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In today's world, there is a high pressure to change lifestyle, which is increasing the incidence of neurological diseases, such as Parkinson's disease. To assess motor dysfunction in these patients, approaches based on markerless motion capture (MMC) technology have been tested in recent years. Despite the high sampling rate and accuracy of commercial depth sensors such as the Leap Motion Controller (LMC), their versatile use is limited due to irregular sensing or processing errors. These affect their reliability and question clinically meaningful data. To mitigate the impact of errors during measurements, we introduce visual feedback for the specialist physician in the form of a real-time display of the measurement data recorded by the LMC. In this proof-of-concept study, we evaluate data from 10 patients with Parkinson's disease and 12 healthy subjects during the finger tapping test (FTT). To verify the suitability of using the LMC sensor for this purpose, we validate the results by simultaneous measurement with digital camera and two contact sensors: an accelerometer and two three-axis gyroscopes placed on the fingertips. The preliminary results confirmed the effectiveness of introducing visual feedback when performing FTT by reducing the impact of LMC sensor failure by 4.3%. Additionally, we used machine learning techniques to determine the clinical relevance of the measured and extracted features, achieving an average classification accuracy of 90.41%. [ABSTRACT FROM AUTHOR]

Reconstruction of a 3D eye model by photogrammetry from a smartphone video could be prospectively used in self-diagnosis, screening and telemedicine monitoring of diseases of the front part of the eye and its surroundings. The main use could be found in the treatment of diseases of the curvature and surface of the cornea and in follow-up after some refractive procedures. In our work, we create 3D image-based models of the eye after scanning the face with a smartphone. An unexpected phenomenon appeared during the reconstruction of the transparent cornea – a crater-like depression was formed at the place where nearby objects reflected on the cornea, which corresponds to the first Purkinje image, the so-called glint. We thus encountered complications that may arise when modelling transparent living structures from a video taken in a normal environment, which will need to be solved if we want to create such 3D models of the eye using this method for medical purposes. Another 3D reconstruction approach or additional algorithms must be considered as a future work. [ABSTRACT FROM AUTHOR]