  Unlike images or videos data which can be easily labeled by human being,
sensor data annotation is a time-consuming process. However, traditional
methods of human activity recognition require a large amount of such strictly
labeled data for training classifiers. In this paper, we present an
attention-based convolutional neural network for human recognition from weakly
labeled data. The proposed attention model can focus on labeled activity among
a long sequence of sensor data, and while filter out a large amount of
background noise signals. In experiment on the weakly labeled dataset, we show
that our attention model outperforms classical deep learning methods in
accuracy. Besides, we determine the specific locations of the labeled activity
in a long sequence of weakly labeled data by converting the compatibility score
which is generated from attention model to compatibility density. Our method
greatly facilitates the process of sensor data annotation, and makes data
collection more easy.
