Biomedical Engineering Reference
In-Depth Information
Table 3.5
Prediction overshoot comparison listed in Table 3.6
Datasets
Average number of overshoot
dataset (IMME/MC-IMME)
(unit: overshoot dataset #)
Improvement (%)
Chest_1
15.00/13.37
10.83
Chest_2
15.25/13.12
13.93
Chest_3
26.00/12.75
50.96
Head_1
15.00/13.75
8.33
Head_2
14.75/13.62
7.62
Head_3
62.12/42.50
31.58
Upper Body_1
15.00/13.37
13.37
Upper Body_2
27.75/23.37
15.76
Upper Body_3
99.62/78.25
21.45
Table 3.6
CPU Time used among the datasets
Datasets
KF
IMME
MC-IMME
Chest
0.244
0.957
0.802
Head
0.246
0.966
0.804
Upper Body
0.249
0.974
0.829
(Unit: ms/sample numbers)
motion data using a Polhemus Liberty AC magnetic tracker with eight sensors, and
then conducted the experimental test for the computational complexity with off-
line. We have implemented the proposed method with Matlab language using a PC
of Pentium core 2.4 GHz with RAM 3.25 GB.
In Table 3.6 , we evaluated the individual dataset to compare KF and IMME
with MC-IMME. Table 3.6 shows the overall performance of CPU time used
among the datasets. Here, we used the period of the first 20 s for all nine datasets
to calculate CPU time used for KF, IMME, and MC-IMME. For the comparison of
the different target-tracking methods, we evaluated the computational time
calculating target-tracking estimate filters. That means we only counted the
calculation time for KF and IMME operations with all the methods. Note that
MC-IMME can improve approximately 16 % of the average computational time
with comparison to IMME, even though it requires more than twice the compu-
tational time of KF, as shown in Table 3.6 . An interesting result is that the
proposed method can improve the computational time over IMME. We think that
the actual difference for CPU time used in Table 3.6 mainly comes from the
simultaneous calculation of distributed sensory data in MC-IMME. In IMME, it
needs to calculate target-tracking estimation individually, whereas MC-IMME can
evaluate a couple sets of target estimation simultaneously.
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