A survey of visual and procedural handwriting analysis for neuropsychological assessment

Abstract

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

Characteristics of Drawing Process Differentiate Alzheimer’s Disease and Dementia with Lewy Bodies

“…However, to the best of our knowledge, there has been no such investigation for identification of DLB or differentiation between AD and DLB, which we addressed in this study. In addition, several recent studies have investigated local temporal/spatial pattens within a drawing task, by using either hand-crafted, task-specific features (e.g., pauses before drawing numbers or hands in the CDT) [ 54, 55, 57 ], or machine-learned features extracted automatically through neural networks [ 61 ]. Those approaches differ from ours that used global features to capture the overall profile of the drawing process for each task.…”

Dhad—A Children’s Handwritten Arabic Characters Dataset for Automated Recognition

“…An input character would be compared to these templates, with the closest match assigned as the recognized character. However, this approach proved ineffective for children's handwriting due to its inherent lack of conformity [19,20]. However, there are a number of limitations identified in the template matching approaches, particularly for characters with significant variations in form.…”

Tools and Methods for Diagnosing Developmental Dysgraphia in the Digital Age: A State of the Art

“…Several types of variables can be analyzed, depending on the tools used for the evaluation: posture, finger and arm movements, pen grip and finger pressure on the pen, in-air and on-paper durations, pen velocity, pen pressure, etc. The increasing number of publications on the analysis of the handwriting process over the past years attests to the growing interest of researchers in this field (e.g., [39][40][41][42][43]).…”

“…Since dysgraphia is a very heterogenous disorder encompassing a large variety of difficulties, it would be interesting to think about developing a reliable, comprehensive, and universal diagnostic tool for dysgraphia, combining computer and paper-and-pen tools. The main diagnostic features assessed with the paper-and-pen tools can be complemented with the computerized tools, which would provide precise information about the specific handwriting difficulties of each child (for a review, see [43]).…”