Towards an Automated Analysis of Neuroleptics´ Impact on Human Hand Motor Skills

The fact that neuroleptics may have a more or less noticeable influence on fine motor skills is well-known. However, up to now there is no system that allows one to measure and to quantify such an impact of neuroleptics or other drugs. This article goes a first step into this direction by demonstrating how the handwriting dynamics of a healthy person can automatically be distinguished from that of a schizophrenic and, therefore, appropriately medicated person. Moreover, it is shown that differences can be detected even for a very simple kind of hand movement. That is, the persons trace a given meander. The handwriting dynamics are measured by means of a pen equipped with force and tilt sensors (biometric smart pen). Then, the parameterized script generator model proposed by Hollerbach is used in order to extract characteristic features from the measured signals, e.g., features describing deviations of measured time series from predicted model time series. These features are then used as inputs of support vector machines that classify whether the handwriting has been provided by a healthy or a diseased person