multiple wearable sensors placed on different parts of the body to analyze movement patterns specific to each individual with Parkinson's disease. This personalized approach allows the system to adapt to the unique ways FoG manifests in different patients, enhancing detection accuracy.

By employing advanced machine learning algorithms, the model learns from past movement patterns and continuously refines its detection capabilities over time. This adaptive learning process has resulted in more effective identification of gait freeze episodes, making it an invaluable tool for patient monitoring and management.

The research team also focused on practical considerations for real-world application. They tested the model's resilience to sensor failures and its ability to maintain performance as the number of sensors increased, ensuring its reliability for real-time patient monitoring.

This innovative approach provides more accurate and objective measurements of FoG episodes, it also offers clinicians valuable insights for treatment adjustments and disease progression monitoring. Moreover, it opens new avenues for research into the underlying mechanisms of FoG and potential interventions.\n<\/div><\/div>\n<\/section>
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\nFrequently Asked Questions\n<\/h1>
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