Computational analysis of open loop handwriting movements in Parkinson's disease: a rapid method to detect dopamimetic effects

Dopaminergic manipulations affect the modulation and meta-modulation of movement speed: Evidence from two pharmacological interventions

A body of research implicates dopamine in the average speed of simple movements. However, naturalistic movements span a range of different shaped trajectories and rarely proceed at a single constant speed. Instead, speed is reduced when drawing "corners" compared to "straights" (i.e., speed modulation), and the extent of this slowing down is dependent upon the global shape of the movement trajectory (i.e., speed meta-modulation) - for example whether the shape is an ellipse or a rounded square. At present, it is not known how (or whether) dopaminergic function controls continuous changes in speed during movement execution. The current paper reports effects on these kinematic features of movement following two forms of dopamine manipulation: Study One highlights movement differences in individuals with PD both ON and OFF their dopaminergic medication (N = 32); Study Two highlights movement differences in individuals from the general population on haloperidol (a dopamine receptor blocker, or "antagonist") and placebo (N = 43). Evidence is presented implicating dopamine in speed, speed modulation and speed meta-modulation, whereby low dopamine conditions are associated with reductions in these variables. These findings move beyond vigour models implicating dopamine in average movement speed, and towards a conceptualisation that involves the modulation of speed as a function of contextual information.

Patients' Self-Report and Handwriting Performance Features as Indicators for Suspected Mild Cognitive Impairment in Parkinson's Disease

Early identification of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients can lessen emotional and physical complications. In this study, a cognitive functional (CF) feature using cognitive and daily living items of the Unified Parkinson's Disease Rating Scale served to define PD patients as suspected or not for MCI. The study aimed to compare objective handwriting performance measures with the perceived general functional abilities (PGF) of both groups, analyze correlations between handwriting performance measures and PGF for each group, and find out whether participants' general functional abilities, depression levels, and digitized handwriting measures predicted this CF feature. Seventy-eight participants diagnosed with PD by a neurologist (25 suspected for MCI based on the CF feature) completed the PGF as part of the Daily Living Questionnaire and wrote on a digitizer-affixed paper in the Computerized Penmanship Handwriting Evaluation Test. Results indicated significant group differences in PGF scores and handwriting stroke width, and significant medium correlations between PGF score, pen-stroke width, and the CF feature. Regression analyses indicated that PGF scores and mean stroke width accounted for 28% of the CF feature variance above age. Nuances of perceived daily functional abilities validated by objective measures may contribute to the early identification of suspected PD-MCI.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines

Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems.

Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: Performance vs. interpretability issues

In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide more effective and prompt care strategies, that cloud successfully influence patients' life expectancy. However, the most performing systems implement the so called black-box approach, which do not provide explicit rules to reach a decision. This lack of interpretability, has hampered the acceptance of those systems by clinicians and their deployment on the field. In this context, we perform a thorough comparison of different machine learning (ML) techniques, whose classification results are characterized by different levels of interpretability. Such techniques were applied for automatically identify PD patients through the analysis of handwriting and drawing samples. Results analysis shows that white-box approaches, such as Cartesian Genetic Programming and Decision Tree, allow to reach a twofold goal: support the diagnosis of PD and obtain explicit classification models, on which only a subset of features (related to specific tasks) were identified and exploited for classification. Obtained classification models provide important insights for the design of non-invasive, inexpensive and easy to administer diagnostic protocols. Comparison of different ML approaches (in terms of both accuracy and interpretability) has been performed on the features extracted from the handwriting and drawing samples included in the publicly available PaHaW and NewHandPD datasets. The experimental findings show that the Cartesian Genetic Programming outperforms the white-box methods in accuracy and the black-box ones in interpretability.

An improved sex-specific and age-dependent classification model for Parkinson's diagnosis using handwriting measurement

BACKGROUND AND OBJECTIVES

Diagnosis of Parkinson's with higher accuracy is always desirable to slow down the progression of the disease and improved quality of life. There are evidences of inherent neurological differences between male and females as well as between elderly and adults. However, the potential of such gender and age infomration have not been exploited yet for Parkinson's identification.

METHODS

In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's (n = 37; m/f-19/18;age-69.3 ± 10.9yrs) and healthy controls (n = 38; m/f-20/18;age-62.4 ± 11.3yrs). A support vector machine ranking method is used to present the features specific to their dominance in sex and age group for Parkinson's diagnosis.

RESULTS

The sex-specific and age-dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD = 1.63) with the female-specific classifier, and 79.55% (SD = 1.58) with the old-age dependent classifier was observed in comparison to 75.76% (SD = 1.17) accuracy with the generalized classifier.

CONCLUSIONS

Combining the age and sex information proved to be encouraging in classification. A distinct set of features were observed to be dominating for higher classification accuracy in a different category of classification.

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.

Analysis of on-surface and in-air movement in handwriting of subjects with Parkinson's disease and atypical parkinsonism

The purpose of this paper is to emphasize the importance of in-air movement besides on-surface movement for handwriting analysis. The proposed method uses a classification of drawing healthy subjects and subjects with Parkinson's disease, according to their on-surface and in-air handwriting parameters during their writing on a graphical tablet. Experimental results on real data sets demonstrate that the highest accuracy of subject's classification was obtained by combining both on-surface and in-air kinematic parameters.

Effect of levodopa on handwriting tasks of different complexity in Parkinson's disease: a kinematic study

Levodopa treatment does improve Parkinson's disease (PD) dysgraphia, but previous research is not in agreement about which aspects are most responsive. This study investigated the effect of levodopa on the kinematics of writing. Twenty-four patients with PD of less than 10 years duration and 25 age-matched controls were recruited. A practically defined off state method was used to assess the levodopa motor response, measured on the Unified Parkinson's Disease Rating Scale Part III. The kinematic features for six handwriting tasks involving different levels of complexity were recorded from PD patients in off and on states and from the control group. Levodopa is effective for simple writing activities involving repetition of letters, denoting improved fine motor control. But the same benefit was not seen for copying a sentence and a written category fluency test, tasks that carry memory and cognitive loads. We also found significant differences in kinematic features between control participants and PD patients, for all tasks and in both on and off states. Serial testing of handwriting in patients known to be at risk for developing PD might prove to be an effective biomarker for cell loss in the substantia nigra and the associated dopamine deficiency. We recommend using a panel of writing tasks including sentence copying and memory dependence. Dual-task effects may make these activities more sensitive to early motor deficits, while their weaker levodopa responsiveness would cause them to be more stable indicators of motor progression once symptomatic treatment has been commenced.

Handwriting Analysis in Parkinson's Disease: Current Status and Future Directions

BACKGROUND

The majority of patients with Parkinson's disease (PD) have handwriting abnormalities. Micrographia (abnormally small letter size) is the most commonly reported and easily detectable handwriting abnormality in patients with PD. However, micrographia is perhaps the tip of the iceberg representing the handwriting abnormalities in PD. Digitizing tablet technology, which has evolved over the last 2 decades, has made it possible to study the pressure and kinematic features of handwriting. This has resulted in a surge of studies investigating graphomotor impairment in patients with PD.

METHODS

The objectives of this study were to review the evolution of the kinematic analysis of handwriting in PD and to provide an overview of handwriting abnormalities observed in PD along with future directions for research in this field. Articles for review were searched from the PubMed and SCOPUS databases.

RESULTS

Digitizing tablet technologies have resulted in a shift of focus from the analysis of only letter size to the analysis of several kinematic features of handwriting. Studies based on the kinematic analysis of handwriting have revealed that patients with PD may have abnormalities in velocity, fluency, and acceleration in addition to micrographia. The recognition of abnormalities in several kinematic parameters of handwriting has given rise to the term PD dysgraphia. In addition, certain kinematic properties potentially may be helpful in distinguishing PD from other parkinsonian disorders.

CONCLUSION

The journey from micrographia to PD dysgraphia is indeed a paradigm shift. Further research is warranted to gain better insight into the graphomotor impairments in PD and their clinical implications.

A Behavioral Handwriting Model for Static and Dynamic Signature Synthesis

The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.

Spatial and dynamical handwriting analysis in mild cognitive impairment

Background and Objectives Standard clinical procedure of Mild Cognitive Impairment (MCI) assessment employs time-consuming tests of psychological evaluation and requires the involvement of specialists. The employment of quantitative methods proves to be superior to clinical judgment, yet reliable, fast and inexpensive tests are not available. This study was conducted as a first step towards the development of a diagnostic tool based on handwriting. Methods In this paper the handwriting sample of a group of 37 patients with MCI (mean age 76.1±5.8) and 37 healthy controls (mean age 74.8±5.7) was collected using a Livescribe Echo Pen while completing three tasks: (1) regular writing, (2) all-capital-letters writing, and (3) single letter multiply repeated. Parameters differentiating both groups were selected in each task. Results Subjects with confirmed MCI needed more time to complete task one (median 119.5s, IQR - interquartile range - 38.1 vs. 95.1s, IQR 29.2 in control and MCI group, p-value <0.05) and two (median 84.2s, IQR 49.2 and 53.7s, IQR 30.5 in control and MCI group) as their writing was significantly slower. These results were associated with a longer time to complete a single stroke of written text. The written text was also noticeably larger in the MCI group in all three tasks (e.g. median height of the text block in task 2 being 22.3mm, IQR 12.9 in MCI and 20.2mm, IQR 8.7 in control group). Moreover, the MCI group showed more variation in the dynamics of writing: longer pause between strokes in task 1 and 2. The all-capital-letters task produced most of the discriminating features. Conclusion Proposed handwriting features are significant in distinguishing MCI patients. Inclusion of quantitative handwriting analysis in psychological assessment may be a step forward towards a fast MCI diagnosis.

A new computer vision-based approach to aid the diagnosis of Parkinson's disease

BACKGROUND AND OBJECTIVE

Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction.

METHODS

In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach.

RESULTS

The results have shown that we can obtain very reasonable recognition rates (around ≈67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset.

CONCLUSIONS

The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients.

Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

OBJECTIVE

We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD.

METHODS AND MATERIAL

The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM).

RESULTS

For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features.

CONCLUSION

Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.

Toward Monitoring Parkinson's Through Analysis of Static Handwriting Samples: A Quantitative Analytical Framework

Detection of changes in micrographia as a manifestation of symptomatic progression or therapeutic response in Parkinson's disease (PD) is challenging as such changes can be subtle. A computerized toolkit based on quantitative analysis of handwriting samples would be valuable as it could supplement and support clinical assessments, help monitor micrographia, and link it to PD. Such a toolkit would be especially useful if it could detect subtle yet relevant changes in handwriting morphology, thus enhancing resolution of the detection procedure. This would be made possible by developing a set of metrics sensitive enough to detect and discern micrographia with specificity. Several metrics that are sensitive to the characteristics of micrographia were developed, with minimal sensitivity to confounding handwriting artifacts. These metrics capture character size-reduction, ink utilization, and pixel density within a writing sample from left to right. They are used here to "score" handwritten signatures of 12 different individuals corresponding to healthy and symptomatic PD conditions, and sample control signatures that had been artificially reduced in size for comparison purposes. Moreover, metric analyses of samples from ten of the 12 individuals for which clinical diagnosis time is known show considerable informative variations when applied to static signature samples obtained before and after diagnosis. In particular, a measure called pixel density variation showed statistically significant differences ( ) between two comparison groups of remote signature recordings: earlier versus recent, based on independent and paired t-test analyses on a total of 40 signature samples. The quantitative framework developed here has the potential to be used in future controlled experiments to study micrographia and links to PD from various aspects, including monitoring and assessment of applied interventions and treatments. The inherent value in this methodology is further enhanced by its reliance solely on static signatures, not requiring dynamic sampling with specialized equipment.

Graphical Tasks to Measure Upper Limb Function in Patients With Parkinson's Disease: Validity and Response to Dopaminergic Medication

The most widely used method to assess motor functioning in Parkinson's disease (PD) patients is the unified Parkinson's disease rating scale-III (UPDRS-III). The UPDRS-III has limited ability to detect subtle changes in motor symptoms. Alternatively, graphical tasks can be used to provide objective measures of upper limb motor dysfunction. This study investigated the validity of such graphical tasks to assess upper limb function in PD patients and their ability to detect subtle changes in performance. Fourteen PD patients performed graphical tasks before and after taking dopaminergic medication. Graphical tasks included figure tracing, writing, and a modified Fitts' task. The Purdue pegboard test was performed to validate these graphical tasks. Movement time (MT), writing size, and the presence of tremor were assessed. MT on the graphical tasks correlated significantly with performance on the Purdue pegboard test (Spearman's ρ > 0.65; p < 0.05). MT decreased significantly after the intake of dopaminergic medication. Tremor power decreased after taking dopaminergic medication in most PD patients who suffered from tremor. Writing size did not correlate with performance on the Purdue pegboard test, nor did it change after taking medication. Our set of graphical tasks is valid to assess upper limb function in PD patients. MT proved to be the most useful measure for this purpose. The response on dopaminergic medication was optimally reflected by an improved MT on the graphical tasks in combination with a decreased tremor power, whereas writing size did not respond to dopaminergic treatment.

Modeling the lexical morphology of Western handwritten signatures

A handwritten signature is the final response to a complex cognitive and neuromuscular process which is the result of the learning process. Because of the many factors involved in signing, it is possible to study the signature from many points of view: graphologists, forensic experts, neurologists and computer vision experts have all examined them. Researchers study written signatures for psychiatric, penal, health and automatic verification purposes. As a potentially useful, multi-purpose study, this paper is focused on the lexical morphology of handwritten signatures. This we understand to mean the identification, analysis, and description of the signature structures of a given signer. In this work we analyze different public datasets involving 1533 signers from different Western geographical areas. Some relevant characteristics of signature lexical morphology have been selected, examined in terms of their probability distribution functions and modeled through a General Extreme Value distribution. This study suggests some useful models for multi-disciplinary sciences which depend on handwriting signatures.

Characterization of fine motor development: dynamic analysis of children's drawing movements

In this study, we investigated children's fine motor development by analyzing drawing trajectories, kinematics and kinetics. Straight lines drawing task and circles drawing task were performed by using a force sensitive tablet. Forty right-handed and Chinese mother-tongue students aged 6-12, attending classes from grade 1 to 5, were engaged in the experiment. Three spatial parameters, namely cumulative trace length, vector length of straight line and vertical diameter of circle were determined. Drawing duration, mean drawing velocity, and number of peaks in stroke velocity profile (NPV) were derived as kinematic parameters. Besides mean normal force, two kinetic indices were proposed: normalized force angle regulation (NFR) and variation of fine motor control (VFC) for circles drawing task. The maturation and automation of fine motor ability were reflected by increased drawing velocity, reduced drawing duration, NPV and NFR, with decreased VFC in circles drawing task. Grade and task main effects as well as significant correlations between age and parameters suggest that factors such as schooling, age and task should be considered in the assessment of fine motor skills. Compared with kinematic parameters, findings of NFR and VFC revealed that kinetics is another important perspective in the analysis of fine motor movement.

Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease

BACKGROUND AND OBJECTIVE

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis.

METHODS

We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls.

RESULTS

By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy.

CONCLUSIONS

Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.

From micrographia to Parkinson's disease dysgraphia

Micrographia, an abnormal reduction in writing size, is a specific behavioral deficit associated with Parkinson's disease (PD). In recent years, the availability of graphic tablets has made it possible to study micrographia in unprecedented detail. Consequently, a growing number of studies show that PD patients also exhibit impaired handwriting kinematics. Is micrographia still the most characteristic feature of PD-related handwriting deficits? To answer this question, we identified studies that investigated handwriting in PD, either with conventional pencil-and-paper measures or with graphic tablets, and we reported their findings on key spatiotemporal and kinematic variables. We found that kinematic variables (velocity, fluency) differentiate better between control participants and PD patients, and between off- and on-treatment PD patients, than the traditional measure of static writing size. Although reduced writing size is an important feature of PD handwriting, the deficit is not restricted to micrographia stricto sensu. Therefore, we propose the term PD dysgraphia, which encompasses all deficits characteristic of Parkinsonian handwriting. We conclude that the computerized analysis of handwriting movements is a simple and useful tool that can contribute to both diagnosis and follow-up of PD.

Handwriting performance in children with attention deficit hyperactivity disorder (ADHD)

Attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral condition of childhood. Consequences are multifaceted and include activity limitations in daily-living skills, academic challenges, diminished socialization skills, and motor difficulties. Poor handwriting performance is an example of an affected life skill that has been anecdotally observed by educators and clinicians for this population and can negatively impact academic performance and self-esteem. To guide health and educational service delivery needs, the authors reviewed the evidence in the literature on handwriting difficulties in children with ADHD. Existing evidence would suggest that children with ADHD have impaired handwriting performance, characterized by illegible written material and/or inappropriate speed of execution compared to children without ADHD. Studies with larger sample sizes using standardized measures of handwriting performance are needed to evaluate the prevalence of the problem and to better understand the nature of handwriting difficulties and their impact in this population.

How to assess motor impairment in writer's cramp

Writer's cramp is a task-specific hand dystonia affecting handwriting. Clinical scores such as the Arm Dystonia Disability Scale (ADDS) or Writer's Cramp Rating Scale (WCRS) as well as kinematic analysis of handwriting movements have been used to assess functional impairment in affected patients. In 21 patients with writer's cramp and healthy controls, we analyzed the kinematics of writing and cyclic drawing movements. We rated the severity of dystonia using the ADDS and WCRS and correlated the clinical scores with movement kinematics. Mean stroke frequency was significantly reduced in dystonic patients. Drawing movements showed more frequently a decrease in stroke frequency than handwriting movements. During circle drawing, mean vertical peak velocity was more variable in patients relative to controls, indicating an impaired ability to reproduce the same kinematic pattern over time. An increase in vertical writing pressure was only observed during handwriting but not during circle drawing and may reflect a compensatory effort to stabilize the pencil. Kinematic measures and individual ADDS and WCRS scores did not correlate with each other. The lack of correlation is not surprising as ADDS, WCRS, and kinematic analysis probe different aspects of motor impairment. The ADDS characterizes how dystonia affects a set of fine manual tasks, whereas the WCRS scores the manifestation of dystonia during handwriting. Therefore, the clinical scores and kinematic analysis of handwriting provide complementary insights into motor impairment. Future studies need to address which combination of clinical scores and kinematic measures are most appropriate to quantify impairment in writer's cramp.

Brain dopamine and kinematics of graphomotor functions

Three experiments were performed in an attempt to achieve a better understanding of the effect of dopamine on handwriting. In the first experiment, kinematic aspects of handwriting movements were compared between healthy participants and patients with Parkinson's disease (PD) on their usual dopaminergic treatment and following withdrawal of dopaminergic medication. In the second experiment, the writing performance of healthy participants with a hyperechogenicity of the substantia nigra as detected by transcranial sonography (TCS) was compared with the performance of healthy participants with low echogenicity of the substantia nigra. The third experiment examined the effect of central dopamine reduction on kinematic aspects of handwriting movements in healthy adults using acute phenylalanine and tyrosine depletion (APTD). A digitising tablet was used for the assessment of handwriting movements. Participants were asked to perform a simple writing task. Movement time, distance, velocity, acceleration and measures of fluency of handwriting movements were measured. The kinematic analysis of handwriting movements revealed that alterations of central dopaminergic neurotransmission adversely affect movement execution during handwriting. In comparison to the automatic processing of handwriting movements displayed by control participants, participants with an altered dopaminergic neurotransmission shifted from an automatic to a controlled processing of movement execution. Central dopamine appears to be of particular importance with regard to the automatic execution of well-learned movements.

The effect of caffeine on handwriting movements in skilled writers

In laboratory tasks, caffeine has been shown to improve psychomotor performance. The aim of the present experiment was to assess the effects of caffeine on a skilled everyday life task in habitual caffeine consumers. The assessment of handwriting movements of 20 adults was performed following the administration of 0mg/kg (placebo), 1.5mg/kg, 3.0mg/kg or 4.5mg/kg of caffeine. A digitising tablet was used for the assessment of fine motor movements. Participants were asked to perform a simple writing task. Kinematic analysis of handwriting movements showed that, in comparison to placebo administration, high doses of caffeine (i.e., 4.5mg/kg) can produce improvements in handwriting as indicated by more fluent handwriting movements as well as an increase in maximum velocity and maximum positive and negative accelerations. The results suggest that higher doses of caffeine can enhance psychomotor performance.

Attention and movement execution during handwriting

Two studies were performed in order to test the hypothesis that movement execution during handwriting in skilled writers is independent of attention. Study I examined the relationship between attentional functioning and kinematic aspects of handwriting movements in 24 adult participants. A digitizing tablet was used for the assessment of handwriting movements. Participants were asked to perform a simple writing task and various components of their attention were assessed. Correlation analysis indicated no significant relationship between attention functions and both kinematic aspects of handwriting movements and quality of handwriting. In Study II, 20 participants underwent total sleep deprivation (TSD) for 24h. While attention, as assessed with alertness and vigilance tasks, deteriorated during TSD, the execution of handwriting movements, as assessed with a digitizing tablet, improved markedly. The quality of handwriting was not affected by TSD. These findings may suggest an independence between attention and movement generation during handwriting.

Increased periodontal pathology in Parkinson's disease

Parkinson's disease is characterized by rigidity, akinesia and tremor, all of which interfere with automated small hand movements potentially affecting oral care. In addition, medication and craving for sweets are other risk factors for dental and periodontal disease in these patients. Here, we report the Community Periodontal Index for Treatment Needs (CPITN) data in 70 patients with Parkinson's disease and 85 age-matched control subjects. CPITN indices were assessed in all 6 sextants. Mean CPITN indices of control subjects ranged from 1.6 +/- 0.2 in the upper frontal sextant to 2.5 +/- 0.2 in the upper left lateral sextant. 17.9% of controls showed severely affected teeth (CPITN code 4) in the upper left or upper right sextant, while there were no teeth at risk in the upper frontal sextant. Patients suffering from Parkinson's disease, however, had a markedly increased mean CPITN index in the upper frontal sextant (2.4 +/- 0.2) with 11.5 patients having severely affected teeth (CPITN code 4). CPITN indices in all other sextants were less severely increased. Overall, there was a significant difference between mean CPITN indices of patients and controls (p < 0.05). It seems also noteworthy that female controls had lower CPITN indices in all sextants compared with male controls. This gender difference, however, was reversed in Parkinson's patients. We believe that problems in oral hygiene contribute to this increased periodontal pathology in patients with Parkinson's disease, which may further compromise the quality of life.

Kinetic tremor in Parkinson's disease--an underrated symptom

Since the first description of the disorder which we now call "Parkinson's Disease" (PD) much has changed not only because of new therapeutic possibilities. Initially only the rest tremor was described. Today it is generally accepted that PD can be accompanied by different forms of tremor. Nevertheless the kinetic tremor is hardly examined and no attention is paid to it in clinical rating scales although it can already be found in old published drawings of PD-patients. To date instrumented investigations do not capture the most common kinetic tremor of PD that seems to be frequent under everyday life conditions. In order to assess the significance of kinetic tremor in PD, tremor during a spiral drawing task was investigated in an open study involving 870 patients. The results indicate that a combination of rest, postural and kinetic tremors constitute the most frequent tremor constellation in PD.

Motor dysfunction in patients with liver cirrhosis: impairment of handwriting

Motor dysfunction is an important clinical finding in patients with liver cirrhosis and mild forms of hepatic encephalopathy. The mechanisms and clinical appearance of motor impairment in patients with liver cirrhosis are not completely understood. We studied fine motor control in forty four patients with advanced liver cirrhosis (excluding those with hepatic encephalopathy grade II) and 48 healthy controls using a kinematic analysis of standardized handwriting tests. We analysed parameters of velocity, the ability to coordinate and the level of automatisation of handwriting movements. Furthermore, we studied the association between impairment of handwriting and clinical neuro-psychiatric symptoms. As compared with control subjects, patients showed a statistically significant reduction of movement peak velocity in all handwriting tasks as well as a substantial increase of number of velocity inversions per stroke. Using a z-score based assessment we found impairment of handwriting in fourteen out of forty four patients (31.8 %). The deterioration of handwriting was associated with clinical symptoms of motor dysfunction, such as bradykinesia, adiadochokinesia, dysmetria of upper extremities and gait ataxia. This is the first study that quantitatively investigates impairment of handwriting in patients with liver cirrhosis. Our findings suggest the application of kinematic analysis of handwriting for diagnostics of motor dysfunction in patients with mild forms of hepatic encephalopathy.

Kinematic evaluation in Parkinson's disease using a hand-held position transducer and computerized signal analysis

BACKGROUND

The objective of this work was to develop a device for quantification of akinesia in Parkinson's disease, for the use in home monitoring of PD patients, as a part of home telecare programs. For this purpose a simple movement task is to be preferred, and the measurement devices must be small, lightweight, and easy to use, so patients may perform the measurements unattended. Another intended application was optimisation of the electrode position during implantations of neuromodulation systems for treatment of Parkinson.

METHOD

A hand held transducer was used to measure the position of the thumb while the patient repeatedly flexed and extended the thumb. The position data was sampled and stored on a personal computer with a plug in converter card and software. Measurements were performed on 15 PD patients and 6 age-matched controls. Signal analysis procedures were developed in order to automatically derive numerical parameters that quantify the movement performance. In order to select the most relevant parameters, they were correlated to Unified Parkinson Disease Rating Scale (UPDRS) motor scores (Spearman's rank, single sided, p < 0.05).

FINDINGS

In reviews of the raw position signals the amplitude and frequency was found to be lower in patients than in controls. In patients the movement was frequently interrupted by short periods of hesitation. The calculated parameters of covered distance (correlation coefficient r = -0.63), hesitation (r = 0.64) and frequency (r = -0.6) were found to be most relevant, as they correlated best to the UPDRS hand pronation/supination score.

DISCUSSION

The equipment proved to be fast to setup and easy to use. The signal analysis methods provided meaningful numerical parameters for quantification of akinesia, represented in hand pronation/supination. These results suggest that the described methods may be useful for telemedicine and intraoperative use.

Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease

Patients with Parkinson's disease (PD) exhibit impairments in the execution of highly practiced and skilled motor actions such as handwriting. The analysis of kinematic aspects of handwriting movements has demonstrated that size, speed, acceleration and stroke duration are affected in PD. Although beneficial effects of dopaminergic therapy in regard to execution of movements have been reported, the effects of pharmacological therapy on these measures have not been examined in detail. The present study has compared kinematic aspects of handwriting movements of 27 healthy subjects and 27 patients with PD both on their usual dopaminergic treatment and following withdrawal of dopaminergic medication. Healthy subjects were matched with PD patients according to age, sex, handedness and education level. A digitising tablet was used for the assessment of handwriting movements. Subjects were asked to perform a simple writing task. Movement time, distance, velocity, acceleration and measures of fluency of handwriting movements were measured. Compared with healthy subjects, the kinematics of handwriting movements in PD patients were markedly disturbed following withdrawal of dopaminergic medication. Although dopaminergic treatment in PD patients resulted in marked improvements in the kinematics of handwriting movements, PD patients did not reach an undisturbed level of performance. The results suggest that dopamine medication results in partial restoration of automatic movement execution.

The effect of conscious control on handwriting in children with attention deficit hyperactivity disorder

Two experiments were performed regarding the effect of conscious control on handwriting fluency in healthy adults and ADHD children. First, 26 healthy students were asked to write a sentence under different conditions. The results indicate that automated handwriting movements are independent from visual feedback. Second, the writing performance of 12 children with ADHD was examined on their usual medication with methylphenidate and under placebo. In comparison to placebo, medication with methylphenidate resulted in a reduced fluency of handwriting. Automated handwriting movements could be elicited in children with ADHD on medication. The results suggest that both visual and mental control of handwriting movements affect the automation of handwriting movements. Furthermore, a simple training procedure was designed and performed in a case study of a boy with ADHD.

Can a subgroup of OCD patients with motor abnormalities and poor therapeutic response be identified?

RATIONALE

In a subgroup of patients with obsessive-compulsive disorder (OCD), motor soft signs, tics and other movement disorders can be observed, indicating a special pathogenetic involvement of basal ganglia.

OBJECTIVES

The main objective of this study was to verify the hypothesis that such motor dysfunction characterises a subgroup of OCD patients with poor treatment response. For assessing even subtle motor dysfunction, a new method for kinematical analysis of hand movements has been applied.

METHODS

We examined the performance of 45 in-patients who met the DSM-IV criteria for OCD before and under therapy (sertraline and behaviour therapy) using a digitising tablet and kinematical analysis of simple handwriting and drawing movements. All subjects wrote a sentence, their signature and letter sequences. Moreover, they drew circles under different conditions. Three kinematical parameters (stroke duration, variation coefficient of peak velocity, stroke length) were calculated to quantify hand-motor performance.

RESULTS

Prior to therapy, non-responders wrote with significantly smaller amplitudes than responders. Additionally, non-responders drew significantly larger circles with the non-dominant hand at baseline, as compared to responders. Disturbances of handwriting were more frequent in non-responders than in responders.

CONCLUSIONS

Kinematical analysis of handwriting movements seems to be interesting for the prediction of poor response to treatments in OCD patients.

Automatic detection of movement disorders using recordings of rapid alternating movements

The present work assesses the potential of rapid alternating movement analysis for detecting movement disorders like Parkinson's disease. Rapid alternating wrist movements were recorded by a diadochokinesimeter for patients with Parkinson's disease (n=10) and healthy controls (n=20). An index of irregularity was computed for each individual as the density of jerk singularities (i.e. zero-crossings) during the movements. Several scales of analysis (i.e. "coarseness") were used for detecting the jerk events and two methods were compared for all of these scales: (1) automatic classification by means of a threshold that optimally separates the indexes of irregularity of the patients from those of the controls, and (2) statistical decision (normal or abnormal) based upon a distribution of indexes of irregularity obtained from a large population of normal subjects. The results showed that (1) two scales of analysis were sufficient and that (2) both methods presented similar performances (e.g. sensitivity=1.00, specificity=0.85, efficiency=0.90). However, statistical decision should be preferred because of its simplicity. The possibility of automatic detection of movement disorders from alternating movements is discussed.