Modulation of Prosthetic Ankle Plantarflexion Through Direct Myoelectric Control of a Subject-Optimized Neuromuscular Model

Shu, Tony; Shallal, Christopher; Chun, Ethan; Shah, Aashini; Bu, Angel; Levine, Daniel H.; Yeon, Sun-Hwa; Carney, M. F.; Song, Hyungeun; Hsieh, Tsung‐Han; Herr, Hugh M.

doi:10.31224/2325

Cited by 2 publications

(5 citation statements)

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“…They are solved for the articular target acceleration qt that best satisfy their objectives. Numerical integration from the previous articular position and velocity targets yields the support leg target position and velocity (q sp * , qsp * ) tracked using (8). Set-points for Level 2 and 3 of both SoT 1 and SoT 2 are computed from the nominal gait T .…”

Section: Discussionmentioning

confidence: 99%

“…To this end, we rely on the Virtual Guides (VG) methodology [6], [7]. Resembling [8], our VG approach maps the high-dimensional user efforts to a one-dimensional quantity: the velocity at which the swing leg's geometric path is followed. Implicitly, this defines a new schedule for the path, the patient schedule, which we aim to follow as long as safety is not threatened.…”

Section: Introductionmentioning

confidence: 99%

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Fast replanning of a lower-limb exoskeleton trajectories for rehabilitation

Brunet¹,

Marine²,

Meglio

et al. 2022

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

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This paper exposes a control architecture enabling rehabilitation of walking impaired patients with the lower-limb exoskeleton Atalante. Atalante's control system is modified to allow the patient to contribute to the walking motion through their efforts. Only the swing leg degree of freedom along the nominal path is relaxed. An online trajectory optimization checks that the muscle forces do not jeopardize stability. The optimization generates reference trajectories that satisfy several key constraints from the current point to the end of the step. One of the constraints requires that the center or pressure remains inside the support polygon, which ensures that the support leg subsystem successfully tracks the reference trajectory. As a result of the presented works, the robot provides a non-zero force in the direction of motion only when required, helping the patient go fast enough to maintain balance (or preventing him from going too fast). Experimental results are reported. They illustrate that variations of ±50% of the duration of the step can be achieved in response to the patient's efforts and that many steps are achieved without falling. A video of the experiments can be viewed at https://youtu.be/_1A-2nLy5ZE.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast replanning of a lower-limb exoskeleton trajectories for rehabilitation

Brunet¹,

Marine²,

Meglio

et al. 2022

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

View full text Add to dashboard Cite

show abstract

“…This was adapted to also support stair ambulation by Kannape and Herr (2014). Shu et al (2022) applied a similar control approach with an offline optimization method to map EMG inputs to the virtual muscles.…”

Section: Reflex-driven Muscle Model Based Controlmentioning

confidence: 99%

“…Markowitz et al (2011) recognize this circumstance, but do not incorporate any user-controllable feed-forward signals in their control approach. Likewise, Shu et al (2022) call reflexes essential for physiological gait and lament its absence in amputation musculature, but their device control relies on feed-forward signals alone. To our knowledge, the combination of muscle reflexes with feed-forward signals has only been tested in gait simulation, where it improved the model's robustness against perturbations compared to pure reflex control (Haeufle et al, 2018).…”

Section: Combining Emg Input and Simulated Reflexesmentioning

confidence: 99%

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Intuitive and versatile bionic legs: a perspective on volitional control

Voß,

Koelewijn,

Beckerle

2024

Front. Neurorobot.

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Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.

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Modulation of Prosthetic Ankle Plantarflexion Through Direct Myoelectric Control of a Subject-Optimized Neuromuscular Model

Cited by 2 publications

References 0 publications

Fast replanning of a lower-limb exoskeleton trajectories for rehabilitation

Fast replanning of a lower-limb exoskeleton trajectories for rehabilitation

Intuitive and versatile bionic legs: a perspective on volitional control

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