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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.

The integration of Ambient Assisted Living (AAL) frameworks with Socially Assistive Robots (SARs) has proven useful for monitoring and assisting older adults in their own home. However, the difficulties associated with long-term deployments in real-world complex environments are still highly under-explored. In this work, we first present the MoveCare system, an unobtrusive platform that, through the integration of a SAR into an AAL framework, aimed to monitor, assist and provide social, cognitive, and physical stimulation in the own houses of elders living alone and at risk of falling into frailty. We then focus on the evaluation and analysis of a long-term pilot campaign of more than 300 weeks of usages. We evaluated the system’s acceptability and feasibility through various questionnaires and empirically assessed the impact of the presence of an assistive robot by deploying the system with and without it. Our results provide strong empirical evidence that Socially Assistive Robots integrated with monitoring and stimulation platforms can be successfully used for long-term support to older adults. We describe how the robot’s presence significantly incentivised the use of the system, but slightly lowered the system’s overall acceptability. Finally, we emphasise that real-world long-term deployment of SARs introduces a significant technical, organisational, and logistical overhead that should not be neglected nor underestimated in the pursuit of long-term robust systems. We hope that the findings and lessons learned from our work can bring value towards future long-term real-world and widespread use of SARs.

The Coma Recovery Scale-revised (CRS-r) is the gold standard for the behavioral assessment of patients with Disorders of Consciousness (DoCs). However, the misdiagnosis rate is around 40%. For this reason, recent guidelines suggested enhancing the assessment with neurophysiological measures: among these, surface electromyography (sEMG) represents a convenient bedside solution. This work presents the use of the STRIVEfc system, a wearable device that allows CRS-r administration while recording four sEMG signals. STRIVEfc was employed in 40 sessions on 33 DoCs patients and the sEMG was analyzed to look for voluntary and consistent over threshold (OT) muscular activities. Their duration, amplitude, and number were retained and compared between patients in Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), revealing more numerous and significantly longer OTs in the latter group. Lastly, the EMG information was exploited to enrich the behavioral assessment by building the instrumented CRS-r score (ICRS-r). In 9/16 UWS sessions, the ICRS-r score suggested a higher-level functioning, not translated into a behavioral response, compatible with MCS diagnosis. Overall, the use of STRIVEfc allows to reveal hidden muscular patterns not detectable by the clinician, thus improving the characterization of DoCs patient’s functional capabilities and supporting the diagnostic process.

BackgroundLittle is known about the perceived impact of the COVID-19 pandemic and subsequent lockdown measures on young patients with tic disorders. Previous studies focused on clinician and parent ratings of tic severity, whereas the only international self-report data are available for adult populations. We present the first findings from a case–control study on children and adolescents with tics during lockdown in Italy.MethodsWe surveyed 49 patients aged 6–18 years and 245 matched controls with a newly developed questionnaire covering socio-demographic and clinical data, as well as lockdown-related changes to daily life activities.ResultsAbout half (53.2%) of the Italian school-age patients who took part in our survey experienced changes in tic severity during lockdown. Perceived increases in tic severity (29.8%) were reported more often than decreases (23.4%). Analogous trends were reported for perceived restlessness and, more significantly, irritability, whereas changes in pain symptoms were less common and were similar in both directions. The presence of tics was associated with increased difficulties with remote learning (p = 0.01), but decreased feelings of missing out on social interactions with schoolmates (p = 0.03).ConclusionsSelf-reported data on the impact of COVID-19 lockdown in school-age patients with tic disorders indicate perceived changes in tic severity, as well as restlessness and irritability, in about half of the cases. These findings could guide both clinicians and teachers in the implementation of targeted adjustments in the delivery of care and educational strategies, respectively.

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.

Mild cognitive impairment (MCI) is a precursor of dementia. Therefore, MCI identification and monitoring are crucial to delaying dementia onset. Given the limits of existing clinical tests, objective support tools are needed. This work investigates quantitative handwriting analysis, tailored to enable domestic monitoring, as a noninvasive approach for MCI screening and assessment. A sensorized ink pen, used on paper and equipped with sensors, memory, and a communication unit, was used for data acquisition. The tasks included writing a grocery list and free text to mimic daily life handwriting, and a clinical dictation test (parole-non-parole [PnP] test), featuring regular, irregular, and made-up words, aimed at assessing MCI dysgraphia. From the recorded data, 106 indicators describing the performance in terms of time, fluency, exerted force, and pen inclination were computed. A total of 57 patients with MCI were recruited, of whom 45 performed a test-retest protocol. The indicators were examined to assess their test-retest reliability. The indicators from the test repetition were used to assess their relationship with the scores of clinical tests via correlation analysis. For the PnP test, differences in the indicators among the 3 types of words were statistically investigated. These analyses were conducted separately for the cursive (2/3 of the sample) and block letters (1/3 of the sample) allographs, with the level of significance set at 5%. Data from healthy older adults were available for the grocery list (34 participants) and free text (45 participants) tasks. These were exploited to build machine learning classification models for the distinction between patients with MCI and healthy controls. When dealing with reliability, 93% and 44% of the indicators were characterized by a significant reliability of at least moderate intensity for cursive and block letters respectively. As for the correlation analysis, patients with preserved cognitive status and daily life functionality were associated with significantly better temporal performances, both in free writing and PnP. The analysis of PnP highlighted the presence of surface dysgraphia in the recruited sample, as irregular words showed significantly worse temporal indicators with respect to regular and made-up ones. The classification models' built-in free writing data achieved accuracies ranging from 0.80 to 0.93 and F -scores from 0.81 to 0.92 according to the input dataset. The presented results suggest the suitability of ecological handwriting analysis for the all-around monitoring of MCI, from early screening to disease progression evaluation.

The objective of this study is to verify the reliability and the concurrent and discriminant validity of the measurements of spasticity offered by the robotic device, quantifying the (1) test-retest reliability, (2) correlation with the clinical evaluation using the Modified Ashworth Scale (MAS), (3) inter-rater reliability between the two physiotherapists, and (4) ability to discriminate between healthy and stroke patients. A total of 20 stroke patients and 20 healthy volunteers participated in the study. Two physical therapists (PT1 and PT2) independently evaluated the hand spasticity of stroke subjects using the MAS. Spasticity was assessed, both in healthy and stroke patients, with the Amadeo device at three increasing velocities of passive movement for three consecutive repeated assessments, while raw data of force and position were collected through an external program. The intraclass correlation coefficient (ICC) and the weighted kappa were computed to estimate the reliability of the Amadeo device measurements, the inter-rater reliability of MAS, and the correlation between the MAS and Amadeo device measurements. The discriminant ability of the Amadeo device was assessed by comparing the stroke and healthy subjects' spasticity measurements with the percentage of agreements with 0 in MAS for healthy subjects. The test-retest reliability of the Amadeo device was high with ICC at all three velocities (ICC = 0.908, 0.958, and 0.964, respectively) but lower if analyzed with weighted kappa correlation (0.584, 0.748, and 0.749, respectively) as mean values for each velocity. The correlation between Amadeo and the clinical scale for stroke patients with weighted kappa correlation was poor (0.280 ± 0.212 for PT1 and 0.290 ± 0.155 for PT2). The inter-rater reliability of the clinical MAS was high (ICC = 0.911). Both MAS and Amadeo spasticity scores showed good reliability. The Amadeo scores did not show a strong clinical correlation with the MAS in stroke patients. Hitherto, Amadeo evaluation shows trends that are consistent with the characteristics of spasticity, such as an increase in spasticity as the speed of muscle stretching increases. The ability of the device to discriminate between stroke patients and healthy controls is low. Future studies adopting an instrumental gold standard for spasticity may provide further insight into the validity of these measurements.

Background This study is aimed at better understanding the role of a wearable and silent ElectroMyoGraphy-based biofeedback on motor learning in children and adolescents with primary and secondary dystonia. Methods A crossover study with a wash-out period of at least 1 week was designed; the device provides the patient with a vibration proportional to the activation of an impaired target muscle. The protocol consisted of two 5-day blocks during which subjects were trained and tested on a figure-8 writing task: their performances (at different levels of difficulty) were evaluated in terms of both kinematics and muscular activations on day 1 and day 5, while the other 3 days were purely used as training sessions. The training was performed with and without using the biofeedback device: the week of use was randomized. Data were collected on 14 subjects with primary and secondary (acquired) dystonia (age: 6–19 years). Results Results comparing kinematic-based and EMG-based outcome measures pre- and post-training showed learning due to practice for both subjects with primary and secondary dystonia. On top of said learning, an improvement in terms of inter-joint coordination and muscular pattern functionality was recorded only for secondary dystonia subjects, when trained with the aid of the EMG-based biofeedback device. Conclusions Our results support the hypothesis that children and adolescents with primary dystonia in which there is intact sensory processing do not benefit from feedback augmentation, whereas children with secondary dystonia, in which sensory deficits are often present, exhibit a higher learning capacity when augmented movement-related sensory information is provided. This study represents a fundamental investigation to address the scarcity of noninvasive therapeutic interventions for young subjects with dystonia.

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.

The COVID-19 lockdown affected children, especially those with autism spectrum disorder, due to the disruption in rehabilitation and educational activities. We conducted a cross-sectional study of 315 preschool-aged children, 35 of which had autism, to investigate this impact. A questionnaire was administered to explore socio-demographic status, familiar/home environment, and COVID-19 exposure. The clinical features of autistic subjects were also examined. Seven variables were considered to describe the effect of pandemic: Remote learning , Behavior changes , Home activities , Sleep habits , Night awakenings , Physical activity , Information about the virus . The lockdown had a significant impact on Remote learning , Behavior changes , and Information about the virus in participants with autism. Moreover, we found a worsening in repetitive movements, echolalia, restricted interests, and aggressive behaviors.