1. DETECTION OF
PATIENT’S SIGN OF
FALLS
Maki Takeda, Nagisa Sasaki, Kayo Yoshimoto
Takeshi Ando, Sachiko Shimizu, Kenji Yamada
and Yuko Ohno
Department of Health Sciences,
Graduate School of Medicine, Osaka University
CAUTION

2. BACKGROUND
Nurse No
problem.
Nurse station
Inpatient
It is difficult to predict falls.

3. PROPOSED SYSTEM
Motion detection Motion prediction
Video sensor
Catch
Roll over
the sign
or
Patient’s room Wake up
Bayesian approach

4. METHOD & EXPERIMENT
 Center of gravity of head  Bayesian approach
from original image
 a x, y   Lx, y a  a 
������ ������ : prior distribution
������ ������ ������, ������ : posterior distribution
������ ������, ������ ������ ：likelihood
 Culculating velocity of ������： x-coordinate of center of
center of gravity of head gravity of head
������： y-coordinate of center of
Velocity [pixel/frame]
10 25frame
?
gravity of head
5 ������： regression coefficient
0
Estimate gradient by
0 20 40 60 80
Frame maximum likelihood estimate
Motion of posterior distribution
detection

5. EXPERIMENTAL RESULTS
 Subjects performed 3 situations on bed
( wake-up, right or left roll-over)
150
X-axial displacement [pixel]
120
起き上がり
Wake-up
90
右
Right roll-over
60
左
Left roll-over
30
0
0 0.1 0.2 0.3 0.4
Estimated gradient
It is possible to classify wake-up or roll-over
by estimated gradient and x-axial displacement
of center of gravity.