Learning Attribute Representation for Human Activity Recognition
This work addresses the challenge of handling unbalanced and disjoint datasets in human activity recognition, though it appears incremental by adapting attribute-based methods from other domains.
The paper tackled the problem of human activity recognition from on-body sensors by introducing learned attribute representations to address the lack of human-labeled attributes, resulting in outperforming state-of-the-art methods on two datasets.
Attribute representations became relevant in image recognition and word spotting, providing support under the presence of unbalance and disjoint datasets. However, for human activity recognition using sequential data from on-body sensors, human-labeled attributes are lacking. This paper introduces a search for attributes that represent favorably signal segments for recognizing human activities. It presents three deep architectures, including temporal-convolutions and an IMU centered design, for predicting attributes. An empiric evaluation of random and learned attribute representations, and as well as the networks is carried out on two datasets, outperforming the state-of-the art.