ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

from subjects S1, S2, S3, S5, and S6. The test set was composed of data from all
bottles and tasks but only from subject S4, not used for training.
Test S2: Similar to Test S4 but saving subject S2 for the test stage.
4. RESULTS AND DISCUSSION
Table 4 shows the results of the statistical analysis performed over the joint angles’
SDs. Obviously, the errors made by the ANNs prediction cannot be better than the
experimental values. Table 5 indicates the root mean square errors (RMS) for the tests
performed over the two implemented networks. It can be seen that the predictions made
with the 2-layer network, when trained with data from all the subjects (Test B2), are
fairly good taking into account that the collected data are scarce. In this case, the errors
are below 10º for all the joint angles except for the DIP1, due to the bigger deviation in
its posture repeatability, as observed in Table 4. In fact, a Pearson’s correlation
coefficient of 0.82 has been found between the mean SD values and the RMS errors
obtained from the 2-layer feed-forward neural network for the joint angles. These
results are slightly better when a new hidden layer is included in the network, but this
enhance does not justify the use of 3 layers instead of two for our estimation purposes.
Nevertheless, when requiring the estimation of joint angles for a subject who has not
been included in the training phase, the results are obviously worse. In this case, none of
the multi-layer neural networks is able to provide appropriate estimations for joint
angles ROLL, DIP1 and MCP2. The addition of a fourth layer might enhance the results
of Test S4. Note that the hand size for S2 is out of the range of the other subjects’ hand
sizes and this could be the reason for the poor prediction results of Test S2.
Future work includes new experiments with different subjects in order to collect enough
information for allowing the network a proper generalization. Thus, the joint angles of a
subject not included in the training phase could be estimated properly. Also an ANN has
to be design to predict the joint angles of the whole hand. Due to the big amount of data
required and the high computational costs implied, it is likely that different ANNs have
to be used for the different fingers of the hand, similarly to what is done in [4].
Table 4. Statistical description of the data deviations for the experiments (degrees).
Joint angle Mean (º) SD (º) Maximum (º)
ROLL 1.3 2.0 8.7
MCP1 2.3 1.5 5.6
DIP1 4.0 2.1 8.9
ABD1 2.8 3.1 14.6
MCP2 3.0 1.3 5.9
PIP2 3.2 2.1 10.3
Table 5. 2-layer and 3-layer feed-forward network results (root mean square error).
2-layer feed-forward network 3-layer feed-forward network
RMS (º) RMS (º) RMS (º) RMS (º) RMS (º) RMS (º)
Test B2 Test S4 Test S2 Test B2 Test S4 Test S2
ROLL 3.15 15.38 28.66 3.59 7.36 37.94
MCP1 9.01 5.36 9.24 8.50 7.26 18.21
DIP1 10.44 18.58 65.40 10.11 17.90 64.41
ABD1 6.15 3.67 16.22 5.95 3.79 19.35
MCP2 7.47 28.45 76.66 7.27 25.59 96.34
PIP2 7.82 11.04 53.45 8.46 12.46 46.44