Robust compensation of electromechanical delay during neuromuscular electrical stimulation of antagonistic muscles

Qiu, Tianyi; Alibeji, Naji; Sharma, Nitin

doi:10.1109/acc.2016.7526124

Cited by 8 publications

(2 citation statements)

References 46 publications

(92 reference statements)

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“…Models of the muscle response to electrical stimulation are available and could be incorporated as a feedforward element to the existing controller to ensure muscle contraction resulting from FES occurs in a similar time range as the robotic torque assistance for the motion to be produced [48]- [51]. Recent works have also targeted the development of controllers to reduce the effect of the electromechanical delay of the stimulation [52], [53], which would lead to a better alignment of the motion resulting from the two actuation systems. However, these approaches for delay compensation are not considering the time-varying nature of the electromechanical delay [43], and are applied to cyclic gait motion or to the tracking of predetermined trajectories, hence they would not be suitable for this pseudo-random tracking task.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Cazenave,

Einenkel,

Yurkewich

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users' performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance.

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Section: Discussionmentioning

confidence: 99%

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Cazenave,

Einenkel,

Yurkewich

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The two aforementioned approaches were implemented on several practical systems. In (Sharma, Gregory, & Dixon, 2011;Qiu, Alibeji, & Sharma, 2016), the robust predictive controllers were applied to neuromuscular rehabilitation systems, and a similar delay compensation technique was further designed for musculoskeletal wearable systems (Alibeji, Dicianno, & Sharma, 2017;Alibeji, Kirsch, & Sharma, 2017;Sheng, Sun, Molazadeh, & Sharma, 2021). The second method (62)-( 63) handled the accurate grasp-place control of a 7-DoF Baxter manipulator (Bagheri et al, 2018(Bagheri et al, , 2019b(Bagheri et al, , 2019b.…”

Section: Predictor-based Control For Mechanical/robotic Euler-lagrang...mentioning

confidence: 99%

Predictor-based control of time-delay systems: a survey

Deng

Léchappé

Moulay

et al. 2022

International Journal of Systems Science

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With the developments of wireless data communication and network technology, time-delays are widely found in nowadays' control systems, e.g. networked control systems, mobile robot systems, and multiagent systems. Predictor-based control is an effective method dealing with long time-delays because it can generally lead to a delay-free closed-loop system by introducing a prediction for future states. Recently, various predictor-based control methods have been developed for numerous control systems subject to different time-delays, which motivates this survey. This paper presents a comprehensive review of the up-to-date results on the predictor-based control of time-delay systems. Firstly, the ordinary differential equation-based approaches for designing and analysing predictor-based controllers are summarised. Secondly, one reports an alternative method of predictor-based control, in which the systems/controllers are understood in the sense of partial differential equations. Next, several integration-free predictor-based controllers are introduced: by abandoning the infinite-dimensional integral terms, the control laws become easier to realise in practice. Hereafter, the paper discusses the real-time implementations and the practical applications of predictor-based control methods to several particular control systems. Finally, this paper suggests some new trends of predictor-based control for future research.

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Deep Neural Network-Based Adaptive FES-Cycling Control: A Hybrid Systems Approach

Griffis

Isaly

et al. 2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Robust compensation of electromechanical delay during neuromuscular electrical stimulation of antagonistic muscles

Cited by 8 publications

References 46 publications

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Predictor-based control of time-delay systems: a survey

Deep Neural Network-Based Adaptive FES-Cycling Control: A Hybrid Systems Approach

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