Biomedical Engineering Reference
In-Depth Information
For more detailed correlations between walking speed and joint angles the reader
is encouraged to refer to [
39
].
The typical shape of joint trajectories can be mostly found in the sagittal plane
and seems more difficult to find in the other anatomical plans. This means that joint
angles seem to follow a common shape for any healthy user, but only for flexion and
extension.
Let us consider now coordination between the joint angles involved in human
locomotion. Angle diagrams have been introduced in medicine in order to highlight
changes in joint coordination for people with gait disorders [
43
]. It consists of dis-
playing the evolution of one joint angle according to another one leading to a kind
of 2D signature. One step further in explaining joint coordination has been proposed
by [
11
]. The key idea is to find correlations between joint trajectories using data
reduction with Principal Component Analysis. If we consider the 3D space based on
the three joint angles of the lower-limb (hip, knee and ankle joints), many researchers
have highlighted a covariance plan that tends to demonstrate coordination between
the joints. This covariance plan has been shown in many types of locomotion and
its orientation in space seems to be a good predictor of the energy expenditure and
style [
10
]. This coordination could be interesting to analyze in virtual environments
to check if joint coordination is affected by immersion. It would thus participate in
defining a metric to measure the quality of the walking cycle in immersive environ-
ments compared to real ground walking.
In nonlinear walking, all of these variables are affected [
48
]. Self-selected walking
speed while turning was reduced compared to walking straight ahead. It decreased
from 1
s
−
1
s
−
1
and
.
±
.
·
.
±
.
·
00
0
02 m
for straight line walking down to 0
91
0
01m
s
−
1
for spin and step turns respectively. Table
3.5
reports other relations
between turning strategies and gait parameters.
Hence, nonlinear walking not only changes the orientation of the body, but also
affects global gait variables and joint trajectories. It may be interesting to evaluate
to what extent taking this knowledge into account in camera motion control would
improve immersion in navigation tasks. To our knowledge this type of evaluation
has not been carried-out yet.
0
.
87
±
0
.
01m
·
3.3 Dynamics of Human Walking
Joint angles and overall kinematic parameters are strongly linked to dynamic con-
straints such as external forces and muscle activation patterns. Hence, one has to
Table 3.5
Reference gait parameters values for straight line walking compared to spin turns and
step turns
Variable
Straight
Spin
Step
Stride length (m)
1
.
30
±
0
.
02
1
.
14
±
0
.
03
1
.
36
±
0
.
03
Peak ankle dorsiflexion (
◦
)
14
.
4
±
1
.
3
15
.
6
±
1
.
4
10
.
6
±
1
.
7
Peak knee extension (
◦
)
−
1
.
65
±
1
.
11
1
.
21
±
1
.
25
−
3
.
49
±
1
.
48
Adapted from [
48
]
