Assessing handwriting task difficulty levels through kinematic features: a deep-learning approach

Frontiers in Robotics and AI

Robotics and AI

Haneen Alsuradi¹  Mohamad Eid¹  Muhammad Hassan Jamil¹  Vahan Babushkin²  Muhamed Osman Al-Khalil³ 

[1] Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;Applied Interactive Multimedia Lab, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;Tandon School of Engineering, New York University, New York, NY, United States;Arabic Studies Program, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;

关键词: artificial neural networks;    deep learning;    learning from demonstration;    machine learning;    sensorimotor learning;   

DOI  :  10.3389/frobt.2023.1193388

 received in 2023-03-24, accepted in 2023-08-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Handwriting is a complex task that requires coordination of motor, sensory, cognitive, memory, and linguistic skills to master. The extent these processes are involved depends on the complexity of the handwriting task. Evaluating the difficulty of a handwriting task is a challenging problem since it relies on subjective judgment of experts.Methods: In this paper, we propose a machine learning approach for evaluating the difficulty level of handwriting tasks. We propose two convolutional neural network (CNN) models for single- and multilabel classification where single-label classification is based on the mean of expert evaluation while the multilabel classification predicts the distribution of experts’ assessment. The models are trained with a dataset containing 117 spatio-temporal features from the stylus and hand kinematics, which are recorded for all letters of the Arabic alphabet.Results: While single- and multilabel classification models achieve decent accuracy (96% and 88% respectively) using all features, the hand kinematics features do not significantly influence the performance of the models.Discussion: The proposed models are capable of extracting meaningful features from the handwriting samples and predicting their difficulty levels accurately. The proposed approach has the potential to be used to personalize handwriting learning tools and provide automatic evaluation of the quality of handwriting.

【 授权许可】

Unknown   
Copyright © 2023 Babushkin, Alsuradi, Jamil, Al-Khalil and Eid.