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behavior. However, NB has a higher average rate with
84.5% compared to HO with 78.6% as shown in ta-
ble 5. Apparently, NB is more suited to deal with
small amounts of training data for certain user behav-
iors in our scenario: Due to the conditional indepen-
dence assumption in the NB approach, the probabil-
ities for each observation variable given the user be-
havior can be calculated independently of the other
observation variables. This leads to a more accurate
prediction of the underlying probabilities and a higher
classification rate compared to the HO method where
the observation variables are subsumed in a single ob-
servation. As shown in table 5, the NB produces ex-
cellent classification results for single user behaviors
showing a huge difference in length according to ta-
ble 4. The classification rates range from 81.7% for
brush teeth to 95.7% for rinse mouth. Only nothing
has a decreased rate of 50.4%.
Our results show that our hierarchical classifica-
tion framework based on the results of IU analysis is
well suited to approach the recognition problem in our
scenario. Our framework can deal well with the spe-
cific requirements of small amounts of training data
for certain behaviors and arbitrary behavior lengths.
7 CONCLUSIONS
In this paper, we focus on the challenging problem
of user behavior recognition in a real-world scenario.
We use a structured approach to develop a recognition
framework for an automatic prompting system assist-
ing persons with cognitive disabilities in the everyday
task of brushing teeth. We analyze the task using IU
analysis. We identify user behaviors which are im-
portant to complete the task successfully as well as
environmental configurations of objects involved in
the task. User behaviors are classified based on envi-
ronmental configurations using a Bayesian Network
(BN) in a Bayesian Filtering approach.
We present recognition results on 18 trials per-
formed by regular users. In future work, we aim to
test our recognition framework in a study with per-
sons with cognitive disabilities. An average recog-
nition rate of 84.5% using a BN with a NaiveBayes
structure shows that our framework is suitable to user
behavior recognition for an automatic prompting sys-
tem in a complex real-world scenario: The Bayesian
Filtering approach can deal with the specific require-
ments like small amount of training data for user be-
haviors and arbitrary behavior lengths. Furthermore,
the framework is applicable to other tasks and easily
extendable with different classifiers due to the hierar-
chical structure.
ACKNOWLEDGEMENTS
The authors would like to thank the inhabitants and
caregivers of Haus Bersaba for their high motiva-
tion to cooperate in the project ’Task Assistance for
Persons with Cognitive Disabilities’ (TAPeD) of the
Cognitive Interaction Technology Center of Excel-
lence (CITEC), Bielefeld University.
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