Personlighetstyper och upplevelse av ett nytt arbetsstöd : undersökning gjord på Trafikverkets Intranät och anställda

In order to get a better understanding of the interaction between employees and their technical work tools one needs to know what factors influence the interaction. The purpose of this study was to examine if there is a correlation between the personality traits Extraversion (E) and Neuroticism (N), tested with Eysenck Personality Inventory (EPI), and experience of the new Intranet among employees at The Swedish National Transport Administration (SNTA), and also to gather information of employees’ opinions about the new Intranet. A survey, containing questions about the Intranet and a personality test (EPI), was posted on SNTA’s Intranet for eight workdays (N = 88, females = 53, males = 35). A Multiple Regression showed no significant correlations between personality traits (E/N) and experience of the new Intranet. Considering the study’s low Power (.34) one cannot draw any conclusions of the statistical tests. A majority of the participants did not think that the new Intranet is better than the old one, and thought it was difficult to find necessary information on the Intranet at first. However, they did not think this had a negative effect on the time it took to accomplish their work tasks. For upcoming studies more participants are required (preferable more than 200) and the survey should not only be available via computers in order to reach people who is not frequent users of computers.

A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings