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

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

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

Color calibration Correction of the image and so compensate different contrast and brightness conditions Task of curve fitting Different color calibration methods are compared: 1. Linear interpolation (LI) 2. Cubic Hermite functions (HF) 3. Multiple linear regression model (MLR) Uncalibrated Calibrated

Color calibration – multiple linear regression 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 Blue RedGreen 3D transfer function with linear terms 3D transfer function with non-linear terms Blue RedGreen 5

Color calibration - evaluation Used color for calibration c E cal 1D transfer functions3D transfer function MLR(t) LIHFMLR3MLR7MLR10MLR13 K, W xxxx R, G, B xxx C, M, Y xxx C, M, Y, K xxx R, G, B, K, W xxx C, M, Y, K, W xxx all 24 colored squares without calibration159.8 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 Root mean square error: - reference values - calibrated values - all squares on the color chessboard

Marker detection 1.2 seconds; 30 frames2.0 seconds; 50 frames Top view Side view

Camera calibration 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

Camera calibration and 3D reconstruction Barrel distortionUndistorted For measurements is necessery undistorted image - distorted image coordinates - tangential distortion - camera parameters - new normalized point coordinate 9

Movement analysis D side view 2D top view 3D

Movement analysis Motionabcde S V R var SkSk EkEk standart deviation ( S ) variation coefficient ( V ) range ( R var ) skewness ( S k ) kurtosis ( E k )

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

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