User:AlbertoGnemmi
Alberto Gnemmi
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| First Name: | Alberto |
| Last Name: | Gnemmi |
| E-Mail: | gnemmi.alberto@gmail.com |
| Advisor: | |
| Project page: | Learning movements with robot and buzzer |
| Project page(s): | Learning movements with robot and buzzer |
| Status: | active |
Error-Encoded Vibrotactile Feedback to Enhance Motor Adaptation
The study on human learning of motor proficiency and the potential of accelerating this learning process is a topic of extreme interest with a number of possible applications.
The research in this area would be beneficial to a variety of people: Athletes, surgeons, pilots, and technicians that perform high precision or high-risk tasks, casual sportsmen and artists who would like to learn a skill faster, or people with movement disorders who have to relearn skills necessary for everyday life.
Learning to move skillfully requires that the motor system adjusts muscle commands based on ongoing performance errors, a process influenced by the dynamics of the task being practiced.
Modern Augmented Reality (AR) environments, combined with robotic devices, give to the researchers a complete control over the task performed by the subjects. From analyzing the subjects’ reaction to sudden changes in the dynamic of the task, it is possible to infer how the motor learning system works.
The aim of my research is to study the way humans learn to perform a simple motor task and how it is possible to speed up this process. Recent studies from my laboratory suggest that the use of robotic devices and AR systems can be successfully used to improve the motor learning process. An effective way to achieve this objective is to artificially enhance the errors performed by the subjects. Up to now this technique has been used with a visual feedback (giving the impression of a higher error) and/or with a force feedback (increasing de facto the error). In both these cases it led to a substantial increase of the task’s learning rate.
The precise objective of my research is to understand if it is possible to obtain similar results with a vibrotactile feedback. This task is made to be particularly challenging by the psychophysical characteristics of human haptic system and by the predominance of the visual system over it.
A successful result could lead, for example, to a new generation of devices, which will not necessary require a complex and expensive AR environment, but only some vibrotactile devices and accelerometers.
