More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
This work addresses the practical challenge of reducing sensor burden in real-world health monitoring applications, representing an incremental improvement in multimodal learning for wearables.
The paper tackles the problem of recognizing health conditions from wearable sensors when using fewer sensors at test time, by proposing a learning framework that leverages complementary information from multiple modalities during training to achieve comparable performance to using all sensors.
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common scenario in many applications, but may not always be feasible in real-world scenarios. For example, although combining bio-signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework to improve testing performance with reduced sensors through leveraging the complementary information of multiple modalities during training. More specifically, different sensors may carry different but complementary information, and our model is designed to enforce collaborations among different modalities, where positive knowledge transfer is encouraged and negative knowledge transfer is suppressed, so that better representation is learned for individual modalities. Our experimental results show that our framework achieves comparable performance when compared with the full modalities. Our code and results will be available at https://github.com/compwell-org/More2Less.git.