The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.

We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters.

Many cognitive elements are involved in handwriting. However, it has not yet been clarified how handwriting changes as cognitive ability improves. This study is a preliminary study of the cognitive activity of handwriting and attempts to clarify the changes in handwriting between children and adults. This study investigated the relationship between development changes in writing dynamics and the tendency of attention deficit hyperactivity disorder/autism spectrum disorder (ADHD / ASD) in typical developing participants. This study is a preliminary study of handwritten cognitive activity and attempts to reveal changes in handwriting between children and adults. We also investigated the relationship between ADHD, ASD tendency, and writing dynamics, where cognitive specificity is pointed out, in participants with typical development.A pen tablet was used to examine developmental changes in writing dynamics. Participants included 27 children and 26 adults. They wrote 30 hiragana words dictated by a Japanese woman on a pen tablet. The write process data were collected on writing time, writing pressure, on-screen coordinates, and pen angle, from the writing process. In addition, participants answered a questionnaire to investigate trends in developmental disorders. We analyzed the writing dynamics of hiragana characters to investigate the differences between children and adults. They differed in writing speed which suggested developmental changes in writing dynamics. Correlation with developmental disorders suggests that ASD and ADHD affect writing dynamics. Writing dynamics showed the specificity of ASD and ADHD, which suggests that it may serve as an index for screening these conditions.