Biology subjects

Verdel, D.

Publications and source records attributed to Verdel, D..

5 recordsLinked to original sources

bioRxiv2025

Intermittent movement control emerges from information-based planning

Mammalian motor control is inherently discrete, with movement corrections occurring at rates determined by task demands and the quality of sensory information. While several models have been proposed to explain this discreteness, it remains unclear when a new movement should be initiated and how long it should last. To address this gap, we introduce the Information Predictive Control (IPC) framework, which combines model predictive control with information theory. IPC triggers corrections only when unexpected deviations arise and when corrective actions are likely to succeed. By quantifying "surprise" relative to predicted internal and external states, IPC generates successful movements while robustly integrating sensorimotor noise, task constraints, and target variability. Simulations show that IPC reproduces human-like behavior in discrete reaching, continuous target tracking, and adaptive planning under uncertainty, while dynamically adjusting the planning horizon in complex, unpredictable environments.

neuroscience↗

bioRxiv2025

Model predictive game control for personalized and targeted interactive assistance

Contact robots are increasingly used to assist humans in physical training and manufacturing tasks. However, the effectiveness of current systems is limited as their control focuses on the system performance without explicitly considering the upcoming human users control. Here we present a differential game-based controller for contact robots that ensures optimal interaction with the human user by predicting their control during movement while considering their inherently finite planning horizon. Using this model-predictive game (MPG) controller, we systematically investigated human-robot co-adaptation in experiments, demonstrating that: (a) interaction with MPG remains stable for all participants while effectively reducing human effort; (b) the robot adapts to human behavior, identifying and characterizing individual motor control strategies that remain consistent over time and may be used as control biomarkers; (c) the human adapts to the robots behavior, and their interaction behavior can be modulated through an assistance meta-parameter. These findings indicate that humans can understand and adapt to a partners control strategy during movement, thereby exhibiting behavior consistent with game theory principles. Furthermore, the ability of the assistance meta-parameter to guide human users toward specific interaction behaviors can be used to develop versatile robot-assisted learning systems for physical training and rehabilitation.

bioengineering↗

bioRxiv2024

Co-Contraction Embodies Uncertainty: An Optimal Feedforward Strategy for Robust Motor Control

Despite our environment is often uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a singular relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success. Author summaryThis work presents a computational framework for predicting how humans modulate muscle co-contraction to cope with uncertainties of different origins. In our neuromusculoskeletal system, uncertainties have both internal (sensorimotor noise) and external (environmental randomness) origins. The present study focuses on the latter type of uncertainty, which had not been dealt with systematically previously despite its importance in everyday life. Therefore, we thoroughly investigated how random disturbances occurring with some probability in a motor task shape the feedforward control of mechanical impedance through muscle co-contraction. Here we provide theoretical, numerical and experimental evidence that the optimal level of co-contraction steeply increases with the uncertainty of our environment. These findings show that muscle co-contraction embodies uncertainty and optimally mitigates its consequences on task execution when feedback control is insufficient due to sensory noise and delays.

neuroscience↗

bioRxiv2024

EMG-to-torque models for exoskeleton assistance: a framework for the evaluation of in situ calibration

In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

neuroscience↗

bioRxiv2022

Fast reoptimization of human motor patterns in non-Earth gravity fields locally induced by a robotic exoskeleton

Gravity is a ubiquitous component of our environment that we learnt to optimally integrate in movement control. Yet, altered gravity conditions arise in numerous applications from space exploration to rehabilitation, thereby pressing the sensorimotor system to adapt. Here, we used a robotic exoskeleton to test whether humans can quickly reoptimize their motor patterns in arbitrary gravity fields, ranging from 1g to -1g and passing through Mars- and Moon-like gravities. By comparing the motor patterns of actual arm movements with those predicted by an optimal control model, we show that our participants (N = 61) quickly and optimally adapted their motor patterns to each local gravity condition. These findings show that arbitrary gravity-like fields can be efficiently apprehended by humans, thus opening new perspectives in arm weight support training in manipulation tasks, whether it be for patients or astronauts.

neuroscience↗