Digital Trail Making Test: Proposal of the age estimation model using multi-task learning neural network for evaluation of attention

被引：0

作者：

Mukai, Haruto ^{[1
]}

Yorita, Akihiro ^{[2
]}

Mekata, Kojiro ^{[3
]}

Aomura, Shigeru ^{[4
]}

Obo, Takenori ^{[4
]}

Kubota, Naoyuki ^{[4
]}

机构：

[1] Tokyo Metropolitan Univ, Dept Syst Design, Tokyo, Japan

[2] Kwansei Gakuin Univ, Sch Engn, Nishinomiya, Hyogo, Japan

[3] Shijonawate Gakuen Univ, Dept Rehabil, Osaka, Japan

[4] Tokyo Metropolitan Univ, Grad Sch Syst Design, Tokyo, Japan

来源：

2024 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, FUZZ-IEEE 2024 | 2024年

基金：

日本科学技术振兴机构;

关键词：

attentional function; Trail Making Test; machine learning; multi-task learning;

D O I：

10.1109/FUZZ-IEEE60900.2024.10612208

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

"Attention" is like a filter that selects certain information coming in through our eyes, nose, ears, etc. Once attentional function is damaged, it would be difficult to find it attentional disorder based on appearance, and the person himself or herself is not fully aware of the disorder. Trail Making Test (TMT) can determine attentional function, but it is difficult to know the degree to which it is normal. Based on the above, the technical challenges of this research are set as (1) the development of a digital TMT (d-TMT) that can be completed with a single iPad, and (2) the development of a method for evaluating attention functions using the data from d-TMT. This paper summarizes the research results of developing an iPad app and constructing and evaluating a machine learning model based on the hypothesis that it is possible to classify the age groups from time and handwriting information obtained from TMT.

引用

页数：7

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