  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.
