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Pupil Size Derived Features Improve the Accuracy of Eye-Tracker Based MCI Classification
No full textEye tracking has shown promise in detecting groupwise differences between healthy controls and people with Mild Cognitive Impairment (MCI) who might progress to develop Alzheimer's disease. As there is currently no cure, only medications to slow the progress of the disease, it is of paramount importance to find the people at risk early. We extracted saccade- and Region of Interest (ROI)-centric feature sets from a clinical eye tracking dataset and analyzed the results using machine learning to establish which features were most beneficial to correctly classify individuals at risk. Our results show that the analysis of multimodal eye tracking recordings of number and text reading tasks produces feature sets that can have good predictive value to classify MCIs from healthy controls. Changes in participants' pupil sizes from the personalized baseline appear to be especially promising candidates for improving the classification efficiency of MCIs
Den finländska justitiekanslern som ändringssökande i advokatdisciplinära ärenden
No full textI artikeln analyseras justitiekanslerns rätt och skyldighet att anföra besvär över tillsynsnämndens disciplinära beslut i advokatärenden. Justitiekanslern kan och ska anföra besvär om det allmänna intresset föranleder det. Trots detta anses justitiekanslern i vissa fall anföra besvär utan att det allmänna intresset motiverar det, och i enskilda fall kan besvären till och med framstå som oförenliga med det allmänna intresset. Mot denna bakgrund syftar artikeln till att fastställa när och hur justitiekanslerns besvärsrätt bör utövas. Slutsatsen som dras i artikeln är att justitiekanslern endast bör anföra besvär i fall där det allmänna intresset kräver eller berättigar det. Vidare bör justitiekanslern i besvärsskriften på ett öppet och konkret sätt motivera hur det allmänna intresset gör sig gällande i det enskilda fallet. Dessutom bör justitiekanslern iaktta proportionalitet i användningen av domstolsväsendets resurser och beakta att resurserna för rättsskydd är begränsade
Literacy skills and reading habits of 15-year-olds in Finland: The significance of socioeconomic factors and gender considering the PISA 2018 study
No full textBook of abstracts : the 4th international conference for sustainable resource society. Environmental monitoring and impact assessment
No full textSynthetic images of skeletal motion for pose and kinematics estimation tasks
No full textThis dataset contains automatically annotated synthetic images of human kinematics. There are 50,000 images of real motion and 50,000 images of randomized motion. The data is uploaded in ZIP archives of 10,000 images and corresponding annotations, labels, and metadata. The ZIP archives are named "Output_real_X" or "Output_random_X" for images generated from real human kinematics (analyzed from motion capture data) and random-generated kinematics, respectively. The index X ranges from 1 to 5; the archive with index 1 contains the first 10,000 images, index 2 the second 20,000 images, and so on. Each "image" consists of an RGB image, a silhouette mask image, and several body segment mask images in 1024x1024 resolution. Each image contains a single person in a varied pose, skin and clothing texture, background, and viewer perspective. Annotations are generated from musculoskeletal modeling and simulation. They include positions of joints, bodies, and virtual markers of the musculoskeletal model. Positions are given as the 2D coordinates in the image and the depth of the position in meters, measured from the perspective of the viewer. Each position also has a visibility value between 0 and 1, where 0 represents complete occlusion and 1 represents clear visibility. Additionally, each image is annotated with a 2D bounding box enclosing the person. Labels include generalized coordinates of the musculoskeletal model, i.e., joint angles. Annotations and labels are contained in annotations/annotations.csv, which has the following columns: file_names: name of the RGB image without file suffix (.jpg) jp_hip_r_x, jp_hip_r_y, jp_hip_r_z etc: annotations of joint positions all fields start with "jp_" followed by the name of the joint (i.e., "hip_r" which is the right hip) and ending with "_x", "_y", or "_z" x and y coordinates are given in image pixels z is depth of the position and given in meters bp_femur_r_x, bp_femur_r_y, bp_femur_r_z etc: annotations of body positions fields have similar naming convention as above, but starting with "bp_" vm_fibula_head_r_x, vm_fibula_head_r_y, vm_fibula_head_r_z etc: annotations of virtual marker positions fields have similar naming convention as joint positions and body positions, but starting with "vm_" visibility_jp_hip_r, visibility_bp_femur_r, visibility_vm_fibula_head etc: visibility values of annotations bb_x, bb_y, bb_w, bb_h: 2D bounding boxes given in pixels as x, y, width, height gc_hip_flexion_r, gc_hip_adduction_r, gc_hip_rotation_r etc: labels of generalized coordinates, given in radians Additional information is provided in annotations/metadata.csv, which has the following columns: image_name: same as file_names in annotations.csv sex: sex of the human mesh used to create the synthetic person in the image, where 0=male and 1=female weight: index of the body shape morphology of the human mesh, where different indices indicate different body shapes background: index of the HDRI background of the image frame: frame of kinematics used to set the pose of the human in the image clothing: index of the clothing texture of the human in the image skin: index of the skin texture of the human in the image xrot: rotation of the viewer perspective around the horizontal axis, in degrees yrot: rotation of the viewer perspective around the vertical axis, in degrees fov: field of view of the viewer perspective, in degrees See the GitHub repository (https://github.com/jerela/Godosim) if you want to generate your own annotated images. A data paper describing the data generation workflow is in progress. Contact: Jere Lavikainen, [email protected]
Detection of large European aspen from LiDAR-point cloud by using Artificial Intelligence
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