Quantifying abnormal writing kinematics in writer's cramp using a novel software platform

Area of Science:

• Neurology
• Biomechanics
• Digital Health

Background:

• Writer's cramp (WC) is a clinical diagnosis of focal hand dystonia.
• Current quantification methods rely on subjective clinical scales.
• There is a need for objective, digitized tools to assess WC kinematics.

Purpose of the Study:

• To design and validate a novel software platform for differentiating and quantifying abnormal writing kinematics.
• To assess the platform's efficacy in adult-onset isolated writer's cramp (WC) patients.

Main Methods:

• A Java-based platform integrated with a Wacom tablet was developed.
• Data analysis was performed using the MATLAB-based Large Data-Based Evaluation of Kinematics in Handwriting (LEKH) platform.
• Handwriting kinematics of 21 WC patients were compared to 39 age-, gender-, and education-matched healthy controls.

Main Results:

• The LEKH platform identified significant differences in writing kinematics between WC patients and controls.
• Key differentiating factors included stroke frequency, velocity inversions, number of breaks, and air/paper time.
• Statistical significance (P < 0.001) was observed for most kinematic parameters.

Conclusions:

• The LEKH platform successfully differentiates the kinematic profiles of individuals with WC from healthy controls.
• Further research with larger cohorts is needed to develop statistical models for distinguishing WC subtypes (flexion/extension).
• This technology holds potential for guiding muscle selection and quantifying treatment efficacy in WC.