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.

Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.