1,720,969 research outputs found
Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model
No full textThe tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required accuracy in reaching a target or farther is the target, the slower has to be the movement. A variety of computational models of neuromusculoskeletal systems have been proposed to pinpoint the neurobiological mechanisms that are involved in human movement. We introduce a neurocomputational model of spinal cord to unveil how the tradeoff between speed and accuracy elicits from the interaction between neural and musculoskeletal systems. Model simulations showed that the speed–accuracy tradeoff is not an intrinsic property of the neuromuscular system, but it is a behavioral trait that emerges from the strategy adopted by the central nervous system for executing faster movements. In particular, results suggest that the velocity of a previous learned movement is regulated by the monosynaptic connection between cortical cells and alpha motoneurons
A paradigm for emulating the early learning stage of handwriting: Performance comparison between healthy controls and Parkinson's disease patients in drawing loop shapes
No full textWe present a novel paradigm, aimed at emulating the early stage of handwriting learning in proficient writers, by asking them to produce a familiar shape through a novel (unfamiliar) motor plan. Handwriting of beginner writers is characterized by slower movements, reduced spatial precision, lower fluency and reduced force regulation compared to those observed in the handwriting production of proficient writers. Features observed in the ink trace obtained with the novel motor plan and performance comparison of the handwriting obtained by familiar and unfamiliar motor plan suggest that the proposed paradigm is able to elicit non-automated movements in proficient writers. As that produced by beginner writers, handwriting of Parkinson's disease (PD) patients is characterized by lack of fluency, slowness and abrupt changes of direction. Furthermore, PD patients show impaired performance in learning novel motor behaviors, as well as in executing motor behaviors acquired before the onset of the disease. We used the proposed paradigm for comparing the performance achieved by healthy controls in writing a familiar shape through a novel motor plan with those obtained by PD patients performing a well-known motor plan for drawing the same shape. Our analysis points out some similarities between performance obtained by healthy controls and those obtained by PD patients, sustaining the hypothesis that the fine tuning of the motor plan parameters involved in the handwriting production is impaired by PD
Do handwriting difficulties of Parkinson's patients depend on their impaired ability to retain the motor plan? A pilot study
No full textPatients affected by Parkinson’s disease (PD) show deficits in learning novel motor behaviors and executing previously acquired ones. We investigated whether the two phenomena are related, evaluating the hypothesis that PD patients have difficulties in executing fine movements (such as handwriting) acquired before the onset of the disease since they perform the task as they are executing it for the first time. We asked healthy subjects to write a sequence of ‘l’ on a digitizer tablet by drawing the loop of the letter in counterclockwise fashion (as they are used to do) and clockwise fashion (i.e. using a novel motor plan). We compared the kinematic features of the samples produced by healthy subjects to those measured in samples produced by PD patients. We focused the analysis on the ink trace segmentation points, which represent the starting/ending points of the elementary handwriting movements. Our results suggests that deficits observed in PD patients in executing both novel tasks (reduced learning performance compared to controls) and previously acquired task (disrupted kinematic features compared to controls) could be due to the same underlying deficit, i.e. impaired ability of PD patients to retain the motor plan associated to the task
From Motor to Trajectory Plan: A feedback loop between unfolding and segmentation to improve writing order recovery
No full textA computational model-based analysis of cerebellar plasticity in motor learning
No full textThe Cerebellum is involved in many cognitive and motor functions. This work focuses on the inspection of the role of the Cerebellum during motor learning. The investigation is led through simulation of
a computational model representing the types and the connections of the different cerebellar neural structures. We inspect the role and the function of the main actor in cerebellar learning: the synaptic plasticity mechanism. The presence of different plasticity sites has been reported in the Cerebellum and this work aims at analysing the role of the two mechanisms of synaptic plasticity: Long-Term Depression (LTD) and Long Term Potentiation (LTP). We want to inspect their relevance in every site and the contribution of the site itself during motor learning. We have investigated the role of the Cerebellum in three different tasks: learning motor behaviours, acquiring conditioned responses and adapting the natural vestibulo-ocular reflex (VOR). We have also simulated lesion-based scenario where we caused artificial lesions at granule cells, the input processing units for all the analysed tasks. Our work remarks some results reported in literature and also leads to some new considerations about the role of some plasticity sites
Automatic Diagnosis of Neurodegenerative Diseases: An Evolutionary Approach for Facing the Interpretability Problem
No full textBackground: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not clearly state the classification rules adopted. Methods: In this framework we propose a classification method based on a Cartesian Genetic Programming (CGP) approach, which allows for the automatic identification of the presence of the disease, and concurrently, provides the explicit classification model used by the system. Results: The proposed approach has been evaluated on the publicly available HandPD dataset, which contains handwriting samples drawn by Parkinson’s disease patients and healthy controls. We show that our approach compares favorably with state-of-the-art methods, and more importantly, allows the physician to identify an explicit model relevant for the diagnosis based on the most informative subset of features. Conclusion: The obtained results suggest that the proposed approach is particularly appealing in that, starting from the explicit model, it allows the physicians to derive a set of guidelines for defining novel testing protocols and intervention strategies
- …
