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sample t tests and the 95% confidence interval of the difference. Observing the results
for the positions Down and Northwest, it is valid to claim there are statistical evi-
dences to affirm that the mean of time with joystick and head movements is different.
This reveals the different performance by using in the same experience the joystick
and the head movements.
Table 3.
Confidence intervals of the difference and p values
95% Confidence Interval of
the difference
Move the red dot to:
Lower
Upper
P value
Right
-2.29
0.67
0.273
Up
-1.38
0.08
0.080
0.005
*
Down
-9.67
-1.87
Northeast
-2.89
0.66
0.211
0.028
*
Northwest
-2.74
-0.17
Southeast
-6.26
1.00
0.150
Northeast - Northwest -
Southeast
-5.32
0.37
0.085
Clustering analysis is a technique that can be used to obtain the information about
similar groups. In the future, this can be used to extract characteristics for classifica-
tion and users' profiling.
The results obtained by hierarchical clustering, using the nearest neighbour method
and squared Euclidean distance, show the similar performance of subjects except one
individual. In this case, using the R-square criteria, the number of necessary clusters
to achieve 80% of the total variability retain by the clusters is 12. Since the sample of
volunteers was from the same population, this kind of conclusions are very natural. So
the next step will consist in obtain information about handicapped people. In fact, if
the clusters of subjects could be defined then it should be interesting to work with
supervised classification in which the best command mode would be the class.
6
Conclusions and Future Work
Although many Intelligent Wheelchair prototypes are being developed in several re-
search projects around the world, the adaptation of user interfaces to each specific patient
is an often neglected research topic. Typically, the interfaces are very rigid and adapted to
a single user or user group. The Intellwheels project is aiming at developing a new con-
cept of Intelligent Wheelchair controlled using high-level commands processed by a
multimodal interface. However, in order to fully control the wheelchair, users must have
a wheelchair interface adapted to their characteristics. In order to collect the characteris-
tics of individuals it is important to have variables that can produce a user profile. The
first stage must be a statistical analysis to extract knowledge of user and the surrounding.
