Coping with variability in motion based activity recognition
Matthias Kreil, Bernhard Sick, Paul Lukowicz · 2016
A key issue in the automatic recognition of human activities with body worn sensors is the variability of human motions and the huge space of possibilities for executing even fairly simple actions. In this article we introduce a new algorithm to address this issue. The core idea is that often even highly variable actions include short more or less invariant parts which are due to hard physical constraints. The aim is to develop a method that can identify such invariants and use them to improve the classification of the respective activities. The method is meant to be combined with existing classification approaches in an ensemble like fashion, being applied only to the classes for which appropriate invariants can be found and leaving the other classes to be handled by classical methods. We compare our method's results to prior publications on two well known data sets and are able to improve the classification in 5 of 23 respectively 4 of 19 classes, in same cases by a large margin (best case is from 27% to 76% in the first and from 50% to 64% in the second). In each data set there is only one class for which we make the recognition worse and in both cases it is one with poor results to start with and a relatively small decrease (from 54% to 45% in the first and from 65% to 62% in the second). The results are achieved for an user independent case.