Cognitive Impairment Detection Based on Frontal Camera Scene While Performing Handwriting Tasks

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Federico Candela, Santina Romeo, Marcos Faundez-Zanuy, Pau Ferrer-Ramos
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引用次数: 0

Abstract

Diagnosing cognitive impairment is an ongoing field of research especially in the elderly. Assessing the health status of the elderly can be a complex process that requires both subjective and objective measures. Subjective measures, such as self-reported responses to questions, can provide valuable information about a person’s experiences, feelings, and beliefs. However, from a scientific point of view, objective measures, based on quantifiable data that can be used to assess a person’s physical and cognitive functioning, are more appropriate and rigorous. The proposed system is based on the use of non-invasive instrumentation, which includes video images acquired with a frontal camera while the user performs different handwriting tasks on a Wacom tablet. We have acquired a new multimodal database of 191 elder subjects, which has been classified by human experts into healthy and cognitive impairment users by means of the standard pentagon copying test. The automatic classification was carried out using a video segmentation algorithm through the technique of shot boundary detection, in conjunction with a Transformer neural network. We obtain a multiclass classification accuracy of 77% and two-class accuracy of 83% based on frontal camera images, which basically detects head movements during handwriting tasks. Our automatic system can replicate human classification of handwritten pentagon copying test, opening a new method for cognitive impairment detection based on head movements. We also demonstrate the possibility to identifying the handwritten task performed by the user, based on frontal camera images and a Transformer neural network.

Abstract Image

基于执行手写任务时的正面相机场景的认知障碍检测
诊断认知障碍是一个持续的研究领域,尤其是对老年人。评估老年人的健康状况是一个复杂的过程,需要同时采用主观和客观的测量方法。主观测量,如对问题的自我报告,可以提供有关个人经历、感受和信念的宝贵信息。然而,从科学的角度来看,基于可量化数据的客观测量方法更为合适和严谨,这些数据可用来评估一个人的身体和认知功能。拟议的系统基于非侵入式仪器的使用,其中包括用户在 Wacom 手写板上执行不同手写任务时使用前置摄像头获取的视频图像。我们获得了一个包含 191 名老年受试者的新的多模态数据库,人类专家通过标准的五边形临摹测试将这些受试者分为健康用户和认知障碍用户。自动分类是通过镜头边界检测技术的视频分割算法,结合变形神经网络进行的。基于正面摄像头图像,我们获得了 77% 的多类分类准确率和 83% 的两类分类准确率。我们的自动系统可以复制人类对手写五边形抄写测试的分类,为基于头部运动的认知障碍检测开辟了一种新方法。我们还展示了根据正面摄像头图像和 Transformer 神经网络识别用户所执行的手写任务的可能性。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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