Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces

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PubDate: Feb 2020

Teams: The University of Melbourne

Writers: Ricardo Garcia-Rosas, Ying Tan, Denny Oetomo, Chris Manzie, Peter Choong

PDF: Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces

Abstract

Synergies have been adopted in prosthetic limb applications to reduce complexity of design, but typically involve a single synergy setting for a population and ignore individual preference or adaptation capacity. However, personalization of the synergy setting is necessary for the effective operation of the prosthetic device. Two major challenges hinder the personalization of synergies in human-prosthesis interfaces. The first is related to the process of human motor adaptation and the second to the variation in motor learning dynamics of individuals. In this paper, a systematic personalization of kinematic synergies for human-prosthesis interfaces using on-line measurements from each individual is proposed. The task of reaching using the upper-limb is described by an objective function and the interface is parameterized by a kinematic synergy. Consequently, personalizing the interface for a given individual can be formulated as finding an optimal personalized parameter. A structure to model the observed motor behavior that allows for the personalized traits of motor preference and motor learning is proposed, and subsequently used in an on-line optimization scheme to identify the synergies for an individual. The knowledge of the common features contained in the model enables on-line adaptation of the human-prosthesis interface to happen concurrently to human motor adaptation without the need to re-tune the personalization algorithm for each individual. Human-in-the-loop experimental results with able-bodied subjects, performed in a virtual reality environment to emulate amputation and prosthesis use, show that the proposed personalization algorithm was effective in obtaining optimal synergies with a fast uniform convergence speed across a group of individuals.

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