Preliminary Investigation of Symmetry Learning Control for Powered Ankle-Foot Prostheses

Realmuto, Jonathan; Klute, Glenn K.; Devasia, Santosh

doi:10.1109/wearracon.2019.8719630

Cited by 15 publications

(8 citation statements)

References 23 publications

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“…In terms of benefit, torsion springs are relatively easy to design and are readily available by a variety of manufacturers. Our design is relatively compact and simple compared to prosthesis designs that achieve parallel elasticity using compression springs [43], [44]. Our parallel spring is biased into dorsiflexion, such that the rest angle of the spring is outside of the range of motion of the ankle.…”

Section: Discussionmentioning

confidence: 99%

Design, Control, and Evaluation of a Robotic Ankle-Foot Prosthesis Emulator

Anderson¹,

Hudak²,

Muir³

et al. 2023

Preprint

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<p>People with transtibial limb loss suffer from reduced mobility. Intelligent ankle-foot prostheses have the potential to improve quality of life in people with limb loss, but there are scientific, clinical, and commercial barriers that prevent widespread impact. Further research tools and experiments are needed to expand our understanding of how to design and control intelligent prosthetic limbs. We designed and built a robotic ankle-foot prosthesis with off-board actuation and control to serve as a platform for biomechanical lower limb loss research. Our prosthesis fits inside of a shoe during walking, and attaches to standard clinical prosthesis componentry, including carbon fiber prosthetic footplates and pyramid adapters. Our novel mechanical architecture implements a custom torsion spring in parallel with the ankle joint to allow for dorsiflexion and plantarflexion torque control with a single off-board actuator. Benchtop tests show that our prosthesis has peak plantarflexion torques greater than 175 Nm and a torque control bandwidth of 6.1 Hz. Walking experiments with two participants with lower limb loss indicate that the prosthesis can achieve low torque tracking errors and push-off power greater than the biological ankle during walking. This device will enable future experiments on amputee gait biomechanics, human-robot interaction, and prosthesis control.</p>

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Section: Discussionmentioning

confidence: 99%

Design, Control, and Evaluation of a Robotic Ankle-Foot Prosthesis Emulator

Anderson¹,

Hudak²,

Muir³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A safety harness was included in the experimental setup as any data collected when the subject touched the treadmill handrails were discarded. A novel human-in-the-loop symmetry learning strategy was used to control the PAFP, where an adaptive gain iterative learning control algorithm adjusts the PAFP’s torque after each walking trial to match the achieved intact ankle torque [ 32 ]. A total of 23 walking trials were conducted, with each trial having a unique control command trajectory and prosthetic ankle torque profile.…”

Section: Methodsmentioning

confidence: 99%

Using Deep Learning Models to Predict Prosthetic Ankle Torque

Prasanna,

Realmuto,

Anderson

et al. 2023

Sensors

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Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six input signals. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04 ± 0.02 Nm/kg, corresponding to 2.9 ± 1.6% of the ankle torque’s dynamic range. Comparatively, a manually derived analytical regression model predicted ankle torques with a RMSE of 0.35 ± 0.53 Nm/kg, corresponding to 26.6 ± 40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average decrease in performance of 1.7% of the ankle torque’s dynamic range when compared to the one-sample-ahead prediction. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.

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“…A complete support phase can be divided into one-foot support state and two-foot support state. In the stage of single foot support, the driving torque of hip joint and knee joint of swinging leg is larger than that of bipedal support state; because the ankle joint mainly adjusts the direction of human motion in the horizontal plane, the joint driving torque provided in the sagittal plane is smaller [14].…”

Section: Dynamic Model Of Lower Limb Exoskeleton Robotmentioning

confidence: 98%

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

2021

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At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.

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Preliminary Investigation of Symmetry Learning Control for Powered Ankle-Foot Prostheses

Cited by 15 publications

References 23 publications

Design, Control, and Evaluation of a Robotic Ankle-Foot Prosthesis Emulator

Design, Control, and Evaluation of a Robotic Ankle-Foot Prosthesis Emulator

Using Deep Learning Models to Predict Prosthetic Ankle Torque

Joint Motion Control for Lower Limb Rehabilitation Based on Iterative Learning Control (ILC) Algorithm

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