Publications by authors named "Ferdinando A Mussa Ivaldi"

Article Synopsis
  • * In a study involving non-injured participants, the effectiveness of a high-dimensional BoMI to control a robotic arm was examined over five sessions.
  • * Findings indicate that how robot control is mapped from human inputs affects learning; while joint space control is easier to start with, task space control leads to better long-term performance and learning outcomes.
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Individuals who suffer from severe paralysis often lose the capacity to perform fundamental body movements and everyday activities. Empowering these individuals with the ability to operate robotic arms, in high degrees-of-freedom (DoFs), can help to maximize both functional utility and independence. However, robot teleoperation in high DoFs currently lacks accessibility due to the challenge in capturing high-dimensional control signals from the human, especially in the face of motor impairments.

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In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control.

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Dimensionality reduction techniques have proven useful in simplifying complex hand kinematics. They may allow for a low-dimensional kinematic or myoelectric interface to be used to control a high-dimensional hand. Controlling a high-dimensional hand, however, is difficult to learn since the relationship between the low-dimensional controls and the high-dimensional system can be hard to perceive.

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Objective: Body machine interfaces (BoMIs) enable individuals with paralysis to achieve a greater measure of independence in daily activities by assisting the control of devices such as robotic manipulators. The first BoMIs relied on Principal Component Analysis (PCA) to extract a lower dimensional control space from information in voluntary movement signals. Despite its widespread use, PCA might not be suited for controlling devices with a large number of degrees of freedom, as because of PCs' orthonormality the variance explained by successive components drops sharply after the first.

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Background: The recovery of upper limb mobility and functions is essential for people with cervical spinal cord injuries (cSCI) to maximize independence in daily activities and ensure a successful return to normality. The rehabilitative path should include a thorough neuromotor evaluation and personalized treatments aimed at recovering motor functions. Body-machine interfaces (BoMI) have been proven to be capable of harnessing residual joint motions to control objects like computer cursors and virtual or physical wheelchairs and to promote motor recovery.

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In human-machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human-machine joint performance.

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Several studies have reported that stroke survivors displayed improved voluntary planar movements when forces supporting the upper limb increased, and when impeding forces decreased. Earlier haptic devices interacting with the human upper limb were potentially impacted by undesired residual friction force and device inertia. To explore natural, undisturbed voluntary motor control in stroke survivors, we describe the development of a Decoupled-Operational space Robot for wide Impedance Switching (DORIS) with minimized mechanical impedances.

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The purpose of this study was to find a parsimonious representation of hand kinematics data that could facilitate prosthetic hand control. Principal Component Analysis (PCA) and a non-linear Autoencoder Network (nAEN) were compared in their effectiveness at capturing the essential characteristics of a wide spectrum of hand gestures and actions. Performance of the two methods was compared on (a) the ability to accurately reconstruct hand kinematic data from a latent manifold of reduced dimension, (b) variance distribution across latent dimensions, and (c) the separability of hand movements in compressed and reconstructed representations derived using a linear classifier.

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Background: Body-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency.

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When manipulating objects, we use kinesthetic and tactile information to form an internal representation of their mechanical properties for cognitive perception and for preventing their slippage using predictive control of grip force. A major challenge in understanding the dissociable contributions of tactile and kinesthetic information to perception and action is the natural coupling between them. Unlike previous studies that addressed this question either by focusing on impaired sensory processing in patients or using local anesthesia, we used a behavioral study with a programmable mechatronic device that stretches the skin of the fingertips to address this issue in the intact sensorimotor system.

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A medical student learning to perform a laparoscopic procedure or a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for an external device. Since the user's actions are selected from a number of alternatives that would result in the same effect in the control space of the external device, learning to use such interfaces involves dealing with redundancy. Subjects need to learn an externally chosen many-to-one map that transforms their actions into device commands.

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The simultaneous control of force and motion is important in everyday activities when humans interact with objects. While many studies have analyzed the control of movement within a perturbing force field, few have investigated its dual aspects of controlling a contact force in nonisometric conditions. The mechanism by which the central nervous system controls forces during movements is still unclear, and it can be elucidated by estimating the mechanical properties of the arm during tasks with concurrent motion and contact force goals.

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The sensory system constantly deals with delayed feedback. Recent studies showed that playing a virtual game of pong with delayed feedback caused hypermetric reaching movements. We investigated whether this effect is associated with a perceptual bias.

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When interacting with the environment, the sensorimotor system faces temporal and spatial discrepancies between sensory inputs, such as delay in sensory information transmission, and asymmetrical visual inputs across space. These discrepancies can affect motor control and the representation of space. We recently showed that adaptation to a laterally asymmetric delay in the visual feedback induces neglect-like effects in blind drawing movements, expressed by asymmetrical elongation of circles that are drawn in different workspaces and directions; this establishes a possible connection between delayed feedback and asymmetrical spatial processing in the control of action.

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A majority of the seven million stroke survivors in the U.S. have hand impairments, adversely affecting performance of a variety of activities of daily living, because of the fundamental role of the hand in performing functional tasks.

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Background: Skill assessment in surgery traditionally has relied on the expert observation and qualitative scoring. Our novel study design demonstrates how analysis of performance in sensorimotor tasks and bench-top surgical simulators can provide inferences about the technical proficiency as well as the training history of surgeons.

Methods: We examined metrics for basic sensorimotor tasks in a virtual reality interface as well as motion metrics in clinical scenario simulations.

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In daily interactions, our sensorimotor system accounts for spatial and temporal discrepancies between the senses. Functional lateralization between hemispheres causes differences in attention and in the control of action across the left and right workspaces. In addition, differences in transmission delays between modalities affect movement control and internal representations.

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The brain must consider the arm's inertia to predict the arm's movements elicited by commands impressed upon the muscles. Here, we present evidence suggesting that the integration of sensory information leading to the representation of the arm's inertia does not take place continuously in time but only at discrete transient events, in which kinetic energy is exchanged between the arm and the environment. We used a visuomotor delay to induce cross-modal variations in state feedback and uncovered that the difference between visual and proprioceptive velocity estimations at isolated collision events was compensated by a change in the representation of arm inertia.

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To accurately estimate the state of the body, the nervous system needs to account for delays between signals from different sensory modalities. To investigate how such delays may be represented in the sensorimotor system, we asked human participants to play a virtual pong game in which the movement of the virtual paddle was delayed with respect to their hand movement. We tested the representation of this new mapping between the hand and the delayed paddle by examining transfer of adaptation to blind reaching and blind tracking tasks.

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When we knock on a door, we perceive the impact as a collection of simultaneous events, combining sound, sight, and tactile sensation. In reality, information from different modalities but from a single source is flowing inside the brain along different pathways, reaching processing centers at different times. Therefore, interpreting different sensory modalities which seem to occur simultaneously requires information processing that accounts for these different delays.

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Article Synopsis
  • Internal representations are formed to adapt to deterministic force perturbations based on the current state of the hand, but varying information travel rates create intermodal delays that need to be compensated.
  • In an experiment, participants were exposed to delayed velocity-dependent force fields, and the study examined how they adapted their applied forces to manage these perturbations, revealing a reliance on both current and delayed velocity information.
  • The results indicated that while participants initially relied on the current state, adaptation increased their ability to incorporate delayed representations, showing a flexible sensorimotor system that adjusts forces for smoother movements, even at faster velocities.
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Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility.

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The purpose of this study was to identify rehabilitative effects and changes in white matter microstructure in people with high-level spinal cord injury following bilateral upper-extremity motor skill training. Five subjects with high-level (C5-C6) spinal cord injury (SCI) performed five visuo-spatial motor training tasks over 12 sessions (2-3 sessions per week). Subjects controlled a two-dimensional cursor with bilateral simultaneous movements of the shoulders using a non-invasive inertial measurement unit-based body-machine interface.

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Unlabelled: Each of our movements is selected from any number of alternative movements. Some studies have shown evidence that the central nervous system (CNS) chooses to make the specific movements that are least affected by motor noise. Previous results showing that the CNS has a natural tendency to minimize the effects of noise make the direct prediction that if the relationship between movements and noise were to change, the specific movements people learn to make would also change in a predictable manner.

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