Nowadays, egocentric wearable devices are continuously increasing their widespread among both the academic community and the general public. For this reason, methods capable of automatically segment the video based on the recorder motion patterns are gaining attention. These devices present the unique opportunity of both high quality video recordings and multimodal sensors readings. Significant efforts have been made in either analyzing the video stream recorded by these devices or the bio-mechanical sensor information. So far, the integration between these two realities has not been fully addressed, and the real capabilities of these devices are not yet exploited.
In this paper, we present a solution to segment a video sequence into motion activities by introducing a novel data fusion technique based on the covariance of visual and bio-mechanical features.
The experimental results are promising and show that the proposed integration strategy outperforms the results achieved focusing solely on a single source.
