  Most approaches that model time-series data in human activity recognition
based on body-worn sensing (HAR) use a fixed size temporal context to represent
different activities. This might, however, not be apt for sets of activities
with individ- ually varying durations. We introduce attention models into HAR
research as a data driven approach for exploring relevant temporal context.
Attention models learn a set of weights over input data, which we leverage to
weight the temporal context being considered to model each sensor reading. We
construct attention models for HAR by adding attention layers to a state-
of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on
benchmark datasets achieving sig- nificant increase in performance. Finally, we
visualize the learned weights to better understand what constitutes relevant
temporal context.
