DhilipSanjay/Human-Biomechanic-Analysis
Deep Learning Models for the Early Detection of Parkinson’s Disease using the motor-based symptoms.
This project helps medical professionals, researchers, and caregivers with the early detection and severity assessment of Parkinson's Disease (PD). It takes motor-based data from gait sensors under the feet and acceleration sensors on the body, then outputs whether PD is detected, the severity of the disease (using the Hoehn Yahr Scale), and if freezing of gait is present. The end user is likely a clinician, neurologist, or a research scientist studying movement disorders.
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Use this if you need to analyze motor symptoms from sensor data to identify Parkinson's Disease early or to quantify its severity.
Not ideal if you require a diagnostic tool based on non-motor symptoms or if your data input isn't from ground reaction force and acceleration sensors.
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Feb 19, 2022
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