Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management

Abstract

Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes’ spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.

HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics

“…This is the Use case 1 and it was a coordinated Cobot learning task between the two-arms of Cobot and a worker ( Figure 3 ). The monitoring video sequences acquired by the external high-quality camera were processed by a custom toolbox in MATLAB to create activity time-series [ 36 , 37 ].…”

“…The monitoring video sequences were acquired by a high-quality camera, and processed by a custom toolbox in MATLAB in order to create activity time-series [ 36 , 37 , 38 ]. Then, during the pre-processing the activity areas (motion detection) were calculated based on a frame difference method (Frame absolute difference) between two consecutive frames.…”

“…The evolution and coordination of the components in the system (Cobots and human) was modelled by the trajectories of N activity centroids along time. In this sense as in [ 36 , 37 , 38 ] k-means algorithm was selected because the centroids need to capture the information provided by the main activity areas in order to analyse the environment coordination. In addition, as in previous works [ 36 , 37 , 38 , 39 , 40 ] there was essential knowledge on the task (R/H activity distributed by areas, behaviour features,) and also there were critical requirements of real-time.…”

“…In this sense as in [ 36 , 37 , 38 ] k-means algorithm was selected because the centroids need to capture the information provided by the main activity areas in order to analyse the environment coordination. In addition, as in previous works [ 36 , 37 , 38 , 39 , 40 ] there was essential knowledge on the task (R/H activity distributed by areas, behaviour features,) and also there were critical requirements of real-time. Thus, within each frame in the binarized image, the coordinates of the centres of all the activity areas were calculated and then k-means was applied to find the centroids of the activity clusters (centres, with x and y coordinates).…”

Digital Biomarker Identification for Parkinson’s Disease Using a Game-Based Approach

“…The DFT test assesses distal movements in PD and may be used to evaluate motor problems longitudinally. Lopez-de-Ipina Karmele et al [7] developed a unique technique based on the integration of handwriting and neuroimaging data for the early clinical identification and monitoring of ET. A computerized Archimedes' spiral exercise measures fine motor abilities and correlates with ET MRI biomarkers.…”