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Exploring and Extending the Boundaries of Physical Activity Recognition

Attila Reiss; Markus Weber; Didier Stricker
In: IEEE SMC 2011. IEEE International Conference on Systems, Man, and Cybernetics (SMC-11), Workshop on Robust Machine Learning Techniques for Human Activity Recognition, October 9-12, Anchorage, AK, USA, IEEE Xplore, 10/2011.


This paper discusses several aspects and practical issues of physical activity recognition. Many existent activity recognition applications only include the few and well known basic activities, thus limiting the applicability of these systems. One of the main goals of this paper is to point out the importance of extending activity recognition with background activities, and to demonstrate its effects. Another practical issue of activity recognition is, that the comparison of different approaches is often not possible not only because of the lack of common datasets, but also because of the usage of different evaluation methods. This work argues, that usually subject independent validation techniques should be applied for the evaluation of activity recognition systems. The statements of this work are demonstrated on a dataset with 8 subjects and 13 different activities. Finally, an empirical study carried out within this work shows the feasibility of using more complex classifiers (required due to the extended activity recognition problem) for mobile applications.