Details

Autor(en) / Beteiligte

• Kim, Jeonghee
• Wichmann, Thomas
• Inan, Omer T.
• DeWeerth, Stephen P.

Titel

Fitts' Law Based Performance Metrics to Quantify Tremor in Individuals With Essential Tremor

Ist Teil von

• IEEE journal of biomedical and health informatics, 2022-05, Vol.26 (5), p.2169-2179

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts' law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasks-a spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)-with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts' law), path efficiency (PE), and completion time (CT). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R 2 = 0.57, and R 2 = 0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R 2 = 0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individual's tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts' law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.