Parkinson Classification & Symptoms Profiling with Accelerometer Data
Inter-University Health Data and AI Inquiry Program project.
- Applied Gaussian mixture model clustering to 400+ participant wrist-worn accelerometer data to classify Parkinson’s disease versus controls.
- Achieved a Silhouette Score of 0.688 without labeled training data.
- Engineered tremor-specific features via bandpass filtering between 3 and 12 Hz and PSD metrics.
- Produced symptom profiles with direct applications in remote health monitoring.