Multi-modality Sensor Data Classification with Selective Attention
This work addresses the challenge of adaptability in classification methods for wearable sensor data, which is incremental as it builds on existing reinforcement learning techniques.
The paper tackles the problem of multimodal wearable sensor data classification by proposing a deep reinforcement learning approach with a selective attention mechanism, achieving competitive performance on three datasets compared to state-of-the-art baselines.
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific approaches and is thus ineffective in complex sit- uations where multi-modality sensor data are col- lected. Moreover, the wearable sensor data are less informative than the conventional data such as texts or images. In this paper, to improve the adapt- ability of such classification methods across differ- ent application domains, we turn this classification task into a game and apply a deep reinforcement learning scheme to deal with complex situations dynamically. Additionally, we introduce a selective attention mechanism into the reinforcement learn- ing scheme to focus on the crucial dimensions of the data. This mechanism helps to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier. We carry out several experiments on three wearable sensor datasets and demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines.