Evaluating the effects of signal segmentation on activity recognition

On-body activity recognition systems are becoming more and more frequent in people's lives. These systems normally register body motion signals through small sensors that are placed on the user. To perform the activity detection the signals must be adequately partitioned, however no clear consensus exists on how this should be done. More specifically, considered the sliding window technique the most widely used approach for segmentation, it is unclear which window size must be applied. This paper investigates the effects of the windowing procedure on the activity recognition process. To that end, diverse recognition systems are tested for several window sizes also including the figures used in previous works. From the study it may be concluded that reduced window sizes lead to a better recognition of the activities, which goes against the generalized idea of using long data windows.