  Multimodal features play a key role in wearable sensor-based human activity
recognition (HAR). Selecting the most salient features adaptively is a
promising way to maximize the effectiveness of multimodal sensor data. In this
regard, we propose a "collect fully and select wisely" principle as well as an
interpretable parallel recurrent model with convolutional attentions to improve
the recognition performance. We first collect modality features and the
relations between each pair of features to generate activity frames, and then
introduce an attention mechanism to select the most prominent regions from
activity frames precisely. The selected frames not only maximize the
utilization of valid features but also reduce the number of features to be
computed effectively. We further analyze the accuracy and interpretability of
the proposed model based on extensive experiments. The results show that our
model achieves competitive performance on two benchmarked datasets and works
well in real life scenarios.
