A Method for Predicting Personalized Pelvic Motion based on Body Meta-Features for Gait Rehabilitation Robot
- A Method for Predicting Personalized Pelvic Motion based on Body Meta-Features for Gait Rehabilitation Robot
- 신성열; 홍지수; 전창묵; 김승종; 김창환
- Issue Date
- IROS (IEEE/RSJ International Conference on
Intelligent Robots and Systems)
- Training for balancing, which is governed by the
motion of pelvis and thorax, is a key for gait rehabilitation.
COWALK, which is a gait rehabilitation robot under development
in our institute, is capable of pelvic motion training. In this
paper, we describe a statistical method to generate pelvic motion
which is considered to fit each person, i.e., personalized pelvic
motion.We measured 14 anthropometric features of human and
captured gait motion using an optical motion capture system
from 113 healthy subjects. We setup a database of gait motion
and body measurements; we define a 4 dimensional compact
vector representation of pelvic motion, and body meta-feature,
which is a weighted linear combination of the anthropometric
measurements, to maximize statistical correlation between the
former and the latter. To synthesize a personalized pelvic motion
for a new subject, we search for k nearest neighbors in the
space of body meta-feature (k-NN algorithm), and average the
pelvic motions of them. We validate the algorithm using the
database of 113 subjects by excluding each person, synthesizing
a personalized pelvic motion for the subject, and comparing it
with actual motion of the subject.
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- KIST Publication > Conference Paper
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