A feature fusion sequence learning approach for quantitative analysis of tremor symptoms based on digital handwriting
作者：
Highlights：
• An automated essential tremor assessment model based on a drawing task.
• First database of 3 types of tremor tasks in patients with essential tremors.
• The patient's diagnosis was independently scored by multiple neurologists.
• Digital ink sequences were analyzed using a hybrid model of CNN and transformer.
• The system incorporates both sequence features and kinematic handwriting features.
摘要
•An automated essential tremor assessment model based on a drawing task.•First database of 3 types of tremor tasks in patients with essential tremors.•The patient's diagnosis was independently scored by multiple neurologists.•Digital ink sequences were analyzed using a hybrid model of CNN and transformer.•The system incorporates both sequence features and kinematic handwriting features.
论文关键词：Digital ink,Tremor detection,Rating of severity,Feature fusion,Deep learning
论文评审过程：Received 2 November 2021, Revised 9 January 2022, Accepted 25 April 2022, Available online 28 April 2022, Version of Record 12 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117400
