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3D Hand Movement Analysis in Parkinson’s Disease. Ondřej Rozinek Czech Technical University in Prague Faculty of Biomedical Engineering. Outline. Motivation and goals Color calibration Marker detection Camera calibration and 3D reconstruction Movement analysis Conclusion. Block diagram.
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3D Hand Movement Analysis in Parkinson’s Disease Ondřej Rozinek Czech Technical University in Prague Faculty of Biomedical Engineering
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 3D trajectory 2D trajectory from top view 2D trajectory from side view
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
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
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
Markerdetection 1.2 seconds; 30 frames 2.0 seconds; 50 frames Top view Sideview
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
Cameracalibration and 3D reconstruction • For measurements is necessery undistorted image - distorted image coordinates - tangential distortion - camera parameters - new normalized point coordinate Barrel distortion Undistorted
Movementanalysis 2D sideview 3D 2D top view
Movementanalysis standart deviation (S) variationcoefficient (V) range (Rvar) skewness (Sk) kurtosis (Ek)
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