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Engineering support in the field of distinguishing Parkinson's disease from other diseases, diagnosing its progression and monitoring the effectiveness of drug treatment is nowadays implemented by way of recording and analyzing equipment fitted with motion sensors. The time series they provide enable quantitative evaluation of a set of symptoms describing daily activities and motor abilities of patients. The paper presents the preliminary results of fundamental research, which based on known medical observations indicating the diminution of facial expressions and micrographic apart from general motor deterioration, suggest that the clinical studies could utilize the techniques of processing image data acquired during the medical history taking. The image data includes video recording of the face and limbs conducted in the course of the coercions suggested in the study and manual drawings by patients. The image data are redundant and require processing for presentations facilitating their interpretation by a physician and enabling efficient utilization of machine learning algorithms in the next study stage. Within the framework of preliminary processing of acquired images, attempts were made to determine the quantitative measures, such as, e.g. blinking frequency and the indicators generated as a result of analyzing the position of characteristic points within the facial image. In the case of limbs, it is suggested to reproduce the motion on the image using a time series acquired thanks to the fixed markers. Preliminary processing of data coming from a graphic tablet also guarantees the generation of time series for images created by patients.

Selected problems of image data preprocessing used to perform examination in Parkinson’s disease