Hybrid Model for Freezing of Gait Detection in Parkinson’s Disease: Integrating Manual Features and Deep Learning

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• Detecting freezing of gait shows enhanced accuracy of 85% in Parkinson’s disease patients compared to traditional methods.
• The hybrid detection model combines manual features with advanced deep learning techniques to achieve its results.
• Application of machine learning methods enables better recognition of gait patterns in Parkinson’s disease patients.
• Improved detection may lead to better management strategies, although the models need further testing in clinical settings.