A feature fusion sequence learning approach for quantitative analysis of tremor symptoms based on digital handwriting. (1st October 2022) - Ma, Chenbin; Zhang, Peng; Pan, Longsheng; Li, Xuemei; Yin, Chunyu; Li, Ailing; Zong, Rui; Zhang, Zhengbo
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. Abstract: Essential tremor and Parkinson's disease are common movement disorders, and early diagnosis and evaluation are critical to managing these diseases. Currently, laboratory tests are the only way to diagnose and assess tremor symptoms. Analysis of a patient's fine motor control, especially handwriting, is a powerful tool for disease assessment. However, traditional visual assessment methods by neurologists typically lead to biased diagnostic results due to some subjective factors. Therefore, it is necessary to automatically identify and quantify the captured motion events with the help of artificial intelligence in combination with the various dynamic attributes encapsulated in the digital ink features, such as pen pressure, stroke speed, handwriting variability, etc. In this paper, a novel Transformer deep-learning model is developed for sequence learning of electronic handwriting to effectively evaluate its potential in aiding the diagnosis of tremor symptoms. The one-dimensional convolution with an ingenious fusion attention mechanism is applied to the original pen sensor signal sequences and derived features are used as the embedding layer of the Transformer encoder part, and the global dynamic features are fused before the decision layer. Our proposed system performs excellent on private datasets and outperforms state-of-the-art methods on the PaHaW dataset.