Acta neurologica Belgica · 2024
Quantifying abnormal writing kinematics in writer's cramp using a novel software platform.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Vysakha Kavadisseril Vivekanandan, Radhakrishnan Vineeth, James Praveen, Kumar B Suresh, Susvirkar Ashish Anand, Sarma Gangadhara, Cherian Ajith, Divya K P, Nair Syam Krishnan, Kishore Asha
Аннотация
BACKGROUND: Writer's cramp is a task-specific focal hand dystonia, which is diagnosed clinically. Quantification of defect in WC is done using clinical scales, while digitized platforms are lacking.
OBJECTIVE: To design and test a platform that can differentiate and quantify the abnormal kinematics of writing using a software interface and to validate it in adult-onset isolated writer's cramp (WC).
METHODS: A native platform was designed using Java and Wacom Intuos pro tablet and the data analyzed using a MATLAB-based platform called Large Data-Based Evaluation of Kinematics in Handwriting (LEKH). We standardized this new platform by comparing the handwriting between patients with WC and age, and gender and education-matched healthy controls, using standard tasks to assess the kinematics.
RESULTS: Comparison of the writing of right-handed WC patients (N = 21) and 39 healthy controls (N = 39) showed that patients differed from controls in the frequency of strokes (P < 0.001), number of inversions of velocity (P < 0.001), number of breaks (P = 0.02), air time and paper time (P < 0.001).
CONCLUSIONS: Using the LEKH platform, the kinematic profile of patients with WC could be differentiated from healthy controls. Studies in larger samples will be needed to derive statistical models that can differentiate the flexion and extension types of WC which can help in muscle selection and to quantify the effects of treatment.
OBJECTIVE: To design and test a platform that can differentiate and quantify the abnormal kinematics of writing using a software interface and to validate it in adult-onset isolated writer's cramp (WC).
METHODS: A native platform was designed using Java and Wacom Intuos pro tablet and the data analyzed using a MATLAB-based platform called Large Data-Based Evaluation of Kinematics in Handwriting (LEKH). We standardized this new platform by comparing the handwriting between patients with WC and age, and gender and education-matched healthy controls, using standard tasks to assess the kinematics.
RESULTS: Comparison of the writing of right-handed WC patients (N = 21) and 39 healthy controls (N = 39) showed that patients differed from controls in the frequency of strokes (P < 0.001), number of inversions of velocity (P < 0.001), number of breaks (P = 0.02), air time and paper time (P < 0.001).
CONCLUSIONS: Using the LEKH platform, the kinematic profile of patients with WC could be differentiated from healthy controls. Studies in larger samples will be needed to derive statistical models that can differentiate the flexion and extension types of WC which can help in muscle selection and to quantify the effects of treatment.