Applying a Handwriting Measurement Model for Capturing Cognitive Load Implications Through Complex Figure Drawing

Assessment of children's writing features: A pilot method study of pen-grip kinetics and writing surface pressure

The writing process is a complex task involving dexterous manipulation of the writing instrument by the hand digits and biomechanical ergonomic factors that contribute to handwriting efficiency and productivity. We describe a pilot study using an instrumented writing apparatus - a sensor (pen) and a digitized writing surface (tablet) - to measure the pen-grip kinetics (digit forces) and the pen pressure applied to the tablet during a writing task. Eight elementary school students with no handwriting difficulties copied a short story. The mean digit forces on the pen were compared with the mean pen pressure on the tablet at five interval points. Results revealed that the digit forces on the pen were significantly stronger than the pen pressure on the tablet. Results also showed significantly less digit-force variability throughout the writing task than the pen-pressure variability on the writing surface, which significantly lessened toward the end of the writing task. Information on these properties can broaden understanding of the elements that influence nonproficient handwriting in children with dysgraphia. Results also indicate the possible efficacy of a therapeutic tool for handwriting assessment and intervention using objective measurements during writing, warranting future studies with children with and without dysgraphia.

Development of Laterality and Bimanual Interference of Fine Motor Movements in Childhood and Adolescence

Drawing and handwriting are fine motor skills acquired during childhood. We analyzed the development of laterality by comparing the performance of the dominant with the nondominant hand and the effect of bimanual interference in kinematic hand movement parameters (speed, automation, variability, and pressure). Healthy subjects (n = 187, 6-18 years) performed drawing tasks with both hands on a digitizing tablet followed by performance in the presence of an interfering task of the nondominant hand. Age correlated positively with speed, automation, and pressure, and negatively with variability for both hands. As task complexity increased, differences between both hands were less pronounced. Playing an instrument had a positive effect on the nondominant hand. Speed and automation showed a strong association with lateralization. Bimanual interference was associated with an increase of speed and variability. Maturation of hand laterality and the extent of bimanual interference in fine motor tasks are age-dependent processes.

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.

Quantitative Analysis of Relationship Between Hypokinetic Dysarthria and the Freezing of Gait in Parkinson's Disease

Hypokinetic dysarthria (HD) and freezing of gait (FOG) are both axial symptoms that occur in patients with Parkinson's disease (PD). It is assumed they have some common pathophysiological mechanisms and therefore that speech disorders in PD can predict FOG deficits within the horizon of some years. The aim of this study is to employ a complex quantitative analysis of the phonation, articulation and prosody in PD patients in order to identify the relationship between HD and FOG, and establish a mathematical model that would predict FOG deficits using acoustic analysis at baseline. We enrolled 75 PD patients who were assessed by 6 clinical scales including the Freezing of Gait Questionnaire (FOG-Q). We subsequently extracted 19 acoustic measures quantifying speech disorders in the fields of phonation, articulation and prosody. To identify the relationship between HD and FOG, we performed a partial correlation analysis. Finally, based on the selected acoustic measures, we trained regression models to predict the change in FOG during a 2-year follow-up. We identified significant correlations between FOG-Q scores and the acoustic measures based on formant frequencies (quantifying the movement of the tongue and jaw) and speech rate. Using the regression models, we were able to predict a change in particular FOG-Q scores with an error of between 7.4 and 17.0 %. This study is suggesting that FOG in patients with PD is mainly linked to improper articulation, a disturbed speech rate and to intelligibility. We have also proved that the acoustic analysis of HD at the baseline can be used as a predictor of the FOG deficit during 2 years of follow-up. This knowledge enables researchers to introduce new cognitive systems that predict gait difficulties in PD patients.

A Bayesian Assessment of Real-World Behavior During Multitasking

Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monitored in healthy individuals with a wearable sensor network during single and multitask conditions. Motor performance was quantified by the median frequencies (fm) of hand trajectories and wrist accelerations. The probability that multitasking occurred based on the obtained motor information was estimated using a Naïve Bayes Model, with a specific focus on the single and triple loading conditions. The Bayesian probability estimator showed task distinction for the wrist accelerometer data at the high and low value ranges. The likelihood of encountering a certain motor performance during well-established everyday activities, such as preparing a simple meal, changed when additional (cognitive) tasks were performed. Within a healthy population, the probability of lower acceleration frequency patterns increases when people are asked to multitask. Cognitive decline due to aging or disease might yield even greater differences.

A Comparative Study of In-Air Trajectories at Short and Long Distances in Online Handwriting

Existing literature about online handwriting analysis to support pathology diagnosis has taken advantage of in-air trajectories. A similar situation occurred in biometric security applications where the goal is to identify or verify an individual using his signature or handwriting. These studies do not consider the distance of the pen tip to the writing surface. This is due to the fact that current acquisition devices do not provide height formation. However, it is quite straightforward to differentiate movements at two different heights (a) short distance: height lower or equal to 1 cm above a surface of digitizer, the digitizer provides x and y coordinates; (b) long distance: height exceeding 1 cm, the only information available is a time stamp that indicates the time that a specific stroke has spent at long distance. Although short distance has been used in several papers, long distances have been ignored and will be investigated in this paper. In this paper, we will analyze a large set of databases (BIOSECUR-ID, EMOTHAW, PaHaW, OXYGEN-THERAPY, and SALT), which contain a total amount of 663 users and 17,951 files. We have specifically studied (a) the percentage of time spent on-surface, in-air at short distance, and in-air at long distance for different user profiles (pathological and healthy users) and different tasks; (b) the potential use of these signals to improve classification rates. Our experimental results reveal that long distance movements represent a very small portion of the total execution time (0.5% in the case of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the largest database). In addition, significant differences have been found in the comparison of pathological versus control group for letter “l” in PaHaW database (p = 0.0157) and crossed pentagons in SALT database (p = 0.0122).