3D Hand Movement Analysis in Parkinson’s Disease

1. 3D Hand Movement Analysis in Parkinson’s Disease Ondřej Rozinek Czech Technical University in Prague Faculty of Biomedical Engineering

2. Outline • Motivation and goals • Color calibration • Marker detection • Camera calibration and 3D reconstruction • Movement analysis • Conclusion Block diagram

3. Motivation and goals • Task: Are there any changes in patient‘s conditions after a drug was administered? • Solution: 3D video analysis of hand movement 3D trajectory 2D trajectory from top view 2D trajectory from side view

4. Colorcalibration • Correction of the image and so compensate different contrast and brightness conditions • Task of curve fitting • Different color calibration methods are compared: • Linear interpolation (LI) • Cubic Hermite functions (HF) • Multiple linear regression model (MLR) Uncalibrated Calibrated Uncalibrated Calibrated

5. Colorcalibration – multiple linearregression model • Let Y be the matrix of reference colors (image I) and X the corresponding colors of uncalibrated image J t- number of terms MLR (linear combination of color components) n- used colors for color calibration t ≤ n - condition • Disadvantage: multicollinearity of colors: white, grayscale, black 3D transfer function with non-linear terms 3D transfer function with linear terms Blue Blue Red Green Red Green

6. Colorcalibration - evaluation • Root mean square error: - reference values - calibrated values - all squares on the color chessboard black (K), white (W), red (R), green (G), blue (B), cyan (C), magenta (M), yellow (Y), c – number of corresponding colors, t –terms, t ≤ n

7. Markerdetection 1.2 seconds; 30 frames 2.0 seconds; 50 frames Top view Sideview

8. Cameracalibration and 3D reconstruction • Pinhole camera model - image coordinates - world coordinates - camera calibration matrix with intrinsic camera parameters - extrinsic camera parameters • Estimate the camera matrix • Direct linear estimation • Closed-form solution • Estimate the fundamental matrix • relationship between the locations of two cameras • using eight point alghoritm for point correspondences (u, v) for m ≥ 8 (i = 1,…m) Chessboard for point correspondences

9. Cameracalibration and 3D reconstruction • For measurements is necessery undistorted image - distorted image coordinates - tangential distortion - camera parameters - new normalized point coordinate Barrel distortion Undistorted

10. Movementanalysis 2D sideview 3D 2D top view

11. Movementanalysis standart deviation (S) variationcoefficient (V) range (Rvar) skewness (Sk) kurtosis (Ek)

12. Conclusion • Blue markers are proposed • 3D hand trajectory of patients is obtained • Error is 1-3 mm at rest and for slower motion (camera has only 25 frames per second) • Color calibration to obtain the required brightness and contrast for the segmentation • Hand velocity, angle in wrist and some statistic parameters are evaluated • Future plans

13. Thank you for your attention