ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2020
Wearer-Prosthesis Interaction for Symmetrical Gait: A Study Enabled by Reinforcement Learning Prosthesis Control.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 47 citations in OpenAlex.
- Intuitive and versatile bionic legs: a perspective on volitional control.Frontiers in neurorobotics · 2024Article
- Gait quality in prosthesis users is reflected by force-based metrics when learning to walk on a new research-grade powered prosthesis.Frontiers in rehabilitation sciences · 2024Article
- A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses.Annual reviews in control · 2023Article
- Human-prosthesis cooperation: combining adaptive prosthesis control with visual feedback guided gait.Journal of neuroengineering and rehabilitation · 2022Article
- Foot contact forces can be used to personalize a wearable robot during human walking.Scientific reports · 2022Article
- Visual guidance can help with the use of a robotic exoskeleton during human walking.Scientific reports · 2022Article
- Taking Both Sides: Seeking Symbiosis Between Intelligent Prostheses and Human Motor Control during Locomotion.Current opinion in biomedical engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
With advances in robotic prostheses, rese-archers attempt to improve amputee's gait performance (e.g., gait symmetry) beyond restoring normative knee kinematics/kinetics. Yet, little is known about how the prosthesis mechanics/control influence wearer-prosthesis' gait performance, such as gait symmetry, stability, etc. This study aimed to investigate the influence of robotic transfemoral prosthesis mechanics on human wearers' gait symmetry. The investigation was enabled by our previously designed reinforcement learning (RL) supplementary control, which simultaneously tuned 12 control parameters that determined the prosthesis mechanics throughout a gait cycle. The RL control design facilitated safe explorations of prosthesis mechanics with the human in the loop. Subjects were recruited and walked with a robotic transfemoral prosthesis on a treadmill while the RL controller tuned the control parameters. Stance time symmetry, step length symmetry, and bilateral anteroposterior (AP) impulses were measured. The data analysis showed that changes in robotic knee mechanics led to movement variations in both lower limbs and therefore gait temporal-spatial symmetry measures. Consistent across all the subjects, inter-limb AP impulse measurements explained gait symmetry: the stance time symmetry was significantly correlated with the net inter-limb AP impulse, and the step length symmetry was significantly correlated with braking and propulsive impulse symmetry. The results suggest that it is possible to personalize transfemoral prosthesis control for improved temporal-spatial gait symmetry. However, adjusting prosthesis mechanics alone was insufficient to maximize the gait symmetry. Rather, achieving gait symmetry may require coordination between the wearer's motor control of the intact limb and adaptive control of the prosthetic joints. The results also indicated that the RL-based prosthesis tuning system was a potential tool for studying wearer-prosthesis interactions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.