Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage

: Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children's lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts' evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible

Spiral drawing analysis with a smart ink pen to identify Parkinson's disease fine motor deficits

Introduction: Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors. Methods: Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability. Results: Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important. Conclusion: Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed

Increased task-uncorrelated muscle activity in childhood dystonia

Even if movement abnormalities in dystonia are obvious on observation-based examinations, objective measures to characterize dystonia and to gain insights into its pathophysiology are still strongly needed. We hypothesize that motor abnormalities in childhood dystonia are partially due to the inability to suppress involuntary variable muscle activity irrelevant to the achievement of the desired motor task, resulting in the superposition of unwanted motion components on the desired movement. However, it is difficult to separate and quantify appropriate and inappropriate motor signals combined in the same muscle, especially during movement

Non-Functional Jaw Muscular Activity in Patients with Disorders of Consciousness Revealed by A Long-Lasting Polygraphy

The presence of involuntary, non-functional jaw muscle activity (NFJMA) has not yet been assessed in patients with disorders of consciousness (DOC), although the presence of bruxism and other forms of movement disorders involving facial muscles is probably more frequent than believed. In this work, we evaluated twenty-two prolonged or chronic DOC patients with a long-lasting polygraphic recording to verify NFJMA occurrence and assess its neurophysiological patterns in this group of patients. A total of 5 out of 22 patients showed the presence of significant NFJMA with electromyographic patterns similar to what can be observed in non-DOC patients with bruxism, thus suggesting a disinhibition of masticatory motor nuclei from the cortical control. On the other hand, in two DOC patients, electromyographic patterns advised for the presence of myorhythmia, thus suggesting a brainstem/diencephalic involvement. Functional, non-invasive tools such as long-lasting polygraphic recordings should be extended to a larger sample of patients, since they are increasingly important in revealing disorders potentially severe and impacting the quality of life of DOC patients

Problemi di reazione-diffusione: dinamica dell'ossigeno in sistemi acquatici ad alta trofia

Dottorato di ricerca in matematica computazionale e ricerca operativa. 11. ciclo. Relatori A. Lunardi e G. BuffoniConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Synergy-Based Myocontrol of a Multiple Degree-of-Freedom Humanoid Robot for Functional Tasks

In the context of sensor-based human-robot interaction, a particularly promising solution is represented by myoelectric control schemes based on synergy-derived signals. We developed and tested on healthy subjects a synergy-based control to achieve simultaneous, continuous actuation of three degrees of freedom of a humanoid robot, while performing functional reach-to-grasp movements. The control scheme exploits subject-specific muscle synergies extracted from eleven upper limb muscles through an easy semi-supervised calibration phase, and computes online activation coefficients to actuate the robot joints. The humanoid robot was able to well reproduce the subjects’ motion, which consisted in free multi-degree-of-freedom reach-to-grasp movements at self-paced speeds. Furthermore, the synergy-based online control significantly outperformed a traditional muscle-pair approach (that uses a pair of antagonist muscles for each joint), in terms of decreased error, increased correlation, and peak correlation between the subjects’ and the robot’s joint angles. The delay introduced by the two algorithms was comparable. This work is a proof-of-concept for an intuitive and robust myocontrol interface, without the need for any training and practice. It has several potential applications, especially for functional assistive engaging devices in children with social and motor impairments

Synergy-based myocontrol of multiple degrees of freedom in children with secondary dystonia

Myoelectric control can significantly improve human–robot interaction and intensive research has worked on the attempt of providing the user with intuitive control of multiple Degrees of Freedom (DOFs). However, no work has focused on patients with severe dyskinetic cerebral palsy (CP) who are unable to achieve effective voluntary movements. Research aimed at developing intuitive and flexible control interface strategies has the potential to provide these patients with significantly improved mobility. Indeed, in CP patients, there is no disconnect between the brain and the spinal cord, so that the electromyographic (EMG) signal provides a direct read-out of the movement-related activity in motor cortex. On the other hand, a major obstacle to the use of myoelectric control in patients with CP and arm dystonia is that the EMG signal is corrupted by co-contraction, variability, and noise. To address this problem, a synergy-based myoelectric approach should be tested. Indeed, when extracting synergies from multi-muscle EMG, a set of incoming EMG signals is converted into repeatable descriptors, while discarding irrelevant information, thus making muscle synergies more robust to possible noisy activity. In addition, previous studies showed that, although children with dystonia present aberrant EMG activity compared to control subjects, muscle synergies extracted from the two groups are very similar in terms of number and structure. In a previous work, we developed and successfully tested, on healthy subjects, a semi-supervised method to achieve online, simultaneous, continuous control of 2 DOFs of a robotic arm, using muscle synergies extracted from 8 upper limb muscles while performing reaching movements of the elbow and shoulder joints in the horizontal plane. Here, we tested this synergy-based myoelectric interface on 5 children with secondary dystonia due to CP. Our goal was to evaluate the feasibility and the efficacy of the synergy-based control method, compared to the muscle-pair method typically used in commercial applications, using EMG signals recorded during both unconstrained movements (Dynamic Condition) and isometric contractions (Isometric Condition). For the Dynamic Condition, the control performance was assessed by computing the Root-mean-square Error and the Pearson’s Correlation coefficient between the subject’s and the robot’s angles. For the Isometric Condition, we designed a graphical interface with a cursor that tracked the position of the robot’s end-effector and specific targets to be reached. The performance was evaluated using the time needed to accomplish the task and the number of targets reached. Results show that our method is able to provide online, simultaneous, and accurate control of 2 DOFs of a robotic arm in children with secondary dystonia due to CP. The current study is a first step toward application of synergy-based myocontrol for patients with dyskinetic CP and other disorders of the control of muscles

A Tablet App for Handwriting Skill Screening at the Preliteracy Stage: Instrument Validation Study

Background: Difficulties in handwriting, such as dysgraphia, impact several aspects of a child’s everyday life. Current methodologies for the detection of such difficulties in children have the following three main weaknesses: (1) they are prone to subjective evaluation; (2) they can be administered only when handwriting is mastered, thus delaying the diagnosis and the possible adoption of countermeasures; and (3) they are not always easily accessible to the entire community. Objective: This work aims at developing a solution able to: (1) quantitatively measure handwriting features whose alteration is typically seen in children with dysgraphia; (2) enable their study in a preliteracy population; and (3) leverage a standard consumer technology to increase the accessibility of both early screening and longitudinal monitoring of handwriting difficulties. Methods: We designed and developed a novel tablet-based app Play Draw Write to assess potential markers of dysgraphia through the quantification of the following three key handwriting laws: isochrony, homothety, and speed-accuracy tradeoff. To extend such an approach to a preliteracy age, the app includes the study of the laws in terms of both word writing and symbol drawing. The app was tested among healthy children with mastered handwriting (third graders) and those at a preliterate age (kindergartners). Results: App testing in 15 primary school children confirmed that the three laws hold on the tablet surface when both writing words and drawing symbols. We found significant speed modulation according to size (P<.001), no relevant changes to fraction time for 67 out of 70 comparisons, and significant regression between movement time and index of difficulty for 44 out of 45 comparisons (P<.05, R2>0.28, 12 degrees of freedom). Importantly, the three laws were verified on symbols among 19 kindergartners. Results from the speed-accuracy exercise showed a significant evolution with age of the global movement time (circle: P=.003, square: P<.001, word: P=.001), the goodness of fit of the regression between movement time and accuracy constraints (square: P<.001, circle: P=.02), and the index of performance (square: P<.001). Our findings show that homothety, isochrony, and speed-accuracy tradeoff principles are present in children even before handwriting acquisition; however, some handwriting-related skills are partially refined with age. Conclusions: The designed app represents a promising solution for the screening of handwriting difficulties, since it allows (1) anticipation of the detection of alteration of handwriting principles at a preliteracy age and (2) provision of broader access to the monitoring of handwriting principles. Such a solution potentially enables the selective strengthening of lacking abilities before they exacerbate and affect the child’s whole life

Play-Draw-Write: usability and acceptance of a tablet app for the early screening of handwriting difficulties in kindergartners

Dysgraphia is a Learning Disability that prevents from mastering handwriting. It is belatedly diagnosed, with negative consequences on children’s life. To anticipate Dysgraphia screening to a pre-literacy age, we present Play-Draw-Write, a tablet-based application designed to assess handwriting-related features, starting from drawing. It focuses on different aspects of graphical gesture production, such as rhythmicity and speed-accuracy tradeoff, but also to the possible alteration which might occur in gesture production itself, in free drawing. Preliminary inspection of quantitative parameters extracted from the app games suggests their potential in detecting children considered at risk of developing delays in graphical abilities, according to their teachers’ judgement. In this work, we focus on children’s opinion in terms of system acceptance and usability, to enable a longitudinal monitoring through our app and a better evaluation of the direction of possible corrections. Results from usability and acceptance questionnaires on 177 children revealed that they liked playing with the app, and wish to use it again, even when encountering some difficulties. These results are a first step toward an early, easy, and broad screening of Dysgraphia, before handwriting is learnt