Analysis of hand and face images for the purpose of engineering support for Parkinson's disease diagnosis

Abstract

Engineering support in the field of recognizing Parkinson's disease against the background of other diseases, its progression and monitoring the effectiveness of drugs is currently widely implemented as part of work devoted to the use of re-cording and analysis devices equipped with sensors of movement parameters attached to the patient's body, e.g. accelerometers and gyroscopes. This material touches on an alternative approach, in which the concept of using techniques for processing selected image data obtained during a clinical examination evaluating a patient using the unified UPDRS number scale is proposed. The research was conducted on a material that corresponded to selected components of the scale and included images of faces recorded in the visible light range and images of the outer surfaces of the hand recorded with a thermal imaging camera. This was aimed at assessing the possibility of differentiating persons in terms of detecting Parkinson's disease on the basis of registered modalities. Thus, tasks aimed at developing characteristics important in the binary classification process were carried out. The assessment of features was made in a modality-dependent manner based on available tools in the field of statistics and machine learning.