ArticleIEEE robotics and automation letters2019
Inferring Control Intent during Seated Balance using Inverse Model Predictive Control.
Article in IEEE robotics and automation letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Human-in-the-Loop Robot Control for Human-Robot Collaboration: HUMAN INTENTION ESTIMATION AND SAFE TRAJECTORY TRACKING CONTROL FOR COLLABORATIVE TASKS.IEEE control systems · 2020Article
- Quantifying trunk neuromuscular control using seated balancing and stability threshold.Journal of biomechanics · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Patients with Low Back Pain (LBP) are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Model Predictive Control (iMPC) problem. The standard Model Predictive Control (MPC) structure includes constraints, therefore, it allows us to model the physiological constraints of motor control. We devised an iMPC algorithm to solve iMPC problems with experimentally collected output trajectories. We used experimental data of one healthy subject during a seated balance test that used a physical Human-Robot Interaction (pHRI). Results show that the estimated MPC weights reflected the task instructions given to the subject and yielded an acceptable goodness of fit. The iMPC solution suggests that the subject's control intent was dominated by minimizing the squared sum of a combination of the upper-body and lower-body angles and velocities.
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