Paper Reading AI Learner

Assessment of Autism and ADHD: A Comparative Analysis of Drawing Velocity Profiles and the NEPSY Test

2024-01-28 16:02:27
S. Fortea-Sevilla, A. Garcia-Sosa., P. Morales-Almeida, C. Carmona-Duarte

Abstract

The increasing prevalence of Autism Spectrum Disorder and Attention-Deficit/ Hyperactivity Disorder among students highlights the need to improve evaluation and diagnostic techniques, as well as effective tools to mitigate the negative consequences associated with these disorders. With the widespread use of touchscreen mobile devices, there is an opportunity to gather comprehensive data beyond visual cues. These devices enable the collection and visualization of information on velocity profiles and the time taken to complete drawing and handwriting tasks. These data can be leveraged to develop new neuropsychological tests based on the velocity profile that assists in distinguishing between challenging cases of ASD and ADHD that are difficult to differentiate in clinical practice. In this paper, we present a proof of concept that compares and combines the results obtained from standardized tasks in the NEPSY-II assessment with a proposed observational scale based on the visual analysis of the velocity profile collected using digital tablets.

Abstract (translated)

在学生中Autism Spectrum Disorder(ASD)和Attention-Deficit/ Hyperactivity Disorder(ADHD)的发病率不断增加,这凸显了需要改进评估和诊断技术以及有效工具来减轻这些疾病带来的负面后果。随着智能手机的广泛应用,可以收集到除视觉线索之外更全面的數據。这些设备可以收集和可视化关于速度曲线的信息,以及完成绘图和书写任务所需要的时间。这些数据可以用于开发新的神经心理学测试,基于速度曲线,用于区分在临床实践中难以区分的挑战性ASD和ADHD病例。在本文中,我们提出了一个概念证明,将标准化任务在NEPSY-II评估中获得的結果与基于数字平板收集的速度曲线下进行的视觉分析提出的观察尺度进行比较和結合。

URL

https://arxiv.org/abs/2401.15685

PDF

https://arxiv.org/pdf/2401.15685.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot