Biology subjects

Bruneau, O.

Publications and source records attributed to Bruneau, O..

5 recordsLinked to original sources

bioRxiv2025

Less is more: uncompensated gravity torques for intuitive EMG-based assistance with a robotic exoskeleton

Despite extensive investigation on the use of electromyographic (EMG) activity to control active exoskeletons over the past decade, designing intuitive assistive controllers that seamlessly integrate with natural human motor control have yet to be realized. While existing EMG-based controllers often achieve substantial reduction in muscle effort, they frequently incur increased cognitive and attentional load for the user, thereby compromising the overall efficacy of the assistance. Here we introduce a novel EMG-based assistive controller founded upon neuroscience principles, specifically the observation that humans naturally exploit gravity torque to facilitate movement control. Therefore, deviating from conventional compensation strategies, our approach purposely leaves a fraction of the predicted human gravity torque uncompensated so that users can still take advantage of gravity as they would without assistance. Through a load-carrying arm movement task, we show that enabling gravity exploitation improves traditional EMGbased assistance by achieving a significant reduction in muscle effort, while concurrently yielding superior kinematic performance (i.e., faster, smoother movements) and enhanced subjective user experience. These findings demonstrate that integrating principles from neural motor control into assistive controllers allows to implement a favorable tradeoff between muscle effort reduction and functional usability.

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

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↗

bioRxiv2023

The value of time in the invigoration of human movements when interacting with a robotic exoskeleton

Time and effort are critical factors that are thought to be subjectively balanced during the planning of goal-directed actions, thereby setting the vigor of volitional movements. Theoretical models predicted that the value of time should then amount to relatively high levels of effort. However, the time-effort tradeoff has so far only been studied for a narrow range of efforts. Therefore, the extent to which humans can invest in a time-saving effort remains largely unknown. To address this issue, we used a robotic exoskeleton which significantly varied the energetic cost associated with a certain vigor during reaching movements. In this situation, minimizing the time-effort tradeoff would lead to high and low human efforts for upward and downward movements respectively. Consistent with this prediction, results showed that all participants expended substantial amounts of energy to pull on the exoskeleton during upward movements and remained essentially inactive by harnessing the work of gravity to push on the exoskeleton during downward movements, while saving time in both cases. These findings show that a common tradeoff between time and effort can determine the vigor of reaching movements for a wide range of efforts, with time cost playing a pivotal role.

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↗