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Parkinetics: Better Monitoring of Parkinson’s Disease Patients
- Program: Biomedical Engineering
- Course: EN.580.497 Advanced Design Team
- Year: 2025
Project Description:
Structured assessments of PD lack quantitative measures of functional impairment, remaining highly subjective and variable. Clinicians involved in the long-term management of PD would benefit from a quantitative method to assess patient impairment in the home. To investigate this, nine-axis inertial measurement unit (IMU) sensors were attached to implements used in basic activities of daily living (ADLs), namely brushes and combs. Continuous time-series accelerometer and gyroscope data was collected while people with Parkinson’s Disease (pwPD) performed brushing and combing ADLs. Harnessing temporal and spectral features extracted from time-series data, a linear regression model with L2 regularization achieved a moderately high correlation with patient total score on the Unified Parkinson’s Disease Rating Scale. Additionally, a decision tree machine learning model (XGBoost) trained on signal features accurately performed a classification task, separating participants into healthy control, mild severity PD, and moderate severity PD classes with ~84-88%% accuracy on a held-out test set. Due to the relationship between the patient’s rated severity and features captured by the IMU sensor, this approach has potential use in more objectively characterizing patient functionality longitudinally in the home setting.
Project Photo:
Tools like toothbrushes, spoons, hair combs, and pens can be used to understand how impaired a Parkinson’s patient is.