Real-time visual verification of leap motion controller measurements for reliable finger tapping test in Parkinson’s disease

Abstract 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%.