Biology subjects

Mussa-Ivaldi, F. A.

Publications and source records attributed to Mussa-Ivaldi, F. A..

5 recordsLinked to original sources

bioRxiv2023

An Exploratory Multi-Session Study of Learning High-Dimensional Body-Machine Interfacing for Assistive Robot Control

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-dimensions, helps to maximize both functional utility and human agency. However, high-dimensional robot teleoperation currently lacks accessibility due to the challenge in capturing high-dimensional control signals from the human, especially in the face of motor impairments. Body-machine interfacing is a viable option that offers the necessary high-dimensional motion capture, and it moreover is noninvasive, affordable, and promotes movement and motor recovery. Nevertheless, to what extent body-machine interfacing is able to scale to high-dimensional robot control, and whether it is feasible for humans to learn, remains an open question. In this exploratory multi-session study, we demonstrate the feasibility of human learning to operate a body-machine interface to control a complex, assistive robotic arm in reaching and Activities of Daily Living tasks. Our results suggest the manner of control space mapping, from interface to robot, to play a critical role in the evolution of human learning.

bioengineering↗

bioRxiv2022

Diversity of Learning to Control Complex Rehabilitation Robots Using High-Dimensional Interfaces

Upper body function is lost when injuries are sustained to the cervical spinal cord. Assistive machines can support the loss in upper body motor function. To regain functionality at the level of performing activities of daily living (e.g., self-feeding), though, assistive machines need to be able to operate in high dimensions. This means there is a need for interfaces with the capability to match high-dimensional operation. The body-machine interface provides this capability and has shown to be a suitable interface even for individuals with limited mobility. This is because it can take advantage of peoples available residual body movements. Previous studies using this interface have only shown that the interface can control low-dimensional assistive machines. In this pilot study, we demonstrate the interface can scale to high-dimensional robots, can be learned to control a 7-dimensional assistive robotic arm, to perform complex reaching and functional tasks, by an uninjured population. We also share results from various analyses that hint at learning, even when performance is extremely low. Decoupling intrinsic correlations between robot control dimensions seem to be a factor in learning--that is, proficiency in activating each control dimension independently may contribute to learning and skill acquisition of high-dimensional robot control. In addition, we show that learning to control the robot and learning to perform complex movement tasks can occur simultaneously.

bioengineering↗

bioRxiv2019

The dynamics of motor learning through the formation of internal models

A medical student learning to perform a laparoscopic procedure as well as a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for the external device. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of learning dynamics and the performance observed in a group of subjects demonstrate first-order exponential convergence of the learning process toward a particular state that depends only on the initial inverse and forward models and on the supplied sequence of targets. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.\n\nAuthor summarySeveral studies have suggested that as we learn a new skill our brain forms representations, or \"internal models\", of the skill and of the environment in which we operate. Theories of motor learning postulate that the brain builds forward models that predict the sensory consequences of motor commands, and inverse models that generate successful commands from planned movements. We test this hypothesis taking advantage of a special interface that generates a novel relation between the subjects actions and the position of a cursor on a computer monitor, thus allowing subjects to control an external device by movements of their body. We recorded the motions of the body and of the cursor, and obtained estimates of both forward and inverse models. We followed how these estimates evolved in time as subjects practiced and acquired a new skill. We found that the description of learning as a simple deterministic process driven by the sequence of targets is sufficient to capture the observed convergence to a single solution of the inverse model among an infinite variety of alternative possibilities. This work is relevant to the study of fundamental learning mechanisms as well as to the design of intelligent interfaces for people with paralysis.

neuroscience↗

bioRxiv2017

Energy Exchanges at Contact Events Guide Sensorimotor Integration Across Intermodal Delays

One must know the mass of an object to accurately predict how it moves under the effect of an applied force. Similarly, the brain must represent the arms inertia to predict the arms 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 arms 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 crossmodal 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. The compensation maintained an invariant estimate across modalities of the expected energy exchange with the environment. This invariance captures different types of dysmetria observed across individuals following prolonged exposure to a fixed intermodal temporal perturbation and provides a new interpretation for cerebellar ataxia.

neuroscience↗

bioRxiv2017

State-Based Delay Representation and Its Transfer from a Game of Pong to Reaching and Tracking

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. These blind tasks enabled to capture the representation in feedforward mechanisms of movement control. A Time Representation of the delay is an estimation of the actual time lag between hand and paddle movements. A State Representation is a representation of delay using current state variables: the distance between the paddle and the ball originating from the delay may be considered as a spatial shift; the low sensitivity in the response of the paddle may be interpreted as a minifying gain; and the lag may be attributed to a mechanical resistance that influences paddles movement. We found that the effects of prolonged exposure to the delayed feedback transferred to blind reaching and tracking tasks and caused participants to exhibit hypermetric movements. These results, together with simulations of our representation models, suggest that delay is not represented based on time, but rather as a spatial gain change in visuomotor mapping.\n\nSignificance StatementIt is known that the brain copes with sensory feedback delays to control movements, but it is unclear whether it does so using a representation of the actual time lag. We addressed this question by exposing participants to a visuomotor delay during a dynamic game of pong. Following the game, participants exhibited hypermetric reaching and tracking movements that indicate that delay is represented as a visuomotor gain rather than as a temporal shift.

neuroscience↗