2025
doi: 10.12720/jait.16.4.539-544
A Depression Severity Prediction Model by Handwriting
Tanabe Hiroto*
,
Kimura Masaomi
- Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo, Japan
- Manuscript receivedJanuary 7, 2025
- revisedJanuary 23, 2025
- acceptedFebruary 13, 2025
- publishedApril 16, 2025
Abstract
In this study, we propose a model for determining depression severity through handwriting analysis. Traditional diagnostic methods rely on subjective communication between doctor and patient, which can lead to varied interpretations and prolonged diagnostic timelines. It is expected that handwritten data can be used to objectively diagnose depression. We utilize Residual Neural Network (ResNet) and Gradient-weighted Class Activation Mapping++ (Grad-CAM++) to identify regions of interest in handwriting images, associating these with normalized handwriting speed, which has been shown to correlate with depressive states. Experimental results showed that the model’s region of interest focuses on the slow rather than the fast rate part. This model approach facilitates early and efficient detection of depression by making the process more accessible and minimizing the need for specialized equipment.
Keywords
- depression
- handwriting analysis
- Convolutional Neural Network (CNN)
- Gradient-weighted Class Activation Mapping++ (Grad-CAM++)
- Residual Neural Network (ResNet)
How to Cite
Tanabe Hiroto and Kimura Masaomi, "A Depression Severity Prediction Model by Handwriting," Journal of Advances in Information Technology, vol. 16, no. 4, pp. 539-544, 2025. https://doi.org/10.12720/jait.16.4.539-544
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
