Paper Info
Title
Exploiting Inherent Human Motor Behaviour In The Online Personalisation Of Human-Prosthetic Interfaces
Abstract
Human-prosthetic interfaces require their settings to be tuned to individual users. This can potentially be done autonomously while the prosthesis user performs a task by using online personalisation algorithms. These online personalisation algorithms adjust the interface parameters to optimise a given measure of performance. For convergence to be reached, both the human and the personalisation algorithm need to optimise towards the same objective. To date, task-oriented measures of performance have been utilised as the objective, requiring explicit feedback of the measure of performance to the prosthesis user, which is not practical. In this letter, the use of inherent human motor behaviour as the measure of performance for online personalisation algorithms is proposed and investigated. This allows the personalisation procedure to occur without the prosthesis user needing explicit knowledge of the measure of performance. The methodology for formulating inherent human motor behaviour within the framework of online personalisation of human-prosthetic interfaces is presented and validated through an experiment with nine able-bodied subjects. Experimental results demonstrate the efficacy of inherent human motor behaviour-based measures of performance in the design of an intuitive human-prosthetic interface specifically, applicable to human-robot interaction in general.
Year
DOI
Venue
2021
10.1109/LRA.2021.3061351
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Prosthetics and exoskeletons, human-robot collaboration, optimization and optimal control
Journal
6
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ricardo Garcia-Rosas112.07
Tianshi Yu200.68
Denny Oetomo352.88
Chris Manzie431846.66
Ying Tan573786.47
Peter Choong600.68