252 research outputs found
Computational models of the recovery process in robot-assisted training
No full textThis chapter reviews the state of the art of computational models for neuromotor recovery, with a focus on state-space models that describe the development of functional behaviors through exercise and on the relation between neuromotor recovery and motor learning. We first review models of the dynamics of sensorimotor adaptation and motor skill learning. We then elaborate on similarities and differences with neuromotor recovery. We finally discuss how these models can be used to achieve a better understanding of the role of robots to promote recovery and to develop personalized forms of treatment
Behind, beyond, and around truth: semiotic perspectives and approaches
No full textApresentação da revista Estudos Semióticos, v. 18, n. 2, agosto de 2022
Perceptual Judgements of Plaid Motions Biased by Active Movements
No full textThe interpretation of a plaid stimulus moving through an aperture is inherently ambiguous. It can be perceived either as a coherent pattern moving rigidly or as two gratings sliding over each other. Perceptual uncertainty thresholds can be modulated by changing the relative luminance properties of single gratings. Many studies on action-perception transfer suggested that information required by the motor system to produce movements affects visual motion perception. We reasoned that physical interaction between an observer and the stimulus may influence the perceptual uncertainty associated to the moving plaids. Accordingly, we designed a motor task in which observers actively generate the relative movement between the plaid and the aperture. A two-alternative forced choice task was performed before and after the motor task to assess the motor effect on the perception of plaid motion. Preliminary results show that action biases the perceptual decision in a wide range of conditions and with spatial differences
Robot Assisted Exercise: Modelling the Recovery Process to Personalise Therapy
No full textNeurorehabilitation may greatly benefit from computational approaches. We present a computational model of the trial-by-trial dynamics of recovery through task-specific, robot-assisted exercise. The model assumes that recovery is driven by movement performance. The model explicitly addresses the extent to which training in one direction affects performance of subsequent movements in other directions. We fitted the model to data from a rehabilitation trial based on a task-specific exercise, involving reaching movements to different directions. The model reproduces the trial-by-trial speed and smoothness time series. These findings suggest that the model can be used to interpret the evolution of performance, and to formulate testable hypotheses on the recovery mechanisms, at the individual subjects' level. Therefore, it can be used to adaptively customize the robot-aided exercise based on each patient's direction-specific impairment
A protocol for the use of 99mTc(V) DMSA in the scyntigraphic diagnosis of cardiac amyloidosis
No full textIl 99mTc è un isotopo radioattivo gamma emittente comunemente impiegato in medicina nucleare diagnostica. La sua utilità consiste nel fatto che può essere coniugato a differenti molecole organiche, per la diagnostica in vivo. Il 99mTc(V)-DMSA (acido imercaptosuccinico) è un tracciante radioattivo originariamente impiegato nella diagnosi scintigrafia del carcinoma midollare della tiroide, ma è stato anche proposto come tracciante selettivo per la diagnosi differenziale di amiloidosi. Riportiamo qui un caso di amiloidosi cardiaca in cut il preminente assorbimento del tracciante da parte del miocardio ha suggerito una diagnosi di malattia amiloide, successivamente confermata da diagnosi istologica. Un'analisi quantitativa della distribuzione della radioattività ha dimostrato una chiara distinzione, tale da permettere la definizione dei criteri quantitativi per la diagnosi della malattia, impiegando il 99mTc (V) - MSA off-label come strumento di captazione selettiva.99mTc is a gamma emitter radioactive isotope commonly employed in nuclear medicine diagnostic. Its utility lies in the fact that it can be conjugated to different organic molecules, for in vivo diagnostic. The 99mTc(V)-DMSA (DiMercaptoSuccinic Acid) is a radioactive tracer originally employed in the scintigraphic diagnosis of medullary thyroid carcinoma, but has been proposed a selective tracer for the differential diagnosis of amyloidosis. We report here a case report of cardiac amyloidosis in which the prominent myocardial uptake of the tracer suggested a diagnosis of amylod disease, confirmed by histologic diagnosis. A quantitative analysis of the radioactivity distribution demonstrated a clear-cut distinction from the uptake in patients not affected by cardiac amyloidosis, so allowing quantitative criteria for a disease diagnosis employing the off-label 99mTc(V)-DMSA as a selective uptake tool
Inflammatory cytokines: from discoveries to therapies in IBD
No full textIntroduction: Although the etiology of inflammatory bowel diseases (IBD) remains unknown, accumulating evidence suggests that the intestinal tissue damage in these disorders is due to a dynamic interplay between immune cells and non-immune cells, which is mediated by cytokines produced within the inflammatory microenvironment. Areas covered: We review the available data about the role of inflammatory cytokines in IBD pathophysiology and provide an overview of the therapeutic options to block the function of such molecules. Expert opinion: Genome studies, in vitro experiments with patients' samples and animal models of colitis, have largely advanced our understanding of how cytokines modulate the ongoing mucosal inflammation in IBD. However, not all the cytokines produced within the damaged gut seem to play a major role in the amplification and perpetuation of the IBD-associated inflammatory cascade. Indeed, while some of the anti-cytokine compounds are effective in some subgroups of IBD patients, others have no benefit. In this complex scenario, a major unmet need is the identification of biomarkers that can predict response to therapy and facilitate a personalized therapeutic approach, which maximizes the benefits and limits the adverse events
Automatic human interaction understanding: lessons from a multidisciplinary approach.
No full textIn everyday life, people continuously interact with each other to achieve goals or to simplyexchange states of mind. How people react to and interact with the surrounding world is a productof evolution: the success of our species is also due to our social intellect, allowing us to live ingroups and share skills and purposes. In other words, our brain has evolved not only in term ofcognitive but also of social processing.From one side, social neuroscience has recently stressed the investigation of human interactions toreveal brain areas involved in these complex processes. Interestingly enough, in the past, socialsignals during interactions have been studied mainly by social sciences, highlighting characteristicgestures (such as hand shaking) but neglecting the contribution that the motor system may give tohuman interplays. Actually, the motor system is a fundamental part of the brain networks involvedin social cognition, as motor predictive mechanisms may contribute to the anticipation of whatothers are going to do next and regulate our own reactions, a principal function of social cognition.On the other side, social interactions are nowadays accessible to automatic analysis throughcomputer science methods, namely, computer vision and pattern recognition, the main disciplinesused for automatic scene understanding. Observation activities have never been as ubiquitous astoday and they keep increasing in terms of both amount and scope. Furthermore, the involvedtechnologies progress at a significant pace (some sensors exceed now human capabilities) and, asthey are cheap and easily available, have an increasingly large diffusion. This does not happen bychance: automatic tools make the observation objective and rigorous while safer and more2comprehensive, so that public and private environments can be monitored 24 hours a day fromdifferent points of view with limited human intervention.To date, however, neuroscientific findings about social interactions have been rarely shared withcomputer vision, as these disciplines are traditionally far from each other. The goal of this paper isto survey the methods for understanding human behavior in social interactions from both acomputational and a neuroscientific perspective, showing how they can gain large mutual benefitswhen these issues are tackled in an unified manner.In particular, methods for real-time gesture recognition, algorithms for the analysis of the bodypostures and the extraction of proxemics cues are only few examples of features that may help theonline registration and characterization of interactions under a genuine neuroscience perspective. Inthis way, the video modality could be finally considered in the analysis, whereas the audio channelhas been traditionally the most considered information source by neuroscientists so far.Similarly, understanding the processes underlying human behavior in social interactions startingfrom motor gestures is extremely important to design computer systems able to model specificsituations and events in an principled way. This can be faced by capturing novel features (specificpostures, subtle gestures) which have a precise meaning as consequences of activations of welldefined parts of the brain network.In conclusion, in this work, we will show not only that a joint approach involving neurosciencesand computational sciences can tackle in more rigorous way the studies on human social behavior,but will also disclose new perspectives and open up fresh research issues in both domains
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