## 1 Introduction  
The emergence of affordable digital tablets over the past decade has created new opportunities for identifying cognitive and motor control differences among populations through drawing, tracing, and handwriting tasks [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. Compared to conventional pen-and-paper assessments, which limit analysis to the final product, tablet computers allow researchers to study the process of drawing by capturing continuous changes in pen position, pen-on-tablet pressure, and the stylus azimuth or tilt. These data give rise to efficient, computer-based assessment of graphomotor activities, circumventing the need for time-consuming, subjective judgment of conventional pen-and-paper acquired data [13]. For tablet data, various algorithms have been proposed to detect subtle dynamic, kinematic, and static markers of handwriting/drawing difficulties secondary to impairments of perceptual motor control. These brushstroke-derived biomechanical quantities have expanded our understanding of graphomotor processes and set the stage for machine learning-based detection of handwriting or drawing disorders due to developmental differences or disease [2; 6; 7; 8; 12]. For example, [PERSON] et al. [2], were able to identify 53 measurable brushstroke features (such as in air-time ratio, drawing velocity, essential tremor, among others) that together characterized fine motor control difficulties in children with dysgraphia. Most tablet-based handwriting/drawing studies have focused on identifiable features within brushstrokes. However, the actual segmentation, i.e., automatically identifying from the recorded data the end of one stroke and the beginning of the next, are vaguely described, if at all. Brushstroke segmentation is a critical step in the preprocessing of drawing or handwriting data; errors in brushstroke identification propagate to brushstroke-derived measures, which would ultimately yield inaccurate evaluations of task performance. Few authors have presented approaches to segmenting brushstrokes [1; 2; 14; 15; 16]. Spatial segmentation approaches have categorized strokes based on spatial features such as local minimum/maximal coordinates or have deconstructed characters into predefined components [14; 16; 17]. [PERSON] et al. [16] predefined the strokes needed to trace letters; for example, the letter \"A\" was decomposed into three lines (two slanted and one horizontal). However, this method did not account for cases where the user writes the letter using a different number of strokes. In alternative schemes, [PERSON] et al. [1; 2] segmented strokes using predefined bin sizes of sampled points while others separated strokes according to predefined temporal (e.g., 5-10 s segments [14]) rather than spatial boundaries. By adopting a predefined segmentation approach, these algorithms ignored pen lifts that may have occurred within segments. The multiplicity of pen lifts and greater in-air duration has previously yielded insight into cognitive or motor-processing difficulties [10; 11; 15; 18; 19]. Other segmentation strategies in the literature include separating written strokes by identifying points in time where the velocity was equal to zero, i.e., directional changes [20; 21]. Alternatively, strokes have also been segmented at timepoints where the velocity and acceleration was 0 [14]. However, as participants may pause without lifting the pen, this condition alone is not sufficient to delineate strokes. An alternative is to explicitly instruct participants to start or stop drawing continuously, and observe the time when the pen touches the tablet [4; 22; 23]. However, explicit start/stop instructions detract from naturalistic drawing and may be difficult for children with cognitive disabilities to comply with. To avoid issues associated with predefining strokes, time points that indicate specific fluctuations in applied pen pressure have been used to distinguish segments [16; 22].