Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
This work addresses activity recognition for users of wearable devices, but it is incremental as it builds on existing attention mechanisms and multimodal feature methods.
The paper tackled the problem of selecting salient multimodal features for wearable sensor-based human activity recognition by proposing a dual-stream recurrent convolutional attention model, achieving competitive performance on benchmark datasets.
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 (Fullie and Wiselie)" principle as well as a dual-stream recurrent convolutional attention model, Recurrent Attention and Activity Frame (RAAF), 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 hyper-parameters, accuracy, interpretability, and annotation dependency of the proposed model based on extensive experiments. The results show that RAAF achieves competitive performance on two benchmarked datasets and works well in real life scenarios.