More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
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DOI：
10.1109/embc48229.2022.9871472
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发表时间：
2022-02
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影响因子：
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通讯作者：
Huiyuan Yang;Han Yu;K. Sridhar;T. Vaessen;I. Myin‐Germeys;Akane Sano
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作者：
Huiyuan Yang;Han Yu;K. Sridhar;T. Vaessen;I. Myin‐Germeys;Akane Sano
从可穿戴数据中准确识别与健康相关的状况对于改善医疗保健结果至关重要。为了提高识别精度，各种方法都集中在如何有效地融合来自多个传感器的信息。融合多个传感器在许多应用中是一种常见的选择，但在现实世界中并不总是可行的。例如，虽然组合来自多个传感器的生物信号（即，胸垫传感器和腕部可佩戴传感器）已经被证明对于改善性能是有效的，但是佩戴多个设备在自由生活的环境中可能是不切实际的。为了解决这些挑战，我们提出了一个有效的多到少（M2L）学习框架，通过在训练过程中利用多个模态的互补信息，减少传感器，提高测试性能。更具体地说，不同的传感器可能携带不同但互补的信息，我们的模型旨在加强不同模态之间的合作，鼓励积极的知识转移，抑制消极的知识转移，以便为个别模态学习更好的表示。我们的实验结果表明，我们的框架实现了可比的性能相比，完整的方式。我们的代码和结果将在https://github.com/comp-well-org/More2Less.git上提供。
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 choice in many applications, but may not always be feasible in real-world scenarios. For example, although combining biosignals 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/comp-well-org/More2Less.git.