2023
DOI: 10.3390/biomimetics8040367
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Transhumeral Arm Reaching Motion Prediction through Deep Reinforcement Learning-Based Synthetic Motion Cloning

Abstract: The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation–supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily … Show more

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Cited by 2 publications
(1 citation statement)
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“…The explicit dynamic modeling of a prosthetic running blade involves simulating the real-time physical behaviors of the blade under various conditions, such as different speeds, angles of impact, and forces. This type of analysis is crucial for understanding how the blade will perform under the rapid and high-stress conditions experienced during running (Bellmann et al, 2012;Zhang et al, 2019;Ahmed et al, 2023;Coltelli et al, 2023). It allows for examining stress distribution, strain patterns, and energy absorption and release, which are vital for optimizing the blade's design for performance and safety.…”
Section: Introductionmentioning
confidence: 99%
“…The explicit dynamic modeling of a prosthetic running blade involves simulating the real-time physical behaviors of the blade under various conditions, such as different speeds, angles of impact, and forces. This type of analysis is crucial for understanding how the blade will perform under the rapid and high-stress conditions experienced during running (Bellmann et al, 2012;Zhang et al, 2019;Ahmed et al, 2023;Coltelli et al, 2023). It allows for examining stress distribution, strain patterns, and energy absorption and release, which are vital for optimizing the blade's design for performance and safety.…”
Section: Introductionmentioning
confidence: 99%