Evaluation of Finger Tapping Test Accuracy using the
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Evaluation of Finger Tapping Test Accuracy using the
Finger Tapping (FT) test is one of the items of the Unified Parkinson's Disease Rating. Scale (UPDRS) ... automatic assesment of FT-UPDRS scores. Here we ...
Evaluation of Finger Tapping Test Accuracy using the LeapMotion and the Intel RealSense Sensors * * * * ‡ ‡ * C. Ferraris , D. Pianu , A. Chimienti , G. Pettiti , V. Cimolin , N. Cau and R. Nerino *Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy ‡Department of Electronics, Information, and Bioengineering, Polytechnic of Milan
Motivation Finger Tapping (FT) test is one of the items of the Unified Parkinson's Disease Rating Scale (UPDRS) commonly used to asses the severity of motor impairments in Parkinson’s Disease. Accurate hand/finger tracking is a prerequisite for an automatic assesment of FT-UPDRS scores. Here we compare the FT tracking accuracy of two novel low-cost commercial LeapMotion® hand tracking systems with respect to our solution.
RealSense®
Method Reference system
Experimental setup
We developed a novel Human Computer Interface (HCI) based on colored gloves and a RGB-Depth camera. During comparison our HCI is used as a RGB-D Camera reference system thanks to its good accuracy previously validated by comparison with an optoColored gloves electronic system.
A volunteer was instructed to perform the FT at increasing speed and maximum amplitude. Due to possible interference between the sensors we performed separate acquisitions for both devices. CNR-HCI (Glove+Kinect)
RealSense
CNR-HCI
LeapMotion CNR-HCI (Glove+RealSense)
Comparison between CNR-HCI and optoelectronic system trajectories (vertically shifted to be easily comparable)
LeapMotion setup
RealSense setup
Results CNR-HCI
LeapMotion: sensible amplitude attenuation af faster FT velocity. CNR-HCI
RealSense: missing closures af faster FT velocity. The accuracy of the LeapMotion and RealSense hand trackers is not sufficient for a reliable reconstruction of FT trajectories and our glove-based HCI approach represents a preferable solution for FT acquisition in the context of PD assessment.
Further Reading C. Ferraris et al. “Remote monitoring and rehabilitation for patients with neurological diseases,” in 10th Int. Conf. on Body Area Networks, London, Sep. 29 - Oct. 1, 2014.
