Deep Learning methods have become very attractive in the wider, wearables-based human activity recognition (HAR) research community. The majority of models are based on either convolutional or explicitly temporal models, or combinations of both. In this paper 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 significant increase in performance. Finally, we visualize the learned weights to better understand what constitutes relevant temporal context.