Learning Control and Computational Models of Human Motor Systems
Grant number: DP130100849 | Funding period: 2013 - 2017
With the aim of understanding how humans learn their body movements, this project addresses fundamental cross-disciplinary issues of learning control, robotics and computational models of human motor systems. The results will lead to improvements in smart industrial automation and the development of more effective rehabilitation stategies.
Related publications (8)
Dual-loop iterative optimal control for the finite horizon LQR problem with unknown dynamics
Justin Fong, Ying Tan, Vincent Crocher, Denny Oetomo, Iven Mareels
Achieving optimal performance over a finite-time horizon has gained a lot of attention in many engineering applications. Among the..
Modeling of Endpoint Feedback Learning Implemented Through Point-to-Point Learning Control
Shou-Han Zhou, Ying Tan, Denny Oetomo, Chris Freeman, Etienne Burdet, Iven Mareels
In the last decade, several experiments were conducted to investigate human motor control behavior for the task of arm reaching, u..
Calibration Free Upper Limb Joint Motion Estimation Algorithm with Wearable Sensors
Max van Lith, Justin Fong, Vincent Crocher, Ying Tan, Iven Mareels, Denny Oetomo
This paper aims to establish a post processing algorithm to estimate the upper limb motion, given a set of measurements from weara..
An Investigation into the Reliability of Upper-limb Robotic Exoskeleton Measurements for Clinical Evaluation in Neurorehabilitation
Justin Fong, Vincent Crocher, Denny Oetomo, Ying Tan
Robotic exoskeletons are increasingly being used for neurorehabilitation, due to a number of perceived advantages. Once such advan..