Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children

A survey of visual and procedural handwriting analysis for neuropsychological assessment

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.

Technology-based methods for the assessment of fine and gross motor skill in children: A systematic overview of available solutions and future steps for effective in-field use

The present review aims at providing researchers and practitioners with a holistic overview of technology-based methods for the assessment of fine and gross motor skill in children. We conducted a search of electronic databases using Web of Science, PubMed and Google Scholar, including studies published up to March 2020, that assessed fine and/or gross motor skills, and utilized technological assessment of varying study design. A total of 739 papers were initially retrieved, and after title/abstract screening, removal of duplicates, and full-text screening, 47 were included. Results suggest that motor skills can be quantitatively estimated using objective methods based on a wearable- and/or laboratory-based technology, for typically developing (TD) and non-TD children. Fine motor skill assessment solutions were; force transducers, instrumented tablets and pens, surface electromyography, and optoelectronic systems. Gross motor skill assessment solutions were; inertial measurements units, optoelectronic systems, baropodometric mats, and force platforms. This review provides a guide in identifying and evaluating the plethora of available technological solutions to motor skill assessment. Although promising, there is still a need for large-scale studies to validate these approaches in terms of accuracy, repeatability, and usability, where interdisciplinary collaborations between researchers and practitioners and transparent reporting practices should be advocated.

iDeLog: Iterative Dual Spatial and Kinematic Extraction of Sigma-Lognormal Parameters

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

Kinematic analysis of fast pen strokes in children with ADHD

In this study, we aimed to determine whether a new measure of fine motor skills, the Pen Stroke Test (PST), can discriminate between children with and without attention-deficit/hyperactivity disorder (ADHD). Twelve children with ADHD and 12 controls age 8-11 were asked to produce handwriting strokes on a digitizer. The sigma-lognormal model derived from the Kinematic Theory of rapid human movements was used to analyze the strokes. Standard measurements of fine motor skills and handwriting were also obtained. Children with ADHD demonstrated poorer motor planning (t₀, D) and execution (nbLog) and greater variability in motor control (SNR/nbLog) than did controls. Parameters extracted from the PST were significantly correlated with performance on other motor and handwriting measures. This study provides preliminary evidence that the PST may be useful as a tool for rapidly detecting motor skill problems in the context of ADHD.

Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

Background

Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson’s disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed.

Aim

The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS.

Methods

Sixteen asymptomatic adults (mean age = 69.1 ± 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180° turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase.

Results

The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808).

Conclusion

The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns.

Cultural entrainment of motor skill development: Learning to write hiragana in Japanese primary school

The aim of the present study was to examine how the social norms shared in a classroom environment influence the development of movement dynamics of handwriting of children who participate in the environment. To look into this issue, the following aspects of the entire period of classroom learning of hiragana letters in Japanese 1st graders who had just entered primary school were studied: First, the structure of classroom events and the specific types of interaction and learning within such environment were described. Second, in the experiment involving 6-year-old children who participated in the class, writing movements of children and their changes over the period of hiragana education were analyzed for each stroke composing letters. It was found that writing movement of children became differentiated in a manner specific to the different types of stroke endings, to which children were systematically encouraged to attend in the classroom. The results provide a detailed description of the process of how dynamics of fine motor movement of children is modulated by the social norms of a populated, classroom environment in a non-Latin alphabet writing system.

Graphonomics and its contribution to the field of motor behavior: A position statement

The term graphonomics was conceived in the early 1980s; it defined a multidisciplinary emerging field focused on handwriting and drawing movements. Researchers in the field of graphonomics have made important contribution to the field of motor behavior by developing models aimed to conceptualize the production of fine motor movements using graphical tools. Although skeptics have argued that recent technological advancements would reduce the impact of graphonomic research, a shift of focus within in the field of graphonomics into fine motor tasks in general proves the resilience of the field. Moreover, it has been suggested that the use of fine motor movements due to technological advances has increased in importance in everyday life. It is concluded that the International Graphonomics Society can have a leading role in fostering collaborative multidisciplinary efforts and can help with the dissemination of findings contributing to the field of human movement sciences to a larger public.