1 research outputs found
Personalized On-line Adaptation of Kinematic Synergies for Human-Prosthesis Interfaces
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