Skip to Main content Skip to Navigation
Theses

Characterizing the reciprocal adaptation in physical human-robot interaction to address the inter-joint coordination in neurorehabilitation

Abstract : While many robotic exoskeletons have been developed for stroke rehabilitation in recent years, there were not yet improvements to the traditional therapy. A key to unleash the potentiality of robotics is to adapt the assistance provided by the robot in order to maximize the subject engagement and effort, by having the robotic therapy evolving with the patient recovery. For this reason, we aim at better understanding the process of reciprocal adaptation in a context of physical Human-Robot Interaction (pHRI). We first developed a new adaptive controller, which assists the subject "as-needed", by regulating its interaction to maximize the human involvement. We further compared different signals driving this adaptation, to better following the functional recovery level of the patients. While the control is performed by the robot, the subject is also adapting his movements, and this adaptation has not yet been studied when dealing with 3D movements and exoskeletons. Therefore, we exposed human motions to distributed force fields, generated by the exoskeleton at the joint level, to produce specific inter-joint coordination and to analyse the effects of this exposition. With healthy participants, we observed important inter-individual difference, with adaptation to the fields in 21% of the participants, but post-effects and persisting retention of these in time in 85% of the subjects, together with spatial generalization, and, preliminarily, transfer of the effects outside of the exoskeleton context. This work towards understanding pHRI could provide insights on innovative ways to develop new controllers for improving stroke motor recovery with exoskeletons.
Document type :
Theses
Complete list of metadatas

Cited literature [232 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-01919847
Contributor : Abes Star :  Contact
Submitted on : Monday, November 12, 2018 - 5:23:08 PM
Last modification on : Friday, May 29, 2020 - 3:59:51 PM
Long-term archiving on: : Wednesday, February 13, 2019 - 5:00:34 PM

File

2017PA066589.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01919847, version 1

Citation

Tommaso Proietti. Characterizing the reciprocal adaptation in physical human-robot interaction to address the inter-joint coordination in neurorehabilitation. Robotics [cs.RO]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066589⟩. ⟨tel-01919847⟩

Share

Metrics

Record views

303

Files downloads

91