As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R²) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R² value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

[Picture 1] Textile sensors enable non-invasive health monitoring, crucial for ensuring road safety by conducting mental well-being checks for drivers. Assessing driver stress with multimodal data from textile electrodes requires effectively integrating and interpreting diverse physiological signals and kinematic data. In this study, we evaluated textile electrodes and cross-attention mechanisms for assessing driver stress using multimodal data. Electrocardiography and Respiration data were collected from 15 healthy volunteers wearing smart shirts in two driving scenarios. Signals were sampled at 256Hz and 128Hz, respectively, with vehicle data also recorded. Segmented Physiological & Vehicle data enter separate networks, 1D Convolutional layers for signals and fully connected layers for vehicle data. Cross-attention fuses Physiological data, these features are combined with vehicle data for stress classification using sigmoid. The proposed approach is able to classify driver stress states using multimodal data, achieving an average accuracy of 79% and an average F-score of 75%. The integration of a cross-attention mechanism facilitates the capture of inter-modality information.

Handwriting difficulties can significantly affect education and daily functioning. Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. This narrative review synthesizes recent work on identifying and addressing handwriting difficulties, covering both traditional and technology-based approaches. Rather than proposing a new method or computational framework, it highlights trends, challenges, and opportunities in current research. We systematically reviewed 107 primary studies published between 2019 and 2024, sourced from IEEE Xplore and SpringerLink. Key techniques examined include the use of spatiotemporal signals, image data, feature-extraction methods, and algorithmic classification. We also explored key experimental design elements and assistive interventions aimed at improving handwriting performance. Our review underscores advances in handwriting analysis—particularly the role of technology in assessing and improving handwriting legibility and fluency—and shows how these methods address limitations of conventional assessments. The findings demonstrate the potential of technology-based approaches for assessing and mitigating handwriting difficulties, while revealing ongoing challenges such as limited dataset diversity, the need to integrate cognitive and motor aspects, and inadequate model transparency. Future work should focus on standardizing methods, increasing dataset representation, and developing interpretable, multimodal solutions.