1,721,078 research outputs found
Synergy-Based Myocontrol of a Multiple Degree-of-Freedom Humanoid Robot for Functional Tasks
No full textIn 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
Motor learning onboard the ISS: the kinematic and dynamic strategies in whole body motor tasks.
No full textControl of a humanoid NAO robot by an adaptive bioinspired cerebellar module in 3D Motion tasks
Full text linkA bioinspired adaptive model, developed by means of a spiking neural network made of thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real time. The learning properties of the system have been challenged in a classic cerebellum-driven paradigm, a perturbed upper limb reaching protocol. The neurophysiological principles used to develop the model succeeded in driving an adaptive motor control protocol with baseline, acquisition, and extinction phases. The spiking neural network model showed learning behaviours similar to the ones experimentally measured with human subjects in the same task in the acquisition phase, while resorted to other strategies in the extinction phase. The model processed in real-time external inputs, encoded as spikes, and the generated spiking activity of its output neurons was decoded, in order to provide the proper correction on the motor actuators. Three bidirectional long-term plasticity rules have been embedded for different connections and with different time scales. The plasticities shaped the firing activity of the output layer neurons of the network. In the perturbed upper limb reaching protocol, the neurorobot successfully learned how to compensate for the external perturbation generating an appropriate correction. Therefore, the spiking cerebellar model was able to reproduce in the robotic platform how biological systems deal with external sources of error, in both ideal and real (noisy) environments
Corrigendum: Reconstruction and Simulation of a Scaffold Model of the Cerebellar Network
No full text: [This corrects the article DOI: 10.3389/fninf.2019.00037.]
Whole-Body Movements in Long-Term Weightlessness: Hierarchies of the Controlled Variables Are Gravity-Dependent
No full textSpiking Cerebellar Model with Damaged Cortical Neural Population Reproduces Human Ataxic Behaviors in Perturbed Upper Limb Reaching
No full textThe fundamental role of the cerebellum in motor learning explains the deficits of cerebellar patients in adaptation to a changing environment. For example, lesions to the cerebellar cortex compromise performance during tasks like reaching a target under a force field perturbation. However, the exact relationship between neural damages and misbehaviors still needs to be clarified. To this aim, it could become a turning point to exploit a bio-inspired cerebellar model able to simulate multiple tasks in closed-loop, under physiological and different pathological conditions. In the present study, we used a well-established Spiking Neural Network representing a cerebellar microcomplex to reproduce alterations in a perturbed reaching task, after lesions to the neural population in the cerebellar cortex. Following a multiscale approach, we explored different amounts of damage and analyzed the modified behavior, matching the results of a literature reference study. Then, we could make predictions about the underlying altered neural activity, showing the neural causes of high-level impairments. The results demonstrate the generalization capabilities of the model, extending previous studies on different lesions and tasks. We showed the strong potentialities of computational neuroscience in investigating cerebellar diseases through a non-invasive approach, allowing to isolate damages, test multiple configurations, and suggest treatments thanks to a deeper understanding of pathologie
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