Parkinson's disease (PD) is a neurodegenerative disease, which is becoming an increasingly greater social issue due to the growing incidence rate caused by population ageing. Over the recent years, the doctors have been focusing on searching for new methods supporting the diagnosis of such disorders. Acoustic voice analysis in Parkinson's patients can be a valuable and objective tool supporting the diagnosing diseases of neurodegenerative nature. The article discusses a concept of utilizing voice processing techniques in evaluating patients with Parkinson's disease. Using time analysis, frequency analysis and time-frequency analysis, the authors attempted performing acoustic voice analysis in that group of patients. The research utilized recordings conducted at the Department of Neurology at the Medical University of Warsaw. The study involved both women and men. The recording scenario was divided into several parts. The first part contained two various texts read out by the tested person. Yet another segment involved recording the vowel "a" with prolonged phonation, uttered by a patient for at least 5 seconds. The last part of the recordings involved the patient uttering individual words and sentences according to an assumed scenario. A total of 7 acoustic signals per patient, with an average length of ca. 75 seconds were recorded. The conclusions from the conducted studies will enable determining, which of the applied techniques can be a promising tool for supporting the diagnostics of neurodegenerative disorders, including parkinsonism. Further studies involving larger groups are required in order to confirm the obtained results and the structure of a target diagnostic system.

Parkinson's disease is a relatively common illness, constantly progressing, evoking anxiety and depression resulting from significantly restricted independence. Its dominating symptoms include muscle rigidity, rest tremor, slowness and postural disorders. They can lead to progressing gait disorders, hypomimia, extrapyramidal dysarthria and micrography. Acquisition of data imaging the condition of a patient and their objective evaluation can constitute a valuable tool improving the diagnostics and treatment monitoring. The developed data acquisition set enables recording data while the patient is performing selected tests, based on the UDPRS scale. The set consists of an infrared camera, a camera operating within the visible range, a microphone with a preamplifier, a graphic tablet and a laptop. A set of recording devices controlled by a graphic user interface guarantees the acquisition of data for the purposes of studying primarily motor symptoms. The set of recorded data includes a face image in the visible light range and in infrared for studying hypomimia, video images of the limbs for studying finger tapping movement regularity, hand movement regularity, lower limb agility and gait, and spontaneous and forced speech samples to evaluate the strength of the voice, its timbre and quality. In addition, the used graphic tablet enables collecting handwriting samples for testing writing speed and the force used. The suggested solution enables non-invasive quantitative measurements and archiving multimodal data describing the condition of a patient, which after processing can be used in diagnostics, evaluating treatment effectiveness and studying the progression of the disease.

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.

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.

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