Biomedical Engineering Reference

In-Depth Information

assure an immediate robot stop. Finally, we employ realistic scenarios to test the

FTA sensor's performance in emergency situations.

6.1.5.1 Calibration

First, we evaluate the accuracy of the calibration from IMU to FT sensor.

Therefore, we perform the presented calibration method with two different FT

sensors and two IMUs (including circuit board with ES), resulting in a total of four

FTA sensors. For each FTA sensor, we perform three sets of calibrations with 20

calibrations in a 15-min-interval. We therefore have 60 calibrations of IMU to FT

sensor for each FTA sensor that we use for evaluation.

Quality of the fit:

As the calibration is based on fitted values (cf. Eq.
6.5
), the quality of the fit is

essential for the accuracy of the calibration. Therefore, we estimate for each

recording of each modality the absolute distance to the fitted curve.

Calibration error:

For calculating errors of the calibration, we first transfer the recorded accelerations

A
IMU
into the FT coordinate frame by applying the computed transformation matrix

FT
T
IMU
(cf. Eq.
6.1
). We fit the transferred accelerations to a cosine with the

formula from Eq.
6.5
. We compare the phase angles of the forces (estimated during

calibration) to the phase angle of the transferred accelerations (A
FT
) and compute

the error for each spatial axis by applying the inverse sine to the phase difference:

e
calib
x
¼
arcsin
ðj
b
F
x
b
A
FT
x
jÞ;

ð
6
:
13
Þ

e
calib
y
¼
arcsin
ðj
b
F
y
b
A
FT
y
jÞ;

ð
6
:
14
Þ

e
calib
z
¼
arcsin
ðj
b
F
z
b
A
FT
z
jÞ;

ð
6
:
15
Þ

Stability of calibration:

The stability of the calibration shows the dependency of the calibration to noise

and errors in the measurements. For calculating the stability of the calibration, we

proceed analogously to the error computation for the robot online calibration

difference between these two, we use

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